Author name: Prachi

AI

An Artificial Intelligence Layer is Only as Good as the Data Underneath It

An Artificial Intelligence Layer is Only as Good as the Data Underneath It CRM 12 min Updated: August 7, 2026 The sales tech market has consolidated hard over the past two years, and the AI layer sitting on top of it has moved from a differentiator to table stakes. What customers actually want hasn’t changed underneath that shift: technology that helps close more revenue and closes the gaps that exist across the customer lifecycle, from lead to opportunity to renewal. Tools that give unified visibility across the full funnel, built on clean, complete data and aligned teams, are the ones that win. An intelligence layer is what gets a company closer to its revenue targets, but only if the data underneath it can actually support it. Get our latest insights into your inbox What Is an Intelligence Layer? An intelligence layer is what unlocks the patterns that have long been trapped inside disconnected databases and applications. It makes use of data streaming continuously into your systems and surfaces insight at the moment it’s actually needed, adapting and evolving rather than staying static. Done well, it marries historical context with the constant flow of new data about accounts, opportunities, and prospects, offering predictive signals about what will actually move the buying journey forward at any given point. Those signals make real-time, proactive engagement possible instead of a reactive scramble after the fact. This is the system of action that determines who wins in sales tech: which solution delivers the best insight in a single interface that serves as a rep’s actual point of decision and point of action. Whether it’s deciding who to reach out to, which deal to prioritize this week, or which stalled deal to revive, an intelligence layer surfaces a predictive list of next actions that pushes a team toward close, keeping customer-facing teams focused on the highest-value, most-likely-to-convert accounts. The Real Story: It’s Not Just About the Algorithms The intelligence layer will keep winning in this category. But its usefulness depends entirely on the data underneath it, because AI needs meaningful, accurate input to recommend anything that actually improves revenue outcomes. Building a real intelligence layer requires a solid data layer underneath it first. Without that, even the best model in the world can’t undo what bad data does to revenue. Most businesses are still struggling with exactly this. Modern Sales Pros’ State of Your CRM Data report, produced with BuzzBoard, found only 6% of respondents highly confident in their own CRM data, 58% citing data accuracy as the top barrier to collecting quality data, and 37% saying poor-quality data directly leads to poor conversion rates. The questions revenue leaders actually need to ask: What will my data source be when I deploy an AI agent against it? Do I trust the quality of that data? Is it clean and accurate enough to drive reliable insight for the business? For a company unsure of the answer, adding even the most advanced AI solution to the stack doesn’t improve revenue outcomes. It adds tech debt on top of an already shaky foundation, and prevents the intelligence layer from delivering anything close to what it promises. Read Case Study See How Mimecast generated $150M+ in pipeline and attributed $10M in revenue by fixing their GTM AI foundation Bad Quality Data Leads to Poor Conversions Incomplete and inaccurate contact data has a direct, measurable impact on conversion rates, and therefore on revenue. Without the right data and insight, demand generation and sales teams can’t reliably get the right leads into the funnel, nurture them down it, identify the most viable prospects to engage, or run genuinely personalized outreach. This is exactly where the intelligence layer has to converge with real data to close an organization’s actual revenue gaps. Accurate, rich, complete account data is the foundation an AI-driven sales organization is built on, and companies that skip investing in that foundation won’t hold up against the pace of change in today’s sales environment. Characteristics of Good Quality Data Data quality rests on a specific set of characteristics. Good data is: Accurate, reflecting what’s actually true, not what was assumed or guessed. Automated, captured without depending on a rep remembering to log it. Complete, covering the full picture of an account, not just the fragments a rep happened to enter. Timely, current enough to reflect what’s actually happening right now, not what was true weeks ago. Together, these characteristics are the basis for good decision-making. A quality dataset is what supports AI that actually works, since any model is only as good as what you put into it. Checklist to Assess Your Data Quality If you think your data quality is already good, it’s worth checking again with this specific set of questions, organized by funnel stage. If the answer is no to any of these, it’s worth checking the actual state of your data hygiene directly rather than assuming it’s fine. With the right data strategy, these gaps are fixable. Download Checklist Dive deeper with our AI readiness checklist to see where you stand when it comes to your data foundation. Why This Matters More Now Than It Did a Few Years Ago The core argument here hasn’t changed. What’s changed is what’s riding on it. Salesforce’s April 2026 Headless 360 initiative made every core Salesforce capability available as an API or MCP tool specifically so AI agents can read, write, and execute workflows without a person opening a browser first. That’s a real shift in what bad data actually costs. A gap in the checklist above used to just produce a slightly-off report a manager could catch. Fed into an agent acting on that same gap directly, updating a field, prioritizing an account, flagging risk, the same gap becomes a wrong automated decision, at a speed nobody catches in time. Data quality isn’t just about having a lot of data to feed the system. It’s about trustworthy, complete data underneath it, because that’s the only thing that actually

Buying Group, RevOps

How Nektar Automates Buying Committee Engagement

How Nektar Automates Buying Committee Engagement RevOps 11 min Updated: August 4, 2026 Salesforce’s data model has three objects relevant to a buying committee: Account, Contact, and Opportunity Contact Role. A Contact is a person. An OCR is a static label, “Decision Maker,” “Influencer”, attached to an Opportunity. That’s the entire model. It was built to store contacts. It was never built to map a buying group, and that distinction is architectural, not semantic. The average enterprise deal involves 6 to 10 decision participants. The average CRM opportunity has one or two OCRs. A rep who wants to log a third has to remember to create it, assign a role, and keep it current by hand. In practice, almost nobody does this consistently, and the gap between what’s actually happening in a deal and what’s recorded in Salesforce quietly compounds. Get our latest insights into your inbox Why This Gap Exists, and Why It’s Getting Worse An OCR captures a label at a single point in time. It doesn’t track whether that person is engaging, disengaging, newly arrived, or gone. A contact tagged “Champion” who stopped responding three weeks ago looks identical in Salesforce to one who replied yesterday. The label exists. The signal behind it doesn’t. Every system that reads CRM data inherits this blind spot: forecasting models, ABM platforms, customer success tools, attribution engines, and increasingly, AI agents. When a human reads an incomplete CRM record, they can sense-check it, ask a follow-up question, or lean on institutional memory. When an AI agent reads the same record, it takes it at face value. If Salesforce shows one contact on a deal, the agent plans around one contact. Salesforce’s own April 2026 Headless 360 initiative made every core Salesforce capability available as an API, specifically so agents can read, write, and execute workflows without a human opening a browser first. That’s a real shift in what an incomplete buying group actually costs. It used to produce a slightly-off report a manager could catch. Now it can produce a wrong decision made by software, at a speed nobody catches in time. What Breaks Across the Business When Buying Groups Go Unmapped The gap above doesn’t create one problem. It creates a different failure in every function that touches CRM data. In sales, a rep who inherits an account from a departing colleague gets three logged contacts on a key opportunity when the real buying group was nine. Months of relationship context, who the real blocker was, which VP had a back-channel, evaporate the moment the previous rep leaves. A closed-lost re-engagement trigger fires on the one contact still in the system, while the five or six other people who actually shaped that decision were never captured at all. In sales leadership and forecasting, a CRO’s mandate to “multithread every deal above $200K” has no way to be measured, since there’s no data showing whether a labeled “Executive Sponsor” has attended a meeting in the last month or gone quiet. Forecast models score deals on the activity they can see, which typically represents a fraction of what’s actually happening in the account. In marketing, a VIP re-engagement campaign gets built from CRM contacts that cover maybe one or two people per account, while the procurement lead and technical evaluator who actually shaped the original decision never make it onto the list. A prospect’s CFO can attend a webinar six weeks before close and never show up in an attribution report, because nobody added them as an OCR. In customer success, a champion leaves for a new role three months before renewal, and the team discovers there’s no fallback relationship, because nobody ever recorded who else in the account cared about the outcome. QBR invites go to the two or three operational contacts CS already knows, while the executives who cared about strategic outcomes were never mapped in the first place. In RevOps, a meaningful share of the team’s time goes to chasing and cleaning contact data that was incomplete by design from the start, since adding more contacts manually doesn’t solve a problem that’s actually about missing engagement context, not missing names. How Nektar Automates Buying Group Engagement Nektar’s approach starts from a different premise than “get reps to log more contacts.” The data already exists, it’s in the emails, the calendar invites, and the meeting attendee lists generated by normal sales activity every day. What’s missing is the layer that captures it, structures it, attributes it to the right opportunity, and writes it into Salesforce automatically. Four capabilities do this end to end: 1. Automated Opportunity Contact Role Creation Nektar automatically identifies stakeholders from real email and meeting activity and creates the corresponding Opportunity Contact Role, with no rep input required. Every relevant person gets documented and correctly associated with the opportunity as the relationship develops, not just the one or two contacts a rep remembered to add manually. Why it matters: Eliminates the manual entry that made OCR data unreliable in the first place, keeps contact roles accurate and consistent rather than dependent on rep memory, and gives sales and RevOps leadership a real, current view of who’s actually involved in a deal. 2. Conditional OCR Contact roles get created based on specific, predefined conditions your team actually cares about, not a one-size-fits-all rule. Different sales teams or business units can tailor exactly when and how a contact role gets assigned. Why it matters: Adapts to your specific sales process rather than forcing a generic structure onto it, keeps contact-role creation focused on genuinely relevant people rather than every name that appears in an inbox, and scales to complex sales environments with large, varied buying committees. 3. Intelligent Meeting Tagging Meetings get automatically tagged with relevant context, using AI to identify key details and associate them with the right contacts and opportunities, closing the gap between what happened in a meeting and what’s actually recorded about it. Why it matters: Surfaces real insight into what a meeting actually

Sales

15 Sales Optimization Tools to Fast Track Your Deals in 2025

15 Sales Optimization Tools to Fast Track Your Deals in 2026 Sales, Sales Tech Stack 11 min Updated: August 4, 2026 Imagine a machine with every part in working condition, run daily, that still produces suboptimal output. The issue is oiling. Without proper, regular oiling, the various parts can’t function together well. Sales optimization tools are that oiling, keeping the sales function running at its actual potential rather than just technically operational. Get our latest insights into your inbox What Is Sales Process Optimization? Sales process optimization means refining and improving the steps involved in selling a product or service, lead generation, qualification, needs assessment, proposal creation, negotiation, and closing. The goal is greater efficiency and effectiveness, which translates directly into more successful sales and more revenue. How Sales Optimization Tools Work in B2B Sales B2B sales cycles run longer and more complex than B2C, typically involving multiple decision-makers and influencers, and requiring a rep to demonstrate specific business value rather than a generic pitch. Sales process optimization in B2B specifically involves: Identifying and targeting the right prospects. Using data analytics to find businesses that are genuinely a good fit, and building targeted outreach around that fit rather than a broad net. Understanding the buying process. Identifying the actual decision-makers in a deal, understanding their individual needs and priorities, and tailoring messaging to each of them specifically. Creating a value proposition. Articulating specifically how a product or service solves the prospect’s actual pain points, not a generic feature list. Managing the sales process. Using CRM software to track leads and opportunities, setting regular touchpoints, and managing negotiation and closing deliberately rather than reactively. Analyzing and optimizing continuously. Reviewing sales data and feedback to identify what’s actually working, and adjusting the process accordingly rather than running the same playbook indefinitely. A Closer Look at the Sales Optimization Process Define your goals. Set specific targets, revenue, deal count, or whatever metric matters most to your business right now. Analyze your current process. Review sales data, talk to the team, and gather customer feedback to find where the real friction is. Develop a plan. Based on that analysis, decide what actually needs to change, strategy, tooling, or training. Implement the plan. Roll out new processes or tools, train the team, and adjust strategy as needed. Monitor your results. Track progress against the original goals, and keep gathering data and feedback as you go. Continuously improve. Sales optimization isn’t a one-time project. Keep refining based on what the data and your team are actually telling you, and be ready to make bigger changes if goals or market conditions shift meaningfully. Use Cases of Sales Optimization Tools Sales optimization spans automating lead generation, personalizing the sales experience, improving buying-group intelligence, and building out a real sales enablement strategy. A few specific examples: Automate lead generation. Identify potential customers against specific criteria automatically, rather than relying solely on manual prospecting. Personalize the sales experience. Understand each stakeholder in a buying committee individually, and tailor messaging to resonate with each of them, which also supports better multithreading by design. Open more doors with more active contacts. Equip reps with additional contacts automatically discovered across the sales tools they already use, rather than a list built by hand. Improve buying-group intelligence. Give reps immediate access to a buying committee map, so they know exactly whom to engage, how, and when to move a deal forward. Real-time activity intelligence. Automatically capture structured and unstructured data and update it against active opportunities in real time, with no manual work from reps. Implement a sales enablement strategy. Equip reps with the tools, training, and resources they actually need, a sales playbook, CRM access, and regular training on technique and best practice. Optimize the sales funnel. Identify exactly where prospects drop off and simplify the process at that specific point, rather than guessing at what’s broken. Use customer data to drive sales. Build targeted campaigns and personalized outreach around real buying patterns, rather than a one-size-fits-all message. 15 Best Sales Optimization Tools for 2026 1. Freshsales Suite Freshsales Suite (Freshworks’ rebrand of its former standalone Freshsales product) is a comprehensive automation solution giving sales teams built-in email, phone, chat, and telephony, along with AI-powered insight to attract leads and drive deals. Native CPQ makes generating and sharing quotes straightforward, and the platform pulls sales, marketing, and support data into one system so a rep isn’t reconstructing customer context across separate tools. Key features: built-in phone, email, and chat within the CRM itself; AI-powered deal insights and lead scoring; native CPQ for quote generation; visual pipeline and deal management; workflow automation for repetitive follow-up tasks. Best for: Teams wanting communication channels, CPQ, and CRM unified in one platform rather than stitched together from separate vendors. 2. Aviso AI Aviso brings AI-driven forecasting, pipeline inspection, and deal risk analysis into one workspace, paired with MIKI, a conversational AI orchestrator that can query pipeline data and trigger CRM updates directly rather than just displaying a dashboard. Key features: MIKI conversational AI orchestrator for natural-language pipeline queries, predictive forecasting trained on historical deal and engagement data, real-time deal risk and coaching alerts, no-code agent studio for building custom guided workflows. Best for: Teams wanting AI agents built directly into forecasting and pipeline execution, not just a static report. 3. Kluster Kluster standardizes the forecasting and pipeline-review process, giving teams a consistent cadence and reporting structure rather than rebuilding the process every cycle, with AI-driven pipeline-change analysis flagging where a forecast is quietly drifting from reality. Key features: live forecasting with historical accuracy tracking, pipeline-change analysis to catch drift early, security alerts for anomalous pipeline activity, revenue analytics across the full funnel. Best for: RevOps teams wanting forecast consistency and drift detection without building the process manually in spreadsheets. 4. ClickPoint Software ClickPoint is a cloud-based lead management solution that helps reps reach more prospects and close deals more efficiently, with a track record specifically in improving return on lead follow-up. It’s built around the idea

Sales

Top 10 Sales Methodologies to Use in 2026

Top 10 Sales Methodologies to Use in 2025 Sales 10 min Updated: August 3, 2026 Every business out there is trying to sell something, whether it’s tangible products, services, knowledge, or software. But the real question is: are they keeping up with the changing trends in customer buying patterns? Customers have gotten wiser, and their preferences and behavior have evolved with time. 67% of customers now prefer self-service over speaking to a company representative, and businesses have to tailor their sales approach to that shift. It’s not enough to just sell something anymore; it has to resonate with the customer and meet expectations they’ve already set for themselves elsewhere. Businesses that try to do this without a structured, strategic approach tend to get poor sales results and low methodology adoption. The right sales methodology keeps a team on track and focused on the actual customer journey, but there are enough methodologies out there to genuinely confuse the decision. This guide covers the basics, and the ten worth considering for selling to today’s customer. Get our latest insights into your inbox What Is a Sales Methodology? A sales methodology is a systematic, strategic approach to selling: a set of principles and practices that guide how a sales team interacts with customers, aimed at understanding customer needs, building trust, and closing more deals. Sales methodology has a real history. Early 20th-century sales technique focused almost entirely on personal persuasion and hard-selling. Consultative selling emerged in the 1950s, prioritizing relationship-building and genuine understanding of customer needs, and the approach became widely known through Neil Rackham’s SPIN Selling in the late 1980s. Since then, many methodologies have emerged, each with distinct features: Challenger Sale, Solution Selling, MEDDIC, SPIN Selling, and Value Selling among the most established. Sales Methodology vs. Sales Process A sales methodology is the framework guiding how a rep approaches selling, understanding needs, building trust, closing deals, and includes principles for how to approach customers, position a product, and handle objections. A sales process, by contrast, is the actual series of steps a team follows to move a prospect from first contact to close: prospecting, qualification, needs assessment, presentation, negotiation, closing, something you can map out as a flowchart. Put simply: methodology is the overarching philosophy; process is the specific sequence of steps that carries that philosophy out in practice. Advantages of Sales Methodologies A defined methodology genuinely fuels sales effort in several distinct ways: Increased efficiency. A structured approach helps reps stay organized rather than improvising every interaction from scratch. Improved customer satisfaction. Focusing on genuinely understanding a customer’s needs and pain points builds real trust. Personalized sales. Reps can tailor their approach to different customers based on actual buying motivation and preference, not a single generic script. Better collaboration. A team following the same methodology works together more effectively, since everyone’s speaking the same language about the deal. Measurable sales efforts. Each step is clearly defined, which makes progress and success genuinely trackable. Sales optimization. Businesses can analyze which parts of the methodology are working and adjust the parts that aren’t. Better sales results. Reps equipped with a proven approach that’s worked in similar situations before tend to close more reliably. A well-defined methodology functions like a map through unpredictable terrain, keeping a team oriented toward its actual goals rather than reinventing the approach deal by deal. How to Choose the Best Sales Methodology for Your Business Dr. Leff BonneyMarketing Professor As an industry, we cling to this incorrect notion that there’s a single best way to sell. We select a sales process or methodology that we believe is a “best practice,” and we tell our sellers to repeat that same sales approach with every customer in every circumstance. It’s a fundamentally flawed strategy. A few factors are worth genuinely considering before choosing, rather than defaulting to whatever methodology is currently trending. Needs of your target audience. Customer needs, preferences, and buying habits should sit at the center of the decision. Independent-research-driven customers might fit a self-service approach like the Value Selling Framework; customers who prefer a more interactive process might fit a hands-on technique like SPIN Selling better. Sales cycle and complexity. Duration and complexity vary by what’s being sold, how the decision gets made, and how many people are involved. A long, multi-touchpoint cycle might fit NEAT Selling or the Challenger Sale better than a shorter one. Product or service. A complex offering needing significant explanation might fit Conceptual Selling; a straightforward offering with a short cycle might fit SNAP Selling instead. Business goals. Market-share growth and new-customer acquisition might point toward Target Account Selling or the Challenger Sale; building loyalty with existing clients might point toward the Value Selling Framework. Sales team. A team of strong relationship-builders might fit Inbound Selling well; a team of deep subject-matter experts might fit MEDDIC better. Industry trends. A shift toward inbound marketing and sales across your industry might make Inbound Selling the right call; a wave of new competitive entrants might make the Challenger Sale’s differentiation focus more relevant. Top 10 Sales Methodologies to Use in 2026 1. SPIN Selling Developed by Neil Rackham in 1988, SPIN Selling diagnoses a customer’s problem before attempting to sell anything, using targeted questions rather than a rigid script. The acronym maps to four question types: Situation (a prospect’s processes and objectives), Problem (their current challenges), Implication (what happens if the problem stays unsolved), and Need-payoff (how a solution benefits the organization, framed for the specific stakeholders who’ll evaluate it). Best used when: sales cycles are long and involve multiple touchpoints with decision-makers. 2. N.E.A.T Selling NEAT Selling starts with identifying which leads are actually likely to convert, so reps focus effort on the right audience from the start. The four pillars: Need (the pain point requiring a solution), Economic impact (the financial benefit of solving it), Access to Authority (identifying and reaching the actual decision-makers), and Timeline (a realistic path to sale and implementation). Need and budget are consistently the two biggest

Sales

Everything You Need to Know About Sales Territory Mapping

Everything You Need to Know About Sales Territory Mapping Sales 10 min Updated: August 3, 2026 Successful sales strategies play a key role in achieving business goals. But what drives these strategies from inception to execution, improving sales operations and on-ground performance? Sales territory mapping. Without the right sales territory map template, your sales team could face poor productivity, revenue mismatch or imbalance, subpar revenue performance, poor customer experience, client loss, and wasted resources. So, how do you get sales territory mapping right to achieve business goals and grow your revenue? Let’s find out in this guide. Get our latest insights into your inbox Sales Territory Mapping: Setting On-Ground Sales in Motion Sales territory mapping defines and visualizes the area, sales amount, and revenue your sales team will target. It divides and categorizes customers based on specific characteristics within your ideal customer profile. A sales territory map template also helps you assign categories and customers to the salespersons best equipped to serve them, reaching the right customers in the right areas with the right characteristics to achieve targets and improve growth. The task of aligning your sales plan with business goals in the most profitable way lies with sales managers. From the larger business perspective, sales territory mapping is part of location intelligence, helping segment customers and find more relevant target markets for revenue success. How Is Sales Territory Mapping Instrumental to Business Growth? A sales territory map template does more than act as a blueprint. Here are six ways it contributes to growth. 1. Ties Back to Business Goals Sales territory mapping creates a blueprint for achieving your business goals. It clearly lays out sales targets and directs reps to the most profitable customers or verticals by strategically assigning territories. 2. Maintains Balance One of sales territory mapping’s primary objectives is to secure a balanced and fair distribution of work among sales teams, moving and optimizing resources effectively to maximize revenue potential. 3. Increases Selling Time Reps can increase facetime with clients by cutting down time spent on planning. Sales territory mapping tools can decrease that planning time from months to minutes. 4. Improves Win Rates When reps spend more time selling, they can nurture clients better through the funnel, and each assignment is backed by data. That data offers more visibility into customers and prevents deals from slipping through the cracks, improving customer experience and uncovering new leads to increase win rates. 5. Boosts Morale A sales territory map template encourages intelligent planning, further improving sales productivity. When reps can increase win rates, achieve quota, and earn more, morale improves and attrition drops. It also highlights areas useful for coaching. 6. Extracts Hidden Insights Sales territory mapping helps measure sales data by connecting the map to the CRM, so when multiple salespersons are involved in a deal, you can attribute the sale to the right person. Recognizing the benefits of sales territory mapping is the first step. Step two is understanding its different types. Choosing From 5 Types of Sales Territory Mapping Conventionally, sales territories were based on locations. Today, you can customize them per customer, market, or even product needs. 1. Geography Geographic sales territory mapping is the most commonly used and also the oldest. It classifies your market based on geographical locations, cities, states, countries, and zip codes. For example, Sales Team A can serve Texas, while Sales Team B covers California. To get geographic mapping right, your sales team must have a regional, cultural, and linguistic understanding of the territory they’re allocated, and needs to be available when customers are actually active in that region. 2. Product Product-based sales territory mapping works when you have multiple offerings, categorizing and assigning reps to specific products or technological offerings. It’s useful when certain reps have in-depth expertise on specific products. For example, Team A has expertise in CRM solutions, while Team B caters to clients looking for digital advertising software. Assigning reps to the right products helps them sell to clients more convincingly. 3. Customer The third way to divide sales territories is based on specific customer characteristics like demographics or roles. For instance, Sales Team A may sell to clients with yearly revenue of $500,000, while Sales Team B focuses on higher-margin clients with yearly revenue of $1 million and up. 4. Industry Industry-based sales territory mapping assigns reps to specific industries or verticals. It works best when your product caters to businesses across multiple industries. For example, Team A sells to construction, while Team B covers education, and within education, Team B might handle universities and higher education specifically while Team C is responsible for schools. 5. Sales Channel An increasingly common type of sales territory map template categorizes territories by the sales channels reps or clients use. McKinsey’s research found B2B buyers now use ten or more channels on average as they move through the buying process, combining digital self-serve channels for some activities with video or in-person channels for others. Consider this example: Team A is responsible for selling via digital channels like social media, while Team B sells through offline channels such as cold calling. You may also get more granular, assigning reps with expertise in specific platforms to those mediums, Rep A sells via email, Rep B via LinkedIn. Knowing the key types of sales territories is useful. But figuring out which type suits you best requires a systematic process. Building Your Sales Territory Map Template Whether it’s your first sales territory map template or your seventeenth, these five steps help streamline and simplify the process. Step 1: Define Goals and Objectives Start by defining your goals and objectives, mainly relating to sales, and set measurable goals. Take on revenue goals first, using your revenue forecast to determine how much new revenue you need from each territory, whether from upsells, cross-sells, or new leads. For example, if your goal is to drive sales for a new product functionality that benefits certain industries more than others, that’s the goal to focus

AI, Customer Success

6 AI for Customer Success Use Cases

6 AI for Customer Success Use Cases CSOps 12 min Updated: August 3, 2026 Efficiency has been the dominant theme in tech stack decisions for several years running: if a tool doesn’t clearly improve ROI, it gets cut. That pressure lands hardest on customer success, where the difference between a renewed account and a churned one increasingly comes down to one thing: a genuinely meaningful relationship with the customer. The numbers back up why that relationship matters so much. Acquiring a new customer costs five to ten times more than retaining an existing one, and existing customers are consistently easier to sell to than new prospects, since they’ve already validated your product and built trust with your team. With that much upside on the table, AI has become the obvious lever for customer success teams to reach for, the question is where it actually earns its keep. AI can meaningfully improve net retention rate (NRR) and the customer success processes underneath it. Yet adoption has historically lagged the opportunity: the Customer Success Collective’s State of Customer Success 2023 report found 66% of customer success professionals weren’t using AI in their role at all, a real gap at the time, and one worth checking against more current research given how much AI tooling has advanced since. This guide covers why AI matters for customer success specifically, and six concrete use cases to build around. Get our latest insights into your inbox How Has Customer Success Changed? Customer behavior keeps shifting. Preferences, pain points, and priorities evolve continuously, which means CS has to be agile and quick to adapt rather than running a fixed playbook indefinitely. Remote and hybrid CS is the norm. More customer success roles operate remotely than ever, and the tools and processes supporting them have to work as well outside an office as inside one. Digital-led operations are standard. Onboarding is increasingly automated, and generative AI in chatbots resolves customer questions using real account context rather than a generic script. The growth outlook has shifted from acquisition to resilience. Businesses have moved from “growth at all costs” toward driving more value from existing customers and existing tools, which raises retention’s importance relative to new-logo growth. Churn prediction has become a genuine discipline. Monitoring customer health to catch potential churn early, so a CSM can intervene before sentiment turns, is now a core CS function rather than a reactive afterthought. Why You Need AI for Customer Success Now Retention matters more than ever, and continuously proving value to customers, efficiently, is the harder half of that job. The challenge is scale: the sheer volume of customer behavioral data generated daily makes it genuinely difficult for a CSM to find the signal that actually matters, and even when the data is found, translating it into an unforgettable customer experience takes more than access, it takes the right tooling to act on it. Left unaddressed, that gap shows up as customers who feel forgotten, unclear on how to get value from your product, and quietly disengaging. The upside of closing it is real: even a 5% increase in customer retention can increase revenue by 25-95%. A few more reasons AI earns its place in a CS stack specifically: It automates workflows without replacing the human relationship, cutting reliance on manual data entry so CSMs spend more time actually building relationships. It surfaces more accurate, complete data from a pile most humans can’t realistically comb through manually, cleaner data means sharper insight. It flags sentiment shifts, positive to neutral to negative, and suggests next-best steps to course-correct before the relationship actually sours. It highlights new ways for customers to get value from your product, driving additional value continuously rather than only at renewal time. It shifts CS from reactive to proactive, since a health-score change is visible before a support ticket or a cancellation notice ever arrives. 6 Use Cases of AI for Customer Success 1. Track and Evaluate CSM Activities Most CS leaders can tell you how many accounts a CSM owns. Far fewer can tell you, with any real precision, what that CSM actually did on those accounts last month, or whether it worked. Activity tracking in CS has historically meant a task log or a calendar count: how many calls, how many emails, how many meetings. That tells you volume. It doesn’t tell you whether the activity was any good. The distinction matters because CSM effectiveness varies enormously even at the same activity volume. Two CSMs can each log fifteen customer touchpoints in a month, one spending that time in surface-level check-ins with a low-influence contact, the other running structured business reviews with the actual economic buyer. A dashboard counting fifteen touchpoints treats both CSMs identically. A CS leader trying to coach, staff, or forecast off that dashboard is working from a genuinely misleading picture. This is where Nektar’s CSOps capability is built to do more than log activity, it evaluates it. Every customer interaction is treated as a chance to understand its actual impact on revenue growth or churn mitigation, not just as a box checked on an activity report. Specifically, Nektar: Monitors whether activities are actually taking place, closing the gap between what a CSM reports doing and what the captured email, meeting, and call data actually shows happened, without requiring the CSM to self-report. Assesses effectiveness with detailed insight, not just a raw count. The same underlying data that shows an interaction happened also shows which persona was involved, when it happened relative to the renewal timeline, and what kind of meeting it actually was, a QBR, a support escalation, a casual check-in, each of which carries a different signal about deal or account health. Surfaces patterns a manager can actually coach against. Once activity is tied to persona, timing, and meeting type, a CS leader can see, account by account and CSM by CSM, whether engagement is concentrated in low-influence check-ins or genuinely reaching the stakeholders who matter, and use that as the basis

Salesforce

Salesforce Lead vs. Opportunity: Explore the Difference

Salesforce Lead vs. Opportunity: Explore the Difference Salesforce 9 min Updated: August 3, 2026 Salesforce leads and Salesforce opportunities are usually the first two terms that trip people up when they start working in the platform. Understanding the distinction matters for more than vocabulary: getting the lead-to-opportunity handoff right is what keeps a pipeline, and eventually a forecast, actually reflecting reality. This guide covers what each object represents, the practical differences between them, and when (and how) to convert a lead into an opportunity. Get our latest insights into your inbox What Is a Salesforce Lead? A lead is the earliest stage in the customer acquisition process in Salesforce: a potential customer or business that’s shown initial interest, but whose interest hasn’t yet turned into a concrete sales opportunity. Leads are usually people or organizations who’ve interacted with your company in some specific way, filling out a contact form, attending a webinar, downloading a resource. Consider a software company, XYZ Tech, offering a project management tool. A marketing campaign drives several professionals to sign up for a free demo on the website. At this point, those individuals are leads in Salesforce, their basic information (name, email, source of interest) is recorded, but they haven’t reached the point of being ready to buy. They need further nurturing, information, or engagement before anyone can tell whether XYZ Tech’s tool actually fits their needs. As sales and marketing interact with these leads, some gradually progress to the next stage: becoming opportunities. What Is an Opportunity in Salesforce? An opportunity represents a distinct, more advanced stage: a lead or prospect who’s moved past initial interest and is now a qualified prospect with a real chance of buying. Opportunities are the structured framework sales teams use to actually pursue and close deals, tracking potential revenue, probability of closing, sales stage, and expected close date, giving a complete picture of a prospect’s real sales potential. Back to XYZ Tech: after nurturing leads from the campaign, the sales team identifies John Smith, who’s had multiple conversations with reps, attended a product demo, and expressed intent to implement the software. At this point, John transitions from lead to opportunity, and Salesforce now tracks: Estimated deal value Sales stage (for example, “Proposal Sent”) Probability of closing, based on historical data and current circumstances (for example, a 70% chance of closing) Expected close date (for example, within the next 30 days) Salesforce Leads vs. Opportunities Difference 1: Stage of the sales cycle Leads represent the earliest stage, potential customers who’ve shown initial interest but aren’t yet qualified or ready for direct sales engagement. Opportunities reflect a more advanced stage, where a lead has progressed to genuine sales potential. Difference 2: Information depth Leads contain basic contact information (name, email, source of interest) and limited data on specific needs. Opportunities include far more: deal size, probability of closing, current sales stage, and expected close date, the depth that actually supports revenue tracking and forecasting. Difference 3: Purpose Leads exist to identify potential customers who need further nurturing and qualification. Opportunities are actionable prospects sales teams actively pursue to close. Difference 4: Conversion process Leads convert into contacts, accounts, or opportunities once they meet specified criteria and show real interest or readiness. Opportunities, by contrast, don’t convert into other Salesforce entities, they’re worked directly toward a successful sale. Difference 5: Sales tracking Leads help track the effectiveness of marketing campaigns and lead-generation efforts. Opportunities provide the insight sales teams need to prioritize deals and forecast revenue accurately. When Does a Lead Convert Into an Opportunity? Converting a lead is a real judgment call, not a mechanical checkbox. A few practices help make that judgment more consistent: Qualification and engagement. Has the lead shown genuine interest, and engaged meaningfully with sales or marketing? Budget, authority, need, and timeline (BANT) are still a reasonable starting framework. Information completeness. Make sure the lead’s profile has enough accurate detail, contact information, organization data, relevant notes, before converting. Incomplete or wrong information downstream just creates confusion in the opportunity it becomes. Lead scoring. A scoring system that assigns numerical value based on behavior and engagement gives conversion a consistent, less subjective threshold rather than relying on individual rep judgment alone. Intent signals. A demo request, a quote request, a trial start, these are stronger readiness signals than general interest and worth weighting heavily. Sales team feedback. The rep actually talking to the lead usually has context a scoring model alone can’t fully capture; their read on readiness is worth soliciting directly. Why Lead and Opportunity Accuracy Matters More Now The core distinction between a lead and an opportunity hasn’t changed. What’s changed is what’s riding on getting the conversion and the subsequent opportunity data right. A growing share of CRM data, including exactly the stage, probability, and contact-role fields covered above, is now read and acted on directly by AI agents rather than reviewed by a person first. A lead converted too early, or an opportunity with an inflated probability field, used to just produce a slightly optimistic forecast a manager could catch. Fed into an agent acting on that same field directly, prioritizing outreach, flagging a deal as at-risk, updating a forecast category, the same inaccuracy becomes a wrong automated decision, with less human review standing between the data and the action. This is also where Opportunity Contact Roles specifically matter more than most teams treat them. An opportunity with only the original lead’s contact attached, and none of the other stakeholders who joined calls along the way, gives both a rep and an AI agent an incomplete picture of who’s actually involved in the deal. Nektar’s Data Foundation automatically captures the activity that determines whether a lead is actually ready to convert, and once it does, keeps the resulting opportunity’s contact roles and stage current as new stakeholders join and the deal progresses, without requiring a rep to manually update either. Buying Group Intelligence specifically fills the Contact Role gap most opportunities have: automatically

Founder Story

Growing Beyond $1M ARR: Mistakes to Avoid in the Valley of Death

Growing Beyond $1M ARR: Mistakes to Avoid in the Valley of Death RevOps 12 min Updated: August 03, 2026 If you’re a SaaS business that’s crossed the $1M ARR mark, congratulations. You belong to a group of less than 1% of businesses that manage to reach that number. A bigger challenge lies ahead: the valley of death. Only 4% of SaaS companies reach $1 million in revenue, and only 0.4% make it to $10 million. Scaling to $10 million ARR and beyond is where a lot of promising startups quietly dissolve, and the odds only get longer as the revenue number climbs. Get our latest insights into your inbox What Is the Valley of Death? According to Abhijeet Vijayvergiya, CEO at Nektar.ai, every startup goes through similar challenges, but the specific challenge changes at every stage. What breaks a company at $0-1M is different from what breaks it at $10-25M, which is different again from $25-50M. Each stage needs a genuinely different approach, not more effort applied to the same playbook. 1. Getting to $1 Million: Find Your Product-Market Fit In the 0-1 journey, focus on the core mission a startup was actually formed around is what matters most. This is the stage where founders validate their product’s value proposition, answering two questions directly: is there a real market for this, and does the product actually fit that market? Trying to do a bunch of different things and seeing what sticks is a recipe for disaster here. $0-1M is about calibrated steps toward validation, not breadth. Abhijeet VijayvergiyaCEO & Co-founder, Nektar.ai It’s very easy to get distracted. I think a lot of startups die because they lose focus and they get distracted from their core mission. This does not mean that you can’t pivot. You definitely should pivot when you evolve as a company, you achieve product market fit or you see traction. But at the same time, you should not be doing too many things. That’s a very common mistake I have seen that a lot of start-ups make. Figure out what you’re solving for. Every start-up starts with a vision. You have a problem that you feel is unsolved and something that you can uniquely solve in a way nobody is solving it today. And you can do it X times better in case it’s already sold by somebody. Trying to force growth is what makes several startups fail to go beyond this stage. An initial team’s job is to help a founder see clearly whether product-market fit is actually there, not to force a scaling motion ahead of it. In the 0-1M journey, staying focused, iterating fast, and evolving to solve the problem the company was actually built for, with real commercial value attached, is the whole job. 2. $1M to $10M: Double Down on What Has Worked This is where the valley of death begins in earnest. A startup here has a million dollars in revenue and a validated core value proposition, with a very real, very difficult climb to $10 million ahead of it. Abhijeet’s guidance for this stage is specific: if you’ve achieved product-market fit, you’re in a strong position to scale, but not by opening new markets, launching new products, or chasing new customer segments in a rush to hit the next number. What matters is looking hard at the problem you’ve actually solved and how you solved it, then templating that into a real playbook. Do more of what has worked for you. Just doing this makes the $1-$10m journey quite easy. In my past experience, we figured out how the founders sold to the first 5, 10, 20 and 50 people. We could really onboard sales teams in a way that they started delivering their first deal within the first 6 months of joining in a new market. Abhijeet Vijayvergiya Customer retention matters just as much as new logo growth at this stage. Abhijeet draws on his experience at Capillary Technologies here: We did a great job at retaining our customers. We still have the lowest churn rate in the industry. We’ve always focused on delivering that delight to our users and customers so that they become our evangelists. Abhijeet Vijayvergiya Surviving the valley of death requires founders to keep returning to why the company and product exist in the first place, and to convert the tribal knowledge floating in their own heads into a working, teachable playbook. The first and foremost thing that you should do once you hit a product market fit & you’re looking to scale and ramp up is to articulate that knowledge that is in your head into a playbook and templatize it. That gives you a solid foundation and a scalable model. Abhijeet Vijayvergiya 3. The Journey From $10M to $50M After $10M, most startups start hitting real ceilings in their existing markets. Innovation becomes the difference between plateauing and continuing to climb. You need to continuously innovate, whether it’s creating new products, going for price upgrades, or adding more value to what you’re currently offering. Abhijeet Vijayvergiya Strategy and product both come into sharper focus between $10M and $50M, and one of the most important habits Abhijeet points to is a relentless, ongoing accounting of sales capacity against the addressable market. We would always go into that exercise of what’s our addressable market, how many logos are out there, have we spoken to all of them? And if not, how soon can we speak to them? Because they all have similar problems, they need us, we need to be out there in those meeting rooms where they are thinking about solving those problems. Abhijeet Vijayvergiya Investing in sales enablement becomes crucial here too, so a growing bench of new reps can operate closer to the level the founding team once did on its own. Abhijeet also points to this stage as the right moment to hire VPs across functions, with a sharp caveat about hiring well rather than hiring fast. What I have

CRM

Watch Out for These 8 Types of Dirty Data in Your CRM in 2026

Watch Out for These 8 Types of Dirty Data in Your CRM in 2026 CRM 10 min Updated: July 31, 2026 Dirty data is one of the most expensive problems in revenue operations, and one of the easiest to underestimate, because it rarely shows up as a single, visible failure. It shows up as a slightly-off forecast, a rep who can’t reach a lead, a campaign sent to the same person three times. Gartner’s widely-cited estimate puts the average cost of poor data quality at $12.9 million per organization annually (research from 2020 that remains the standard industry reference point), and that’s before counting the slower, harder-to-trace cost of decisions made on top of bad numbers. High-quality data is the foundation revenue operations runs on. Accessible, accurate data lets leaders act on timely insight instead of guessing; dirty data does the opposite; it erodes trust in the CRM itself until reps stop relying on it altogether. Here are the eight types still sitting in most CRMs today, what they actually cost, and how to clean each one. Get our latest insights into your inbox What Is Dirty Data? Dirty data is inaccurate, incomplete, or poorly structured information that disrupts a company’s database and undermines the functions that depend on it, GTM strategy, segmentation, personalization, lead scoring, prospecting, and ideal customer profile planning among them. The result is poor decisions, inefficiency, missed opportunities, and in some cases real reputational damage. Dirty data usually enters a CRM through manual entry, human error, poor coordination between departments, or third-party integrations that weren’t built to talk to each other cleanly. Understanding the specific forms it takes is the first step to actually fixing it, rather than treating “clean up the CRM” as one vague, occasional project. The 8 Types of Dirty Data in Your CRM 1. Duplicate Data The most common type. Repeated leads, accounts, and contacts, sometimes exact copies, sometimes partial duplicates that are harder to catch and usually the result of manual entry error. Duplicate data skews analysis, clutters workflows, inflates storage, and produces the kind of repetitive outreach that actively damages a prospect’s impression of your team: sending the same ABM-targeted email to what looks like three different people reads as automated rather than personalized, and it costs conversions. How to clean it: Manual cleanup doesn’t scale and rarely catches partial duplicates. An automation platform that detects, merges, or removes duplicates based on your own matching criteria is the only approach that keeps pace with the rate new duplicates get created. 2. Insecure Data Data collected or retained without proper consent, or stored in a way that doesn’t meet current privacy regulations (GDPR, CCPA, and the growing list of state and national frameworks that have followed). Non-compliant data isn’t just a hygiene issue, it’s a direct financial and legal exposure. Regulatory enforcement in this space has only gotten more active since GDPR’s early years, and CRM-level compliance depends entirely on knowing what data you actually hold and whether it was collected properly. How to clean it: Delete unusable or non-compliant records, merge duplicates to keep information current, consolidate your data stack so consent status isn’t scattered across systems, and host your CRM on infrastructure built for compliance from the ground up. 3. Outdated Data Data that was accurate once and no longer is. A prospect who filled out a form as a cold lead may now be deep in an active evaluation, job changes, reorganizations, and mergers all age CRM records quickly, and a CRM that hasn’t caught up keeps treating a warm, engaged buyer like a fresh contact. That mismatch directly limits how far a prospect actually progresses through the funnel, since the content and outreach they receive doesn’t match where they actually are. How to clean it: Purge and cleanse data before any migration or system integration. Decide what “too old to be useful” means for your specific business and enforce it consistently, manual cleansing takes days or weeks; automated tools can do it in hours. 4. Incomplete Data A record missing the specific fields needed to act on it, a phone number with no email, a contact with no company size or role. Incomplete data makes lead scoring and segmentation meaningfully harder, and it’s extremely common: most CRMs are missing a large share of the activity and contact detail that would actually make a record usable. How to clean it: Manual backfilling doesn’t scale past a small dataset. Automated activity and contact capture fills gaps as they occur rather than requiring someone to go back and reconstruct missing fields after the fact. 5. Inaccurate Data Information that was entered correctly into the right field, but is simply wrong, a fake phone number, a mistyped email, a title that’s no longer accurate. Inaccurate data is arguably the most damaging type on this list because it looks trustworthy; nothing about the record signals that it’s wrong until a rep tries to act on it and can’t. Reaching the wrong person, or failing to reach the right one, at a critical moment in a deal can stall the entire purchasing process. How to clean it: Prevent inaccurate data from entering the system in the first place by validating it at the point of capture, rather than trying to catch it after the fact. Automated capture tools that pull data directly from real interactions, rather than relying on manual entry, meaningfully reduce how much inaccurate data gets in. 6. Incorrect Data Information stored in the wrong field or format, a phone number in a text field, a job title where a company name belongs, a date in the wrong format entirely. This produces erroneous campaign targeting and irrelevant communication, and it compounds at scale: a single malformed field might be a nuisance, thousands of them make reliable reporting effectively impossible. How to clean it: Enforce field-level standards so reps can’t enter data outside expected formats, and use validation rules or lookup tables to catch format errors programmatically rather than relying on

Top 9 Sales Commission Software
Sales

9 Sales Commission Software for 2026

9 Sales Commission Software for 2026 Sales 10 min Updated: July 30, 2026 Sales commissions remain one of the oldest and most direct motivators in a sales org, and getting them wrong, through miscalculation, delay, or disputed numbers, erodes exactly the trust a comp plan is supposed to build. Managing commissions manually, through spreadsheets, doesn’t scale past a small team, and it introduces the kind of error that turns a well-designed comp plan into a source of friction rather than motivation. That’s the job sales commission software exists to do: automate the calculation, keep it transparent to reps, and remove the manual reconciliation work from finance and RevOps. Get our latest insights into your inbox What Is Sales Commission Software? Sales commission software (also called incentive compensation management, or ICM) automates the calculation and payment of sales commissions based on quota attainment, deal value, and whatever specific rules a comp plan defines, accelerators, tiers, clawbacks, SPIFs. It removes manual calculation error, gives reps real-time visibility into what they’re earning and why, and gives finance and RevOps teams an auditable, consistent process instead of a spreadsheet that only one person fully understands. 9 Sales Commission Software Platforms for 2026 Spiff, generative AI-assisted commission plan design and management Qobra, no-code compensation automation for modern GTM teams Everstage, no-code commission platform with rep-facing analytics Anaplan, enterprise connected-planning platform with compensation modeling QuotaPath, commission tracking built for SMB and remote sales teams Xactly, enterprise-grade incentive compensation management Performio, data-first commission platform for complex, high-volume sales orgs SalesCookie, budget-friendly commission platform for SMBs OpenComp, compensation platform focused on equity and cash comp together Overview of the 9 Platforms 1. Spiff Salesforce Spiff (fully integrated into Salesforce following the February 2024 acquisition, now sold as Salesforce’s own Incentive Compensation Management product) is a well-established commission platform known for its generative AI assistant, branded Spiff Assistant, which helps admins understand and troubleshoot commission plan logic in plain language, and a Commission Estimator feature giving reps live visibility into potential earnings as they work a deal, directly inside Salesforce. Key features: generative AI-assisted plan troubleshooting (Spiff Assistant), real-time commission estimation during active deals, no-code plan design tools, mobile access for reps. Best for: Teams already on Salesforce wanting commission visibility built into the deal workflow itself, especially now that Spiff operates as part of the Salesforce ecosystem. 2. Qobra Qobra is a sales compensation automation platform built for modern GTM teams, positioning itself around eliminating spreadsheet-based commission management entirely. It automates plan design (accelerators, tiers, bonuses, clawbacks) without requiring code, gives reps real-time visibility into earnings and payout breakdowns, and supports scenario modeling for finance teams evaluating the cost impact of plan changes or new hires. Key features: end-to-end compensation automation from plan design to payout, real-time rep-facing dashboards, scenario modeling and forecasting for finance leaders, broad integration coverage across CRM (Salesforce, HubSpot, Pipedrive), data warehouses (Snowflake, BigQuery), and HR/payroll systems. Best for: Revenue and finance teams wanting to move off spreadsheets entirely, with strong support for both US and European payroll and compliance requirements. 3. Everstage Everstage offers a no-code platform for building commission plans, SPIFs, and bonus structures without engineering support, with rep-facing tools for tracking earnings and understanding plan logic. Key features: no-code plan and rule building, rep-facing earnings dashboards, collaboration tools for plan feedback between finance and sales leadership. Best for: Mid-market teams wanting a no-code platform they can configure and adjust themselves without ongoing vendor or engineering dependency. 4. Anaplan Anaplan is a large-scale connected-planning platform used across finance, supply chain, and workforce planning, with incentive compensation as one significant use case among several. Its core strength is scenario modeling at scale, useful specifically for larger organizations that need to model compensation changes alongside broader business planning. Key features: enterprise-scale scenario modeling and what-if planning, cross-functional connected planning beyond compensation alone, strong integration with BI tools like Tableau and Snowflake. Best for: Large enterprises that want compensation planning integrated into a broader connected-planning platform rather than a standalone comp tool. 5. QuotaPath QuotaPath is built specifically for small to mid-sized businesses and remote sales teams, with a focus on affordability, transparency, and ease of setup relative to enterprise-grade platforms. It offers a free tier for smaller teams, real-time earnings tracking, and self-service rep portals where reps can model “what-if” scenarios for their own commissions. Key features: self-service rep earnings simulator, free tier available for smaller teams, straightforward CRM (Salesforce, HubSpot, Pipedrive) and payroll (Gusto, Rippling, ADP) integrations. Best for: SMBs and remote-first sales teams wanting an affordable, fast-to-implement commission tool without enterprise-level complexity or cost. 6. Xactly Xactly is an established, enterprise-grade incentive compensation management platform used widely by large and Fortune 500 organizations, with strong compliance tooling for operating commission plans across multiple countries and tax jurisdictions. Key features: enterprise-scale plan design and administration, multi-country tax and compliance support, territory and quota planning, strong security and audit-trail certifications (SOC 2, ISO 27001). Best for: Large enterprises needing multi-country compliance and audit rigor alongside commission calculation, not just a domestic SMB use case. 7. Performio Performio positions itself as a data-first commission platform for organizations with complex data environments, aggregating commission-relevant data from multiple CRM, ERP, and other business systems into one place, aimed at industries and teams where commission calculation depends on data scattered across several systems rather than one clean CRM. Key features: multi-source data aggregation for commission calculation, automated approval workflows for payroll processing, audit-ready reporting. Best for: Organizations with commission-relevant data spread across multiple systems (CRM, ERP, and others) that need a platform built to reconcile all of it, not just sync with a single CRM. 8. SalesCookie SalesCookie is a cloud-based commission platform aimed at small and mid-sized businesses, with pre-built plan templates and a straightforward, lower-cost setup relative to enterprise platforms. Key features: pre-built, industry-specific plan templates, multi-currency support for global payouts, straightforward CRM (HubSpot, Pipedrive) and payroll integrations. Best for: SMBs wanting a fast, template-driven setup without building a comp plan structure from scratch. Confirm

Top 10 Chrome Extensions for Salesforce
Salesforce

Top 10 Chrome Extensions for Salesforce in 2026

Top 10 Chrome Extensions for Salesforce in 2026 Salesforce 11 min Updated: July 30, 2026 Salesforce is a browser-first platform, and most admins, developers, and reps spend their entire day in a pinned Chrome tab. Chrome extensions exist to make that tab less painful with faster navigation, fewer clicks, and metadata you’d otherwise have to dig for. It’s a useful reminder that this category moves fast: extensions get deprecated, acquired, or quietly stop supporting Lightning, and a “best of” list from even a year ago can point you somewhere that no longer works. Salesforce Ben’s own comment section flags this exact problem — several older “top extension” posts recommend tools that broke when Lightning matured. Here’s the current, working list for 2026. Get our latest insights into your inbox Top 10 Chrome Extensions for Salesforce in 2026 1. Salesforce Inspector Reloaded The original Salesforce Inspector is still technically installable, but the community has moved on. Salesforce Inspector Reloaded, maintained by Thomas Prouvot and contributors, replaced it and is now the default recommendation across most current admin/dev roundups. It overlays metadata directly on the Salesforce UI — field API names, object details, permission sets — and adds record and field-level data export, SOQL query execution, and import tooling without leaving the page. Key features: instant field/API name inspection, in-browser SOQL editor, CSV export/import, permission and profile analysis. 2. ORGanizer for Salesforce If your team juggles more than one Salesforce org — sandbox, UAT, production — ORGanizer solves the single most expensive mistake in that workflow: making a change in the wrong org. It color-codes and labels browser tabs by org, stores login credentials securely, and gives one-click access to frequently used setup pages. Key features: org color-coding and labeling, saved credentials, quick-links library, one-click login across environments. 3. Salesforce Advanced Code Searcher Still one of the fastest ways to search Apex classes, triggers, and Visualforce pages directly inside your org without opening Setup and clicking through menus. The “advanced quick find” panel jumps straight to a specific class or page. Key features: in-org code search across Apex and Visualforce, advanced quick-find, developer utilities. 4. Salesforce Tool Suite A newer, broader entrant that bundles bulk data operations, debugging, schema exploration, and metadata management into one extension rather than several. Useful if you’d rather run one well-maintained tool than stack five narrow ones — the “one extension per job” principle most current guides recommend. Key features: metadata reports, real-time debug log analysis, schema explorer, event monitoring. 5. Salesforce Sales Cloud Everywhere (Gmail Integration) Salesforce’s own native extension, syncing records directly into Gmail. Reps can create and update contacts, tasks, and opportunities, get real-time engagement alerts, and reference Salesforce data without leaving their inbox. Key features: Salesforce-to-Gmail record sync, in-inbox record creation and updates, real-time engagement notifications, calendar connection. 6. Salesforce Mass Editor Turns any Salesforce list view into a bulk editor. Insert, clone, update, or delete multiple records in one interface instead of opening each one individually — still one of the highest-leverage extensions for admins doing data cleanup or migrations. Key features: bulk record editing across Classic and Lightning, list view data export, Excel-based mass data transfer. 7. Revenue Grid for Salesforce and Gmail Brings Salesforce and Chatter data into the inbox with an activity-capture layer underneath — logging emails and meetings against records and surfacing pipeline and relationship signals from inside Gmail. It’s the one entry on this list that’s aiming at the same problem Nektar solves (complete, structured activity data in Salesforce) through a different architecture: a rep-installed browser extension rather than a headless integration. Worth understanding the tradeoff before choosing between them — more on that below. Key features: email sidebar, activity capture, pipeline inspection, relationship-health signals. 8. Salesforce Apex Debugger Simplifies working through Apex debug logs — search by string, filter by size or date, and jump to key pages with keyboard shortcuts instead of scrolling through raw log output. Key features: log string search, size/date filtering, keyboard-shortcut navigation, structured JSON/XML log viewing. 9. Surfe (LinkedIn-to-Salesforce sync) Replaces  Clearbit for Salesforce – Lite, which no longer exists. Surfe syncs LinkedIn profile and conversation data directly to Salesforce — one-click contact and lead creation from a LinkedIn profile, message logging as activities, and data enrichment on new records. Key features: one-click LinkedIn-to-Salesforce contact creation, message and InMail logging, data enrichment on lead/contact creation. 10. Salesforce Navigator for Lightning Still one of the simplest productivity wins on this list — type-ahead navigation to any Salesforce page, object, or record without clicking through menus, plus Classic-to-Lightning URL mapping and an account merge tool. Key features: type-ahead page navigation, Classic-to-Lightning URL mapping, account merge tool, fast task and record creation. Where Chrome Extensions Hit a Ceiling for Revenue Teams Everything above is genuinely useful for the job it’s built for. Admin productivity, code search, bulk edits, faster navigation: these are real time savings, and a browser extension is the right shape of tool for all of them. Data capture is a different job, and it’s worth being honest about why browser extensions are a weaker fit for it, since #7 on this list (Revenue Grid) and #5 (Sales Cloud Everywhere) are both trying to do it. A browser extension only sees what happens inside that browser, on that rep’s machine, while it’s installed and running. That creates three structural gaps that don’t go away no matter how good the extension is: Coverage depends on installation and compliance. If a rep doesn’t install it, disables it, or works from a different device, that activity never gets captured. There’s no way to backfill it after the fact. It only sees the browser’s version of events. A meeting logged from a calendar app, a call made from a phone, an email sent from a different client — none of that reaches a browser extension unless the rep manually re-enters it. Historical data is out of reach. A browser extension installed today has no visibility into the 12 months of email and meeting history that

AI, Sales

10 Killer Tips to Use ChatGPT for Sales in 2026

10 Killer Tips to Use ChatGPT for Sales Sales 10 min Updated: July 30, 2026 ChatGPT has moved well past the novelty phase in most sales orgs. Reps use it for drafting, research, and prep the same way they use a search engine, reflexively, without much thought.  The current generation of models (the GPT-5 family, as of mid-2026) supports persistent memory across conversations, live web browsing, and multi-tool agentic actions as standard capabilities, a meaningfully different tool than the ChatGPT most sales teams first tried in 2023. The real question in 2026 isn’t whether ChatGPT can help with sales. It’s where general-purpose prompting genuinely helps, and where it quietly runs out of road because it doesn’t know anything specific about your actual deals, accounts, or CRM data. This guide covers both: ten practical ways to use ChatGPT for sales work today, and where that approach hits a wall that only grounded, CRM-connected AI can get past. Sales enablement software exists to fix that ratio: giving reps the tools, content, and resources they need to spend more time selling and less time on everything around it. Get our latest insights into your inbox What Is ChatGPT, and What’s Actually Changed ChatGPT is OpenAI’s conversational AI assistant, built on its GPT model family. The capability gap between the original 2022 version and today’s is substantial: current models hold context and memory across sessions rather than starting fresh every conversation, can browse the live web instead of working from a frozen training cutoff, and can take multi-step agentic actions (running a workflow across several tools in sequence) rather than just answering a single prompt. What hasn’t changed: ChatGPT still only knows what you tell it in a given conversation, plus whatever it can find via web search, plus whatever’s in its memory of your prior chats. It has no native access to your CRM, your actual pipeline, your specific accounts, or your team’s real interaction history unless you explicitly connect it or paste that information in yourself. That single limitation is the throughline of this entire guide. Prompt Engineering Still Matters, But Less Than It Did Prompt engineering got treated as a standalone hot skill in 2023; current-generation models need far less prompt gymnastics to get a useful answer, since they’re better at inferring intent from a straightforward request. What still matters: being specific about the actual scenario, the industry, the objection, the buyer context, since a vague prompt still gets a generic answer regardless of how capable the underlying model is. 10 Ways to Use ChatGPT for Sales 1. Sales outreach drafting ChatGPT can draft personalized email templates and social outreach messages, and adapt tone and language across markets and cultures reasonably well. The catch: it can only personalize based on what you give it. A prompt that includes real, specific detail about the actual prospect (their stated priority, a recent company event, their role) produces a genuinely tailored draft. A prompt with a generic persona description produces a generic-sounding email with a name swapped in. 2. Upselling and cross-selling ideas Given a customer’s stated situation, ChatGPT can suggest complementary products or upgrades and help frame the value case for them. It can’t see actual purchase history or usage data on its own, that context has to come from you, pasted in or connected via an integration, or the suggestions default to generic “customers who buy X often want Y” reasoning rather than anything specific to your actual account. 3. Objection handling practice ChatGPT can generate objection-and-rebuttal scenarios for practice, and can role-play as a skeptical buyer raising realistic pushback. This is genuinely useful for rep training. It’s weaker as a real-time objection-handling tool mid-deal, since it doesn’t know what this specific buyer has actually said in prior conversations unless you feed that context in directly. 4. Lead qualification question design ChatGPT is good at generating a structured set of qualifying questions (budget, authority, need, timeline) tailored to an industry or persona. What it can’t do on its own is apply those questions to your actual leads and produce a real score, that requires connecting it to your actual lead data, which is a different (and more valuable) capability than prompting alone provides. 5. Market and competitor research Current models with live browsing can pull recent public information, reviews, and reporting on competitors reasonably well, a real improvement over the original ChatGPT’s frozen training cutoff. It’s still working from public information only. It has no visibility into how your specific deals are actually going up against a specific competitor in your own pipeline, which is a private, first-party data problem no general-purpose AI assistant can solve without being connected to your CRM directly. 6. FAQ and first-line support drafting ChatGPT can draft consistent answers to common customer questions, useful for building out a knowledge base or a first-pass chatbot script. For anything account-specific (a customer’s actual contract terms, their specific configuration, their support history) it needs that information fed in, or it will answer confidently and generically, which is worse than not answering at all in a support context. 7. Sales strategy brainstorming As a brainstorming partner, ChatGPT can synthesize market trends and general best practice into a reasonable starting point for positioning, segment strategy, or channel prioritization. Treat the output as a first draft to react to, not a strategy grounded in your actual pipeline data, performance history, or competitive reality, since it has none of that unless you supply it. 8. Sales training and role-play This is one of the strongest, least caveated use cases on this list. ChatGPT can realistically simulate a skeptical prospect, adapt the objections it raises based on how a rep responds, and give a rep low-stakes practice reps can’t easily get elsewhere. Current models are noticeably better at this than the original 2023 version, holding a more consistent persona across a longer role-play. 9. Deal scoring criteria design ChatGPT can help you design a scoring rubric, which factors matter, how to weight them, what

RevOps

Revenue Operations vs Sales Operations

Revenue Operations vs Sales Operations: 6 Key Differences in 2026 Sales 12 min Updated: July 30, 2026 Two departments, both aimed at growing the company’s revenue, both eliminating silos, both supporting business strategy. And yet they’re not the same function, which is exactly why most companies still struggle to tell RevOps and SalesOps apart. The rise of new revenue-related roles over the past several years has made this confusion worse, not better. This guide differentiates the two by definition, use case, function, and fit, and includes perspective from Lorena Morales, Director of Global Digital Marketing Revenue Operations at JLL, who addressed the distinction directly in a conversation with us. Get our latest insights into your inbox What is Revenue Operations (RevOps)? RevOps exists to maximize a company’s revenue potential by integrating marketing, sales, and post-sales functions into one coordinated system, rather than three departments each optimizing their own metrics in isolation. A RevOps function breaks down the silos between marketing, sales, customer success, customer support, and finance, using cross-department visibility to achieve three shared objectives: surfacing new revenue opportunities, improving lead conversion, and closing revenue leakage. What Is the Role of Revenue Operations? RevOps increases transparency and communication across every business function touching the customer, from lead generation through customer support. The team is responsible for managing and analyzing data across the entire customer lifecycle, identifying potential customers already in the pipeline, and protecting revenue that’s already been won. RevOps also drives enablement, employee onboarding, training, and the processes that make those things consistent, aiming to increase visibility and communication in ways that ultimately show up as revenue growth. What is Sales Operations (SalesOps)? SalesOps supports the sales team specifically, providing strategic direction and reducing friction so reps can focus on selling. It’s sometimes called sales support or business operations, and it uses software, engagement techniques, and strategic planning to drive growth within the sales function. SalesOps promotes best practices for reps and gives cross-departmental visibility into sales data. Its role has broadened over time to include more of the analytical insight sales teams need to sustain growth, not just administrative support. Source: Forrester Sales Operations Value Model What Is the Role of Sales Operations? SalesOps has two core functions: making sure the sales department has enough resources to stay productive, and enabling accurate data collection to support revenue forecasting. The specific structure and daily duties vary by industry and company size, but territory planning and sales forecasting are close to universal across SalesOps teams. Most also manage sales commission data and implement incentive compensation programs, and monitor the current pipeline directly. RevOps and SalesOps operate as distinct functions, but they work together, and each benefits the other’s effectiveness alongside the company’s overall revenue. What actually separates them? Revenue Operations vs. Sales Operations The core distinction: SalesOps focuses solely on the sales function, while RevOps aligns multiple departments toward shared business growth. Sales operations is best understood as a subset of revenue operations, not a competing function. SalesOps is more executional, handling day-to-day administrative work, data management, tech stack upkeep, form processing, training, so the sales team can focus on approaching leads and closing deals. RevOps takes the more holistic view, acting as a bridge between departments and increasing revenue opportunity by improving communication and shared goals across all of them. RevOps focuses on the entire customer journey, improving the experience at every touchpoint, prospecting, sales, onboarding, customer success, and renewal, which is what builds the long-term relationships that drive customer loyalty and, eventually, more revenue. The RevOps Function of an Organization Process. RevOps builds the well-structured, consistent processes that carry a customer from lead generation to cash collection, since inconsistent process is where revenue quietly leaks out of a funnel. Technology. RevOps selects the tools that make those processes actually run efficiently, and increasingly, makes sure those tools are integrated with each other rather than operating as disconnected point solutions. Data. Accurate data in systems like the CRM is the foundation everything else in RevOps depends on. The insights derived from it directly drive the strategic decisions that grow the business, and this pillar carries meaningfully more weight now than it did even a couple of years ago (more on that below). People. The team responsible for managing systems, process, and data, sized according to the organization’s scale and maturity. Team Structure: RevOps vs. SalesOps SalesOps teams are typically structured simply, aligned with the sales team itself, organized by region, product, or channel depending on the company’s go-to-market approach. Some SalesOps teams organize by function instead, with different members owning pipeline management, forecasting, and similar specific responsibilities. RevOps team structure is usually more complex, since it has to integrate with sales, customer success, marketing, and finance simultaneously. Most RevOps teams organize around functional areas, though some structure around customer segment instead, with team members responsible for driving revenue growth from a specific type of customer. Both functions ultimately aim to improve the bottom line. SalesOps focuses specifically on sales department performance; RevOps focuses on aligning every revenue-facing department across the company. Revenue Operations vs Sales Operations: Which Is the Best Fit for You? When You Need SalesOps SalesOps exists to remove the friction that’s tedious for a sales department specifically, updating lead data, building new pipeline processes, streamlining software, so the team can stay focused on revenue generation rather than administrative overhead. Strengthen your sales operations when: Your sales team spends more time on process than on actual selling You need better visibility and communication between sales and other departments Reps are reacting to inbound activity rather than proactively pursuing new prospects Your strategy is solid, but execution is the actual bottleneck When You Need RevOps RevOps starts from the premise that revenue generation keeps changing, and connects sales, marketing, and service data so every customer-facing department is working from the same picture. A strong RevOps function proactively drives revenue and gets ahead of problems, rather than reacting to one crisis after another. Strengthen your revenue operations when:

Sales, Salesforce

7 Salesforce Data Enrichment Tools

7 Salesforce Data Enrichment Tools for 2026 Salesforce 10 min Updated: July 30, 2026 What Is Data Enrichment? Your ideal customer isn’t just a name and an address. They’re a full person with specific buying habits, a role, and a digital footprint that either fits your ICP or doesn’t. Raw Salesforce data often shows only a distorted or partial picture of that person and the buying committee around them, more like a dusty attic full of half-remembered details than a usable profile. Your CRM is the backbone of business decisions, and skewed numbers make for skewed strategy. Data enrichment is what fills in the gap. Data enrichment adds complete information to your existing leads and contacts: phone number, address, company size, industry, location, and more. A lead named “Adam Doe from Acme Ltd” isn’t useful until enrichment fills in what Acme actually does and who Adam actually is beyond an email address, at which point a rep can tailor a pitch to an actual person instead of a name in a form field. Data enrichment happens in three ways: Direct: the lead provides complete information themselves, through a form or survey. Internal: you combine scattered information about the same lead across your own disconnected systems into one record. External: a third-party enrichment service appends information from outside sources you don’t otherwise have access to. Enrichment itself relies on two underlying processes: data cleansing (removing obsolete, duplicated, or partial data so the dataset is accurate to begin with) and data appending (pulling data from multiple sources together into one unified profile). Get our latest insights into your inbox Why You Need a Salesforce Data Enrichment Tool 77% of organizations report struggling with data quality issues, and reps are already dealing with a growing buying committee to keep track of. Data enrichment makes both problems more manageable by giving reps as much accurate context about a lead as possible, rather than a name and a guess. With the right enrichment in place, sales teams can: Work from better-quality data. Enrichment adds context and removes redundancy, which builds a more effective, less manually-maintained pipeline. Craft messages that actually land. Enrichment can surface real detail about a prospect’s interests and context, letting a rep tailor outreach that actually resonates instead of a generic template. Target the right people. A complete picture of a prospect’s company reveals who the real stakeholders are, letting reps invest effort in the buying committee that actually matters rather than guessing. Forecast with more confidence. Understanding a buying committee’s actual behavior, backed by real data instead of assumption, supports a more accurate read on which deals will really close. Enrichment also strengthens internal data over time: connecting the dots across enriched records surfaces patterns that help spot churn risk before it becomes a lost renewal. Our Top 7 Picks for Salesforce Data Enrichment 1. Nektar Nektar approaches enrichment differently than the other six tools on this list. Rather than appending third-party data purchased from an outside provider, it automatically captures first-party contact and activity data directly from email, calendar, and meetings, the actual record of what’s happening in your own relationship with an account, and structures it in Salesforce with zero rep effort required. That distinction matters: third-party enrichment tells you about a company in general; Nektar tells you what’s actually happening between your team and that specific account right now. Key features: Automated contact and opportunity management. Contacts get added, edited, and linked to the correct opportunity automatically, so it’s clear who’s actually likely to make the decision, not just who happened to get manually entered. Time Travel retroactive correction. Historical records get corrected as new context arrives, rather than staying static from the moment they were first created, closing gaps a one-time enrichment pass can’t touch. Depth and breadth of activity data. Every email and meeting exchange between buyers and sellers across the customer journey gets captured, giving granular, contact-level engagement visibility across leads, opportunities, and accounts. Data automation via Daisy AI. Teams can define logic that produces quantitative output directly in Salesforce: engagement scores, auto-filled fields like Competitor or MEDDPICC criteria, or an automatic stage update (marking an opportunity closed-lost after three months of no engagement, for instance), meaningfully reducing manual CRM upkeep. Vendor-neutral integration, sitting alongside third-party enrichment tools like the ones below rather than replacing them. Best for: Salesforce-first teams whose real gap is first-party activity data, what’s actually happening with an account, not just firmographic detail about the company itself. 2. ZoomInfo ZoomInfo is the largest and most established firmographic database in this category, providing 360-degree intelligence on individuals and companies, firmographic data including company name, size, industry, and location, natively optimized for Salesforce and appending existing or new records automatically as they enter the CRM. Its scale is the core differentiator: for most B2B categories, ZoomInfo’s database coverage is deep enough that a rep rarely comes up empty on a target account. Key features: Massive, continuously refreshed database covering millions of companies and contacts Deep data insights across both firmographics (size, industry, revenue) and technographics (what tech stack a company runs) Direct-dial and verified contact information for hard-to-reach decision-makers Native Salesforce integration with automatic record appending Intent data signaling when a company is actively researching a relevant category Best for: Teams needing the broadest possible firmographic and technographic coverage, especially for finding and verifying contact information for senior decision-makers. 3. Clay Clay has become one of the fastest-growing enrichment platforms in the category, built around waterfall enrichment: automatically querying dozens of underlying data providers in sequence until a field is actually filled, rather than relying on any single provider’s coverage gaps. Instead of buying one vendor’s fixed dataset, teams build their own enrichment logic, chaining together whichever combination of data sources and AI-driven research steps actually gets the field filled for their specific ICP. Key features: Waterfall enrichment across dozens of underlying data sources for meaningfully higher match rates than any single provider Highly customizable, no-code workflow builder for designing your own

Product

Top 7 Data Cleansing Tools

Top 7 Data Cleansing Tools Blog CRM 10 min Updated: July 30, 2026 What Is Data Cleansing? Data fuels every insight and decision a modern business makes, but raw data is rarely clean on arrival. It’s riddled with inconsistencies, errors, and duplicates, “dirty data” that leads directly to inaccurate analysis, flawed decisions, and wasted resources if it goes unaddressed. The scale of the problem is well documented. Gartner has found that only 3% of data meets basic quality standards, and separately estimates the average cost of poor data quality at $12.9 million per organization annually. Data cleansing, also called data scrubbing, is the process of identifying and correcting or removing corrupt, inaccurate, or irrelevant data from a dataset. It’s essential for maintaining data integrity and making sure decisions get made on numbers that actually reflect reality. Get our latest insights into your inbox Why Your Company Needs It Picture your best rep enthusiastically chasing a lead, only to find the phone number is wrong and the email bounced. That’s dirty data in action, and reps run into it constantly. Inaccurate, missing, or duplicated CRM information creates unnecessary friction for exactly the people trying to close deals, the equivalent of taking wrong turns across town: you might eventually arrive, but only after burning hours you didn’t need to. Dirty data quietly costs a business in a few specific, compounding ways: Wasted time and resources. Reps spend hours chasing cold leads, fixing mistakes, or manually verifying details that should have been correct in the first place, time that should have gone toward actually selling. Missed opportunities. Inaccurate data creates a real blind spot: targeted outreach fails to reach existing customers, and prospecting misses new ones. A single bounced email address can be the difference between closing a big account and never hearing back. Poor decision-making. Dirty data skews reports and metrics, distorting the picture leadership is actually working from and leading to decisions that look reasonable on the dashboard and wrong in practice. Strained customer relationships. Irrelevant outreach or contacting the wrong person at an account reads as carelessness to the buyer, damaging trust and making the company look sloppy at exactly the moment it’s trying to build credibility. Proper data cleansing turns chaotic, unreliable data into a single, trustworthy source of truth, and the tools below each take a different approach to getting there. Top 7 Data Cleansing Tools for 2026 Nektar, AI-powered CRM data hygiene, built to prevent dirty data at the source OpenRefine, free, open-source cleansing and transformation Tibco Clarity, enterprise-grade cloud data cleansing and management WinPure Clean & Match, specialist matching and deduplication Integrate.io, cloud ETL/ELT with built-in cleansing Melissa Clean Suite, address hygiene and verification Mammoth.io, no-code data transformation and cleaning Overview of the 7 Best Data Cleansing Tools 1.Nektar Salesforce data can quietly turn into a mess that undermines the reliability of every report built on top of it. Nektar addresses this differently than the other six tools on this list: instead of cleansing data after it’s already dirty, it automatically captures contact and activity data directly from email, calendar, and meetings, and writes it into Salesforce correctly structured from the start, preventing a large share of dirty data from ever entering the system in the first place. Here’s how Nektar solves the problem specifically: Unmatched sync accuracy. Nektar doesn’t just import data at a basic level. It analyzes records using AI to establish links between accounts and opportunities, and assigns confidence scores to each match, cutting out redundant entries and giving reps a single, reliable view of what’s actually happening on an account. Time Travel for historical context. Nektar identifies past interactions, contacts, emails, meetings, tied to a given domain and links them into newly created opportunities, even ones that predate the opportunity’s own creation. This retroactive correction gives reps and managers real historical context on a live deal instead of a record that only starts the day someone remembered to create it. Effortless reporting. High-quality reporting depends on clean data underneath it. Nektar makes this straightforward by automatically syncing contacts, emails, and meetings directly into standard Salesforce objects, so a report reflects what actually happened rather than what got manually logged. Self-healing records. Nektar continuously learns and adjusts, updating CRM records as new information arrives and incorporating manual changes users make along the way, so the data stays accurate on an ongoing basis rather than degrading again right after a cleanup project ends. Smart contact creation. New contacts get created automatically and matched to existing accounts by domain, removing a repetitive manual task and keeping account records properly connected instead of fragmented across near-duplicate entries. Parm UppalCRO, Chainguard Nektar was the first investment I made in my new role because we needed telemetry we could trust. Unlike traditional data cleansing, which requires manual work or a separate third-party tool layered on top of a CRM, Nektar is an AI-powered solution that integrates directly with Salesforce and handles most of this automatically. It keeps learning and adjusting, so data stays clean and accurate on an ongoing basis, freeing reps from the grind of manual data entry so they can focus on actually closing deals. 2. OpenRefine OpenRefine (formerly Google Refine) is a well-established open-source tool for cleaning and transforming messy data. It maintains data in a consistent format, sorts it according to your own rules, imports from web sources, and applies clustering algorithms to solve genuinely complex data-cleaning problems. Where it stands out: Free and open source. Costs nothing to install and can be extensively customized. Broad functionality. Handles a wide range of transformation, cleansing, and parsing tasks across diverse data sources. A relational approach, stronger than a simple flat spreadsheet for handling connected data. Local, on-machine security, rather than uploading sensitive records to a cloud platform. The tradeoff: OpenRefine’s interface is genuinely trickier than most commercial tools on this list, and it takes real technical comfort to use it well. 3. Informatica Data Quality Informatica Data Quality is a large-scale, cloud-based data cleansing

Top Sales Enablement Software
Sales

Top 10 Sales Enablement Software for 2026

Top 10 Sales Enablement Software of 2026 Sales 12 min Updated: July 30, 2026 Your sales reps have a mile-long to-do list. Generate leads, manage the pipeline, attend customer meetings, follow up on everything else, and somewhere in there, actually sell. It’s overwhelming by design, and it’s one of the biggest blockers for sales teams today. Reps consistently report spending only a third of their time actually selling, the rest goes to admin, research, and internal coordination. Sales enablement software exists to fix that ratio: giving reps the tools, content, and resources they need to spend more time selling and less time on everything around it. Get our latest insights into your inbox Top 10 Sales Enablement Software for 2026 HubSpot Sales Hub, unified sales platform with a broad AI toolset Showpad (now part of Bigtincan), content, coaching, and buyer engagement in one platform Spekit, AI-powered just-in-time enablement inside the flow of work Dock, an AI revenue enablement platform InsideSales, AI-driven sales engagement and buyer intelligence Allego, unified AI-driven learning, content, and coaching Spotio, field sales tracking and territory management SalesHood, independent onboarding, content, and coaching platform VanillaSoft, sales engagement with AI-driven lead prioritization Mindtickle, AI-powered sales readiness and live deal coaching What Is Sales Enablement? Sales enablement is no longer a nice-to-have, it’s a must-have for any organization that wants to stay competitive in today’s rapidly evolving business landscape. Forrester Research Sales enablement means giving sales teams the tools and resources they need to sell more effectively: product knowledge, training, customer insight, automation, and the content that helps a rep have a genuinely useful conversation instead of a generic one. The goal, ultimately, is more revenue through reps who are better equipped to have that conversation. Overview of the 10 Best Sales Enablement Platforms 1. HubSpot Sales Hub HubSpot Sales Hub is a unified sales platform known for a broad, genuinely useful AI toolset spanning outreach drafting, prospect research, report generation, and deal evaluation. It also includes CPQ management, conversation intelligence, document tracking, and sales playbook storage, all inside one straightforward interface. Key features: AI-powered lead and deal scoring, automatic next-step suggestions for guided selling, sales forecasting and customizable dashboards, omnichannel communication, automatic CRM data enrichment, AI prospecting agent, email tracking and scheduling. Best for: HubSpot-native teams wanting a single platform covering CRM, enablement, and AI-assisted selling without adding several point tools. 2. Showpad (Now Part of Bigtincan) Showpad was acquired by Vector Capital and merged with Bigtincan in October 2025; the combined company continues to sell and support Showpad’s platform, so evaluating it today means evaluating a business mid-integration with its former competitor. The underlying product still combines content management, buyer engagement tracking, and coaching in one enablement platform, with AI-guided content recommendations that surface the right asset based on deal stage and buyer context, plus a Shared Spaces feature giving buyers a persistent, trackable hub for shared content across a deal. Worth asking directly about post-merger product roadmap stability before committing to a multi-year contract, a pattern that shows up across most of this category’s recent consolidation. Key features: AI-guided content recommendations, buyer-facing Shared Spaces with engagement analytics, structured coaching and call review tools, content performance analytics tied to deal outcomes. Best for: Enterprise sales teams needing content management, buyer-facing engagement tracking, and coaching bundled into one platform at scale, with the caveat that the product is currently integrating with its former competitor’s stack. 3. Spekit Spekit is a modern, AI-powered sales enablement platform built around delivering enablement in the flow of work rather than a separate tool reps have to context-switch into. Its AI Sidekick uses contextual signals from Salesforce, email, and call tools to anticipate what a rep needs next, surfacing the right playbook, case study, or talking point at the moment it’s actually useful. Reps can create and share content instantly through Deal Rooms, track buyer engagement in real time, and see how each asset actually influences pipeline through Revenue Insights. Key features: AI Sidekick for contextual, just-in-time coaching and recommendations; intelligent content management that flags outdated material automatically; Deal Rooms with real-time buyer engagement data; Revenue Insights tying content usage to deal outcomes; a governance dashboard for content accuracy oversight. Best for: Teams wanting the fastest implementation in this category and genuinely high adoption, since Spekit’s enablement surfaces inside existing workflows rather than requiring reps to learn a new interface. 4. Dock Dock is an AI revenue enablement platform that combines digital sales rooms, customer onboarding hubs, centralized content management, and learning tools for go-to-market teams. It gives reps the content, training, and buyer collaboration usually split across separate tools. Dock covers the full customer lifecycle, from first pitch through onboarding and renewal, and keeps enablement content and playbooks current. Its AI drafts sales content and answers rep and buyer questions from the company’s own material and deal data. Key features: Branded digital sales rooms with buyer engagement tracking Customer onboarding hubs with shared plans and tasks Centralized content library for decks, docs, and links Learning playbooks and courses for ramping reps AI agents that answer using your content and deal data Native order forms and e-signature Salesforce and HubSpot integrations 5. InsideSales InsideSales uses AI and machine learning trained on a large base of aggregated buyer-behavior data to help reps figure out who to call, when, and in what sequence. Playbooks™ turns that into structured, repeatable outreach cadences, guiding a rep through a defined sequence of calls, emails, and follow-ups rather than leaving prospecting to individual instinct.  The platform’s Prioritization and Scoring tools rank leads and accounts by actual likelihood to convert, based on historical buyer patterns rather than a static lead-source rule, and its Buyer Intelligence layer surfaces signals (job changes, engagement patterns, account activity) that flag when a previously cold account is worth another look. Reporting and Scorecards give managers visibility into which reps and which cadences are actually driving pipeline, rather than just activity volume. Key features: Playbooks™ for structured, AI-guided outreach cadences; predictive lead and account scoring

Product

Nektar.ai v/s Clari v/s Gong v/s People.ai v/s EAC

Nektar vs. Clari vs. Gong vs. Backstory (People.ai) vs. EAC Product 15 min Updated: July 29, 2026 Automated data capture stopped being a nice-to-have for revenue teams years ago. In 2026, it’s become something closer to infrastructure: the layer that determines whether an AI agent acting on your CRM is working from a complete picture or a partial one. This comparison covers five platforms that all promise some version of automated capture, AI-driven insight, and cleaner CRM data, Nektar, Clari, Gong, Backstory (formerly People.ai), and Salesforce’s own Einstein Activity Capture, and where each one actually delivers versus where the marketing outruns the product. Two of these five have changed meaningfully since this comparison was last written, and getting that history right matters if you’re evaluating them today: People.ai rebranded to Backstory in April 2026, and Clari merged with Salesloft in December 2025. Both are covered accurately below, not under their old, standalone identities. Get our latest insights into your inbox Nektar Nektar was founded in 2020 with a vision to help GTM teams close revenue leaks with a purpose-built AI data foundation that unifies accurate, clean, timely revenue data automatically, at scale. That thesis has sharpened since: Nektar’s current positioning is making Salesforce data trustworthy enough for both reps and AI agents to act on directly, not just clean enough for a human to read. Claim to Fame Nektar’s Data Foundation automatically syncs contacts, emails, and calendar meetings from sales communication into Salesforce, for ongoing activity and historical GTM activity alike, with zero rep effort required.  Time Travel goes further, retroactively correcting historical records as new context arrives, something none of the other four platforms in this comparison do. Daisy AI sits on top, surfacing 39 signals across deal risk, buyer engagement, and rep performance directly on the Salesforce Opportunity tab. It supports every customer-facing team, business development, sales, customer success, and account management, which is why revenue operations leaders specifically choose Nektar for a genuine 360-degree view of the customer, not just a sales-only slice of it. Pros: Captures historical and ongoing contacts and GTM activity to deliver genuinely complete CRM data, not a partial sync Automatically presents the buying committee in every deal by enriching contacts with job titles and buyer roles (influencer, decision maker, economic buyer) Automatically links captured contacts to relevant open opportunities as Opportunity Contact Roles, not just Account-level records Classifies activity by sales or CS process automatically, surfacing exactly how sellers and CSMs are actually spending their time Captures calendar events including recurring events and any updates made to them (participant or schedule changes) Always-on reporting delivered directly to Slack, email, or MS Teams, the power of a dashboard without requiring anyone to open one Time Travel™ continuously maintains CRM data, updating and correcting records as new context arrives rather than leaving them static Works for every customer-facing team, not sales alone, including partnership, channel, and alliance teams Vendor-neutral by design, sits alongside Gong, Clari, or Salesloft rather than replacing them Cons: Best suited for companies with 10+ sellers; smaller teams may not need the full depth of the platform As a more specialized data-foundation layer rather than an all-in-one suite, teams wanting conversation intelligence or forecasting UI natively often pair Nektar with a complementary tool rather than expecting Nektar to do everything Clari (Now Clari + Salesloft) Founded in 2012, Clari built its reputation on AI-driven forecasting and predictive analytics for sales teams. In December 2025, Clari merged with Salesloft, forming a combined revenue-orchestration company under CEO Steve Cox, adding Salesloft’s sales-engagement layer (and Clari’s earlier Groove acquisition) to Clari’s forecasting core. If you’re evaluating “Clari” today, you’re really evaluating this combined platform, not the standalone forecasting tool it used to be. Claim to Fame Clari is still best known and most appreciated for its forecasting capabilities specifically, funnel views, pipeline inspection, and forecast rollups that sales leadership teams lean on heavily. Post-merger, it’s positioning itself as a full revenue-orchestration platform spanning forecasting, engagement, and conversation intelligence in one place, which is a meaningfully bigger promise than the pre-merger product made. Pros: Clean visuals and UI, widely regarded as one of the more polished interfaces in this category Customizable dashboards and a genuinely useful “funnel view” of the pipeline Strong visibility into current and projected pipeline for forecast rollups The Salesloft merger adds sales engagement and cadence automation natively, reducing the need for a separate tool for that function Conversation intelligence (via the earlier Chorus.ai and Wingman lineage) now bundled in, easier to consolidate vendors if you want one platform for forecasting, engagement, and calls Cons: Several contacts still aren’t reliably captured in Salesforce; contact-level completeness has been a longstanding weak point independent of the merger Syncing activity into Salesforce Opportunities isn’t always accurate Salesforce sync issues persist as a recurring theme in user feedback User adoption remains a real risk, and requires ongoing enablement investment to sustain The merger itself introduces real integration risk in the near term; analysts including Forrester have flagged genuine product overlap between the combined pieces that’s still being worked through as of mid-2026, worth a direct conversation with the vendor about current integration maturity before committing Read Detailed Comparison Comparing Nektar & Clari? Gong Founded in 2015, Gong built the conversation-intelligence category, using AI to analyze customer calls and meetings and surface coaching and deal-risk insight from what was actually said. It remains a large, well-resourced, independent company (no merger or rebrand to report here) and has continued investing in AI-native forecasting and coaching since this comparison was last written. Claim to Fame Gong is still best known for helping sales leaders coach reps and ramp new hires faster through conversation intelligence, accuracy and depth of insight from actual call and meeting content remain genuinely best-in-class. What started as a sales-team tool has expanded into real usage among customer success and SDR/BDR teams as well, given its focus on conversation-based engagement broadly. Pros: Ramps new sellers faster and coaches existing reps more specifically, grounded in what

Salesforce

What is Salesforce Duplicate Management?

What is Salesforce Duplicate Management? Salesforce 10 min Updated: July 29, 2026 Duplicate data in Salesforce fills the CRM with untrustworthy data, and once trust in the data goes, so does trust in every insight drawn from it. Duplication in Salesforce happens when the same real-world contact, lead, or account gets entered into the system more than once, sometimes as an exact copy, more often as a near-miss a matching rule doesn’t catch: “Ariana Grande,” “A. Grande,” and “Ari Grande” all describing the same person, none of them flagged as duplicates of each other by a simple exact-match rule. The scale of the problem is well documented. CRM duplication rates commonly reach 20% or more, and 70% of organizations report struggling with duplicate or inconsistent data due to the lack of a proper matching technology.  New records introduced through integrations are especially prone to it. Research puts the share of incoming integration data that already exists in some form in the CRM at 30% to 40%.  94% of organizations also suspect their own customer data is inaccurate, with duplicates as a primary contributor. Get our latest insights into your inbox How Duplicate Data Is Killing Your Salesforce Effectiveness 1. poor Customer experience Receiving the same email twice, or having to repeat an issue to support because two reps are looking at two different records of the same person, is exactly the friction Salesforce is supposed to eliminate. Duplicate records do the opposite, disrupting the seamless experience the platform is built to enable. 2. Wasted sales opportunities If a rep contacts a lead another rep is already engaging, because the two of them are working from separate duplicate records, that’s wasted time, real frustration, and a worse experience for the prospect. At scale, excessive duplicates also erode reps’ trust in the CRM itself: some start over-verifying every contact manually before reaching out, others stop checking background data altogether, both are worse than the CRM actually being reliable. 3. Unnecessary costs Physical marketing materials sent twice to the same duplicated contact are wasted spend, plain and simple. Less obviously, some software licenses are priced per record, so every duplicate is a small, ongoing cost multiplied across the whole database. One estimate puts the cost to properly identify, review, and merge a single duplicate at around $96, which adds up fast at any real scale, a company with 50,000 contacts and a 10% duplication rate is looking at roughly $480,000 in cleanup cost sitting in its own database. 4. Inflated forecasts Forecasting depends on an accurate count of prospects moving through the funnel. When two reps each log the same opportunity as separate records, that opportunity gets counted twice, quietly inflating the forecast past what’s actually achievable. Duplicate customer records also make it harder to get a clean view of a specific customer’s real history and preferences, which leads to misinformed strategy on that account and, eventually, a missed opportunity that looked fine on paper. 5. Bad decision-making Good decisions start from a unified view of the customer, one record pulling together click data, transactional history, and contact information into a single picture. Duplicates break that unification, preventing a comprehensive view of any specific customer and making aggregated analysis across the broader customer base unreliable as well. How Automation Can Resolve Salesforce Duplicate Management 1. Automated data entry instead of manual work Manual data entry is where most duplicates originate in the first place, 92% of duplicate records are created during initial registration or data entry, when a rep or system creates a new record rather than searching for an existing one. Automating data entry, pulling from source systems directly rather than typing records in by hand, removes that root cause rather than cleaning up after it. Automation can also check for duplicates in real time as records are created, merging or flagging them immediately instead of letting them accumulate for a future cleanup project. 2. Audit before importing data Auditing incoming data against what’s already in Salesforce, before the import happens, catches potential duplicates before they ever enter the system. This means cross-referencing new records against existing ones and merging or discarding matches proactively, rather than importing first and cleaning up after. 3. Implement validation rules to enforce data standards Validation rules enforce data standards at the point of entry, blocking a new contact record from being created with an email address that already exists, for instance, rather than allowing the duplicate in and catching it later. This keeps data accurate and consistent by design rather than by cleanup. 4. Proper validation on all CRM-connected forms The same logic needs to extend to every form feeding Salesforce, not just direct CRM entry. A web form requiring a unique email address, for instance, should reject a submission that already matches an existing record rather than silently creating a duplicate lead. 5. Invest in a Salesforce deduplication solution A dedicated deduplication solution uses matching logic well beyond simple exact-match rules to catch the kind of near-duplicates (“A. Grande” vs. “Ari Grande”) that manual review and basic validation rules both miss, merging or flagging them automatically rather than requiring a person to manually reconcile every case. This is where the real time savings show up: automating the ongoing detection and resolution of duplicates, rather than treating deduplication as a periodic cleanup project that leaves the database dirty for most of the year in between. Salesforce Duplicate Management With Nektar Nektar’s role here is upstream of most deduplication tools: it automatically captures contact and activity data directly from email, calendar, and meetings, and writes it into Salesforce structured against the right account and opportunity from the start, which prevents a large share of duplicates from ever being created in the first place, rather than cleaning them up after manual entry has already introduced them. Time Travel™ goes a step further, retroactively correcting historical records as new context arrives, closing gaps a one-time deduplication pass can’t touch. Matt BakerHead of Revenue Systems & Strategy, LaunchDarkly We

Sales

Multithreading in Sales: The Modern Secret to Winning More Deals

Multithreading in Sales: The Modern Secret to Winning More Deals Sales 12 min Updated: July 28, 2026 B2B buying is no longer a solo act. According to a Forrester survey, 94% of respondents sold to a group of three or more individuals, and 38% sold to groups of 10 or more buyers. Gartner puts the current average at 6 to 10 stakeholders, with enterprise deals frequently reaching 17 or more.  The decision to invest in a product is not one person’s job. It takes multiple rounds of discussion with stakeholders across departments, and as much as 82% of decisions are made by a buying group rather than an individual. Take technology sellers as an example: those outside IT now influence 63% of technology purchase decisions, pulling finance, legal, and compliance into a process that used to sit almost entirely inside one department. Digital transformation put buyers at the center of the process, and the buying committee got bigger and more diverse as a result. For a successful sale, you need the C-suite, marketing, and other stakeholders on board, and all of them value different things. What does it take to achieve consensus across a group like that? Multithreading Get our latest insights into your inbox What Is Multithreading in Sales? Multithreading is when a sales rep builds relationships with multiple stakeholders in a buying committee, rather than relying on a single point of contact. It directly increases the odds of closing a deal even if the champion leaves the buying organization mid-cycle. Consider the scenario: a rep has spent months building trust with a champion, the person responsible for implementing the solution internally. Then that champion quits for a new opportunity. What happens next? The rep may reach out to a replacement, but nurturing that new relationship takes weeks or months, and deal momentum slows. In the worst case, there’s no identified replacement at all, and the buying committee moves on to another vendor while the rep is still figuring out who to talk to. Multithread the whole committee instead, and a single departure doesn’t take the deal down with it. The Risks of Single Threading Despite multithreading’s effectiveness, most reps still default to single threading, one seller connecting with one buyer. A LinkedIn study found 78% of sales reps are single-threaded. It looks simpler on the surface, and can build a strong relationship with that one contact, but it overly relies on a single individual on each side of the deal. The moment either one leaves, the whole process is disrupted, and rapport has to rebuild from scratch, which drags out sales cycles, raises churn, and lowers win rates. The numbers back this up directly: roughly 25% of buyers change jobs every year, and 80% of sellers admit at least one deal was lost or delayed because a prospect or key stakeholder changed jobs mid-cycle. At that frequency, the revenue lost to single-threading compounds fast. Deploy Multithreading in Sales 1. Get visibility into your buying committee A buying committee is made up of individuals who wield influence across multiple departments, roles, and personas, and understanding who they are and what they each care about is the whole game. The problem is that most sales reps talk to far more people than ever make it into the CRM. Only 2 to 4 contacts typically get logged per opportunity, which means sales managers have effectively no visibility into the real buying committee. This gap, sometimes called “contact blindness,” puts deals at real risk: reps miss the chance to build relationships with the right stakeholders, and managers lose the context they’d need to coach a rep on moving the deal forward. The first step is making sure you actually have visibility into the full buying committee on every deal, and manually entering contacts into a CRM isn’t a realistic way to get there at scale. Once you can see the buying committee, build an account map and connect each stakeholder’s pain points to your solution. Understand what they’re currently using, why they’d need something new, and how much influence they actually carry in the final decision. 2. Multithread early With an account map in hand, reach out to the key decision-makers directly. LinkedIn is a reliable starting point, but case studies of the buyer’s own company or their competitors are another good source, they often name specific stakeholders by title, which tells you who to look for in your target account. Executive sponsors are another underused path: bring up the account in a deal review and ask a leader to tap their own network for a warm introduction. Or ask your champion directly to introduce you to others on the committee. Once connected, run an initial discussion with a clear agenda to understand each stakeholder’s role and priorities before pitching anything. When you do present, avoid a one-size-fits-all pitch, show each department how the solution affects what they specifically care about. 3. Simplify the buying process 77% of buyers describe their purchase journey as challenging and complex, with multiple stakeholders bringing conflicting information and a confusing number of options to sort through. That complexity is actually an opening for multithreading done well, not a reason to sell harder. A buyer-first approach means: learning what the buyer’s actual problem is before defining it back to them, sharing high-quality information openly (buyers with better information are 26% more likely to purchase), aiming to solve the problem rather than just sell into it, showing real, measurable value rather than just describing it, and staying open to detractors on the committee instead of assuming universal enthusiasm. 4. Drive value Value means different things to different stakeholders on the same committee, optimized pricing, regulatory compliance, a specific pain point solved. Gartner’s research found buyers who got information from suppliers that genuinely helped advance their own buying process were 2.8 times more likely to experience an easier purchase and 3 times more likely to go for a bigger deal with less regret afterward. Value doesn’t stop at

RevOps

10 Revenue Operations KPIs You Must Measure

10 Revenue Operations KPIs You Must Measure RevOps 12 min Updated: July 28, 2026 Tracking the right RevOps KPIs has a real impact on revenue, and it plays a direct role in improving workflows and building a customer experience worth remembering. So how do you maximize RevOps KPIs for profitability, and which ones should you actually measure?  This guide dives into insights from our conversation with Cliff Simon, former Chief Revenue Officer at Carabiner Group, plus a newer perspective on how AI is changing what “measuring the right thing” even means. Get our latest insights into your inbox Revenue Operations KPIs and Their Role in Cross-Functional Alignment Before the KPIs themselves, the basics: what RevOps actually is. Cliff puts it simply: it’s about following the dollar’s value through the revenue funnel. He doesn’t mention sales explicitly. That’s because RevOps is a much larger process than sales operations. It doesn’t just cover the sales touchpoint, it tracks the entire customer journey. Alignment between teams is the driving force behind RevOps, and cross-functional misalignment remains one of the biggest pain points for SaaS businesses. That misalignment shows up as poor communication between teams, which manifests as siloed data. Companies have plenty of data, it just sits in disconnected lakes with no bridges between them, which means organizations can’t meaningfully use the insight buried in it. Achieving alignment is the first step. Maintaining it as you scale is the ongoing work. And the way you track both is with revenue operations KPIs. Why Should You Measure Revenue Operations KPIs? Companies only improve when they know exactly where they’re going wrong. RevOps KPIs track customer progress and team performance across the entire buyer journey, spanning marketing, sales, customer success, product, finance, and beyond. These KPIs measure the progress of shared workflows against actual customer needs, and performance at each touchpoint. Go granular enough with them and you can improve efficiency, remove friction, and maximize revenue for growth. From an overarching perspective, revenue operations KPIs are the strategic guide to hitting business goals through revenue operations. 10 Essential Revenue Operations KPIs You Must Measure 1. Revenue The obvious one, and still the most critical. Revenue is what your business generates, and measuring it tells you whether your revenue stream is consistent over time, what the ups and downs look like, how to adapt pricing, and where you stand against business goals. Recurring revenue specifically, subscriptions, membership fees, license fees, is best tracked as Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR). ARR is the annual figure used for bigger business goals, growth measurement, and sales forecasting, calculated one of two ways: Annual recurring Revenue (ARR) = Total Revenue from New Subscriptions + Recurring Revenue from Existing Subscriptions – Churn + Net Expansion OR Annual recurring Revenue (ARR) = MRR x 12 (months) Monthly Recurring Review (MRR) = Number of Active Customers x Average Billed Amount 2. Sales Pipeline Velocity Pipeline velocity measures how long a customer takes to move through the pipeline from lead to conversion, stated in revenue terms rather than time. A typical B2B sales cycle can run as long as a year, and velocity tells you whether your reps are converting efficiently or whether your workflow needs a rethink. Higher velocity means an organized, structured sales process with frictionless handoffs, MQLs becoming SQLs becoming closed-won opportunities smoothly. Lower velocity means bottlenecks somewhere in the funnel that need to be found and removed. Sales Pipeline Velocity = (Number of SQLs x Average Deal Size x Win Rate) / Length of Sales Cycle 3. Customer Acquisition Cost (CAC) CAC is what you spend to acquire a new customer over a given period, advertising, sales hiring, commission, rep coaching, overhead, all of it. CAC measures ROI on that spend and reflects both marketing and sales effectiveness. A high CAC relative to what a customer’s actually worth is a signal to revisit campaigns, messaging, or targeting, without cutting into the quality of buyer-seller interactions. Customer Acquisition Cost (CAC) = (Sales + Marketing Costs) / Number of New Customers Acquired 4. Conversion Rate Conversion rate (also called win rate, or “opportunities to close ratio” in SaaS) is the share of opportunities that actually become closed deals. A low conversion rate tells you something’s off in the revenue process, but the real value is in what it prompts you to ask next: does marketing need to deliver higher-intent MQLs? Is the team over-indexed on lead volume instead of quality? Do reps need more coaching on multithreading? Going granular (tracking conversion at each specific funnel stage rather than just start to finish) usually surfaces exactly where the problem sits. Conversion Rate = Number of Closed Deals / Number of Potential Deals 5. Average Contract Value (ACV) ACV measures the total revenue earned from a contract over a given period, usually a year, and is best read alongside CAC, ARR, and total contract value (TCV) rather than alone. ACV shows potential revenue from a contract; CAC shows what it cost to close it. Compared together, they tell you how long it takes to become profitable on that specific deal. ACV is also a useful leading indicator for rep development (which reps are ready for higher-ACV accounts) and for spotting upsell and retention opportunities on existing contracts. Annual Contract Value (ACV) = Total Contract Value / Total Years of Contract 6. Revenue Retention Sustainable growth depends on retaining existing customers, not just acquiring new ones. Revenue retention KPIs are the clearest signal of how satisfied customers actually are. Two matter most: Gross Revenue Retention (GRR), the percentage of recurring revenue retained each month after cancellations and downgrades (excluding expansion) and Net Revenue Retention (NRR), which measures your ability to retain and expand revenue. Gross Revenue Retention (GRR) = (Starting MRR – Churned MRR – Contractions) Starting MRR x100 Net Revenue Retention (NRR) = (Starting MRR + Expansion – Churned MRR – Contractions) Starting MRR x100 7. Customer Churn Churn is the customers who stop paying within a given period,

Top 15 Revenue Optimization Tools
RevOps

Top 15 Revenue Optimization Tools to Consider in 2026

Top 15 Revenue Optimization Tools to Consider in 2026 RevOps 10 min Updated: July 28, 2026 You’re set up for the quarter, a dedicated sales team, a real marketing plan, regular customer feedback. And yet revenue growth has quietly hit a ceiling, and it isn’t obvious why. The causes can hide for months or years, draining potential the whole time. Stagnant growth is a real challenge, and diagnosing it can be harder than fixing it once you know what’s actually wrong. This guide covers what’s usually behind it and the tools that help address it. Get our latest insights into your inbox What Is Revenue Optimization? Revenue optimization is maximizing revenue growth over the long term by managing pricing, user acquisition and retention, and sales and support enablement, using data analytics, behavior analysis, and predictive modeling to find where the opportunity actually is. Like a building needs a solid foundation before anything else, revenue optimization needs accurate data and real market analysis before any specific strategy gets built on top of it. The 4 Pillars of Revenue Optimization Pricing. Setting the right price points through market data and testing, freemium, usage-based, or tiered models depending on competition and customer demand, rather than defaulting to “charge more.” HubSpot’s 2018 shift to a modular, lower-priced Starter tier is a well-known example of pricing optimization expanding a market rather than just extracting more from it. Product development. Using customer feedback and usage data to improve features and reduce churn, rather than shipping based on internal assumptions. Spotify’s data-driven personalization (Your Daily Drive being a well-known example) shows what this looks like when it’s genuinely data-led. Sales and support. Equipping sales and support teams with the resources to close deals and retain customers, training, CRM systems, and increasingly AI-assisted tooling that automates routine tasks so teams can focus on complex, high-value interactions. Marketing. Building brand awareness and converting prospects through the right mix of paid acquisition and owned channels like content and SEO. Dropbox’s referral-driven, low-CAC growth remains a commonly cited example of marketing-led revenue optimization done well. The Revenue Optimization Process Collect and analyze data. Everything downstream depends on this step. Gartner’s widely cited estimate puts the cost of poor data quality at $12.9 million per organization annually, a figure that keeps recurring across this whole audit because it keeps being the actual root cause. There’s a real chance your own data is more incomplete than you’d assume. Segment your customers. Once data is reliable, segment by demographics, behavior, and purchase history to identify the most profitable groups and tailor pricing and product to them specifically. Forecast your revenue. Use historical data and market trends to project forward. Organizations using statistical forecasting models consistently outperform those relying on gut-feel projections. Optimize your processes. Apply what segmentation and forecasting reveal to actual pricing, product, and marketing decisions. McKinsey research has found optimization strategies driving average revenue increases of 2 to 7%, with some cases reaching 15%. Monitor and adjust. Revenue optimization isn’t a one-time project. Markets and customer needs shift, and strategies need continuous evaluation to stay aligned with them. Top 15 Revenue Optimization Tools for 2026 1. Nektar Nektar is the data foundation revenue optimization actually depends on, not another point tool competing with the ones below it. Every step in the process above (segmentation, forecasting, process optimization) is only as good as the underlying data feeding it, and most CRMs are missing a large share of the real customer interaction data that would make that process accurate. Data Foundation automatically captures every email, meeting, call, and calendar event across a team and writes it natively into Salesforce, HubSpot, or Dynamics, with zero rep effort required. Time Travel™ retroactively corrects historical records as new context arrives. Daisy AI then surfaces the signals that actually drive revenue optimization decisions: buyer engagement scoring, deal risk flags, and buying-group coverage, directly on the Salesforce Opportunity tab. Key features: zero-rep-effort activity capture, Time Travel™ retroactive correction, Daisy AI signal library across deal risk and buyer engagement, vendor-neutral integration with your existing CRM and sales stack. 2. Gainsight Gainsight offers Customer Success, Product Experience, and community-building tools under one umbrella, tracking interactions, monitoring engagement, and predicting behavior to manage and grow customer relationships at scale. Key features: AI-powered insights for customer success and revenue growth, health score monitoring, journey mapping and touchpoint analysis, personalized recommendations for sales and CS teams. 3. Planhat Planhat is a customer data platform for managing and growing customer relationships, tracking interactions, monitoring health and engagement, and automating workflows around personalized customer experiences. Key features: real-time behavior tracking and segmentation, CRM integrations, health score monitoring, customer journey mapping. 4. ClientSuccess ClientSuccess is a focused customer success platform built to be adopted quickly without heavy overhead, pairing with dedicated onboarding (Baton) and product feedback (Product Signals) tools designed to work together from the start. Key features: real-time engagement tracking, health score monitoring, next-best-action recommendations, collaboration and task management for CS teams. 5. Ambition Ambition is a sales coaching and performance platform giving managers real-time visibility into sales metrics, with gamification (leaderboards, contests) to drive engagement toward revenue goals. Key features: real-time performance tracking and gamification, CRM integrations, customizable KPI dashboards, automated coaching and feedback. 6. Vitally Vitally combines customer data with project management in one collaborative workspace, tracking customer health, automating workflows, and building personalized customer experiences. Key features: customer health insights, automated health scoring and alerting, customizable segmentation, proactive churn-prevention workflows. 7. Totango (including Catalyst) Totango and Catalyst merged in February 2024, and the combined company now operates under the Totango brand across three product lines: Totango (enterprise customer success, strong in hierarchy-based health scoring), Catalyst (customer growth platform, known for ROI-based scoring), and Unison (an AI churn-intelligence engine). If you’re evaluating either name individually, know upfront you’re now looking at the same parent company, with some reported integration disruption as the merged products come together, worth a direct conversation with the vendor about current product maturity before committing. Key features: enterprise-hierarchy and

Salesforce

A Guide to Salesforce Opportunity Management

A Guide to Salesforce Opportunity Management Salesforce 11 min Updated: July 27, 2026 Sales opportunity management is the discipline of prioritizing and nurturing the deals most likely to close. In Salesforce specifically, that discipline runs through one object: the Opportunity, and how well your team actually uses its fields, stages, and automation determines whether Salesforce reflects your real pipeline or just a rough approximation of it. This guide covers what a Salesforce Opportunity actually is, how the object and its fields work, current best practices, and where Salesforce’s own AI (Agentforce) is changing what “managing” an opportunity means in 2026. Get our latest insights into your inbox What Is a Salesforce Opportunity? An Opportunity in Salesforce is the record representing a potential deal, a prospect with genuine interest, a defined amount, and a path toward closing. It’s built around a specific set of fields that matter more than people often realize when they’re filled in accurately:  Stage (where the deal sits in your sales process) Amount (deal size) Close Date (expected close) Probability (likelihood of winning, often tied to stage), and  Forecast Category (how the deal rolls up into forecast reporting: Pipeline, Best Case, Commit, or Closed). Salesforce’s Lightning interface also gives reps a Kanban board view of opportunities by stage, and a Path component that visually walks a rep through the required steps at each stage, both built specifically to make stage progression visible rather than buried in a list view. Read the Blog Discover the Top Sales Intelligence Tools for 2026 How Do Opportunities Differ From Leads in Salesforce? Leads and Opportunities are different objects in Salesforce, representing different stages of qualification. A Lead is unqualified: contact information and an initial expression of interest, with no confirmed budget, authority, or fit. Lead Conversion in Salesforce is the formal process of turning a qualified Lead into an Account, a Contact, and an Opportunity simultaneously, the point where a prospect moves from “might be a fit” to “we’re actively pursuing this deal.” Getting Opportunity data right depends entirely on getting this conversion step right. A Lead converted with incomplete or rushed qualification produces an Opportunity that looks real in the pipeline but isn’t backed by an actual budget or timeline, which is exactly the kind of gap that inflates pipeline coverage without inflating real forecast accuracy. Why Opportunity Management Matters Resource optimization. Prioritizing opportunities by actual potential, not just recency, means reps spend time on the deals most likely to close instead of splitting effort evenly across a list that includes plenty of long shots. Sales productivity. Reps who understand a prospect’s real needs and buying intent, visible through Salesforce’s stage history and activity timeline, can tailor their approach instead of running the same script on every deal. Revenue growth. Consistently focusing on high-potential opportunities is what turns a busy pipeline into predictable, closed revenue. Forecast accuracy. Salesforce’s forecast rollups are only as accurate as the Stage, Amount, and Forecast Category fields feeding them. Get those fields wrong at scale, and the forecast dashboard becomes a confident-looking number built on a shaky foundation. Cross-functional visibility. Opportunity records are usually the shared point of reference between sales, marketing, and customer success, which only works if the record actually reflects what’s happening in the deal. Read the Blog Discover the 10 ways to improve Sales Efficiency Managing Opportunities in Salesforce: The Core Steps Qualify before creating the Opportunity. Confirm budget, timeline, decision-making authority, and fit before converting a Lead, not after. An Opportunity created from a poorly qualified Lead just moves the qualification problem downstream. Keep Stage and Forecast Category in sync with reality. A Stage field should reflect what actually happened in the last real conversation, not what a rep hopes happens next. This is the single highest-leverage habit for keeping Salesforce’s own forecast rollups trustworthy. Use Opportunity Contact Roles to map the buying committee. Salesforce lets you assign roles (Decision Maker, Influencer, Economic Buyer) to the Contacts tied to an Opportunity. Most teams under-use this field, leaving Opportunities single-threaded on paper even when the real deal involves several stakeholders. Build validation rules and required fields around your actual sales process, not a generic template. If Amount or Close Date can be left blank at a stage where they should be known, they usually will be. Automate what you can with Flow, closing tasks, stage-change notifications, approval routing, rather than relying on reps to remember manual steps. Review the pipeline on a real cadence. A deal review that actually checks Stage-versus-activity consistency catches drift before it distorts the whole team’s forecast. Salesforce Opportunity Management in the Agentforce Era This is the part that’s changed most since this guide was last substantially updated. Salesforce’s own AI layer, Agentforce, and its broader Headless 360 initiative are built to let AI agents read and act on Opportunity data directly, updating fields, flagging risk, and triggering next steps without a person reviewing every change first. That raises the stakes on exactly the fields covered above. A Stage field that’s wrong used to just mislead a manager reading a pipeline report. Fed into an agent acting on it directly, the same wrong field can trigger an incorrect automated action, a premature “commit” signal, a misrouted approval, before anyone catches it. Gartner projects that 60% of AI projects will be abandoned through 2026 specifically because the underlying data wasn’t ready for AI to use, and Opportunity data is usually the first place that gap shows up in a sales org. This is exactly why Opportunity data hygiene, that same Stage-and-Forecast-Category discipline sales teams have been told to maintain for years, has moved from a forecasting nicety to a genuine precondition for using AI safely inside Salesforce at all. Where Nektar Fits Into Salesforce Opportunity Management Nektar doesn’t replace the Opportunity object or Salesforce’s own forecast tooling. It makes sure the data feeding both is actually complete. Revenue Telemetry automatically captures every email, meeting, call, and calendar event and writes it natively into Salesforce, structured against the

Top 5 Trends That Will Impact Sales Operations
Sales

Top 5 Trends That Will Impact Sales Operations in 2026

Top 5 Trends That Will Impact Sales Operations in 2026 SalesOps 12 min Updated: July 23, 2026 Sales operations spent the last two years absorbing AI into an already-complex stack. 2026 is the year that absorption gets tested for real: Gartner’s research finds over 60% of sales teams now use generative AI, but only about one in three report genuine productivity gains from it. That gap, between adopting AI and actually getting value from it, is the thread running through every trend below. Gartner’s own framing for sales operations leaders heading into 2026 is blunt: the operating model that worked in the past is no longer relevant. Salesops has to reassess where it invests, moving away from generic tool rollouts toward sales transformation, real data analytics, and disciplined AI utilization. That’s a meaningfully different mandate than “add more tools” or “get buy-in for the tech stack,” which is roughly where this conversation sat a few years ago. Get our latest insights into your inbox What is Sales Operations (SalesOps)? Sales operations is the function responsible for removing friction from the sales process so reps can sell faster, more predictably, and with less manual overhead. It’s the team that builds and maintains the sales tech stack, designs process and territory structure, and increasingly, according to Gartner’s own research, supports five or more groups across the business while allocating roughly 68% of its time to nonclient-facing work. Sales ops has a direct line to revenue, and it’s become a more cross-functional, more analytically demanding role than the “keep the CRM running” job it used to be. The 5 Trends Shaping Sales Operations in 2026 1. AI Moves From Experimentation to Governed Execution The headline number for 2026 isn’t adoption. Adoption is already high. It’s the gap between adoption and results. With most sales teams now using generative AI in some form but only a third seeing real productivity gains, the sales ops mandate has shifted from “get AI deployed” to “figure out where it’s actually working and govern the rest.” Gartner names this directly as the top 2026 priority for chief sales officers: building a sales-centric AI portfolio roadmap tied to specific commercial outcomes, rather than a scattershot rollout of whatever tool looked impressive in a demo. What this looks like in practice: sales ops leaders auditing which AI use cases are tied to a measurable outcome (forecast accuracy, time saved per rep, faster deal-risk detection) and which ones are running on faith. The tools that survive that audit tend to be the ones grounded in real, complete data rather than inference, since an AI feature working from gaps produces confident, wrong output rather than a cautious one. 2. GTM Motions Get Rebuilt Around Buyer Preference, Not Rep Convenience The number of B2B buyers who’d prefer a rep-free purchase experience is at 67%, and buyers now weigh an average of seven information sources before a rep is meaningfully involved. But the same research found 69% of buyers still come back to a human rep specifically to validate AI-generated insights before finalizing a decision. The rep’s role hasn’t disappeared. It’s moved from primary information source to the point of confidence and validation at specific moments in the journey, and sales ops is the function responsible for redesigning the GTM motion around that shift rather than the old linear funnel. Marty OvermanEVP Americas Sales at Darktrace You don’t necessarily need a salesperson anymore. You need a sense maker. Someone who can help buyers make sense of all the data and information available to them. Practically, this means fewer generic top-of-funnel touchpoints and more investment in the specific moments that Gartner’s research shows still require a human: complex objection handling, contract negotiation, and validating a buyer’s own AI-assisted research. Sales ops teams that keep measuring rep activity the old way (call volume, email sequences) are optimizing for a motion buyers are actively moving away from. 3. Sales Ops Becomes the Data-Readiness Function for Agentic AI Clean data has been a Salesops priority for years. What’s different in 2026 is why it matters. MuleSoft’s 2026 Connectivity Benchmark found that half of enterprise AI agents currently operate in isolated silos, and Gartner projects 60% of AI projects will be abandoned through 2026 specifically because the underlying data wasn’t ready for AI to use. That reframes data quality from a hygiene task IT or RevOps handles in the background to a direct precondition for whether any of the AI investment in trend #1 actually pays off. The practical shift: Salesops teams auditing CRM completeness before, not after, rolling out an AI feature on top of it. A stage field that’s wrong, a stakeholder who was never logged as a contact, an activity that never made it into the opportunity record, these used to just produce a slightly-off forecast a manager could catch. Fed into an AI agent acting on that data directly, the same gaps produce a wrong output at machine speed, with nobody reviewing it first. 4. Buying-Group Intelligence Becomes a Formal Discipline, Not a Best Practice Multithreading has been “good advice” in sales for years. In 2026 it’s closer to a measurable operating requirement, because the buying committees it’s meant to cover have kept growing. Gartner puts the average B2B deal at 6 to 10 stakeholders, with enterprise deals frequently reaching 17 or more, most of whom never get added as a CRM contact unless something automatically catches them. Salesops teams are increasingly treating buying-committee coverage as a trackable metric, not just a coaching point: how many contacts are actually mapped per opportunity, how engaged each one is, and where coverage has gone stale. That requires detecting stakeholders from actual email and calendar activity rather than relying on a rep to remember to add them, which is a data-capture problem as much as a sales-process one. By far, Nektar has been the most impactful tool to understand our buyers and their influence. Almost immediately we got the visibility we needed for a long time​ Dan

10 B2B Sales Closing techniques
Sales

10 B2B Sales Closing Techniques for 2026

10 B2B Sales Closing Techniques for 2026 Sales 11 min Updated: July 21, 2026 “How hard can you push a client to close a deal?” It’s still the wrong question, and it’s gotten more wrong since we first wrote this guide. Gartner’s 2026 buyer research found that 67% of B2B buyers prefer a rep-free experience, and buyers now weigh an average of seven different information sources that includes AI tools. All this happens before a rep is meaningfully involved at all.  A tactic designed to manufacture agreement doesn’t land well on a buyer who’s already done most of the homework and has little patience for anything that feels like a script. That doesn’t mean closing techniques stopped mattering. It means what “closing well” looks like has changed. 69% of buyers still turn to sales reps specifically to validate AI-generated insights. The seller’s role has shifted from primary source of information to source of validation and confidence at the specific moments a buyer actually needs it. Buyers who combine self-directed research with the right rep interaction at the right moment are 1.8 times more likely to complete a high-quality deal than buyers who go fully independent. Marty OvermanEVP of Americas Sales, Darktrace You don’t necessarily need a salesperson anymore. You need a sense maker who can help buyers make sense of all the data and information available to them. This guide keeps the techniques that hold up under that shift and replaces the ones that don’t. Get our latest insights into your inbox Why Old-School Closing Tactics Backfire Buying committees have gotten bigger and more skeptical. Gartner puts the average B2B deal at 6 to 10 stakeholders, with enterprise deals reaching 17 or more. The average B2B win rates have fallen to roughly 20%, with sales cycles running 38% longer than in 2021. There are more people in the room, more independent research happening before you’re in it, and less tolerance for anything that feels like pressure rather than partnership. The techniques below are built around that reality: buyers who are already informed, skeptical of scripts, and looking for a rep who reduces their risk rather than one who’s trying to manufacture urgency. The 10 B2B Sales Closing Techniques 1. Lead with their goals, not your script For senior buyers, the decision is close to binary: your product either meets a specific goal or it doesn’t. Consultative selling i.e. diagnosing the real problem before proposing anything remains the technique most aligned with what buyers actually want; multiple 2026 studies cite a strong majority of B2B buyers wanting sales reps to act primarily as advisors rather than pitchers. To do this well: Look past the sales script and ICP data. Ask what the actual person in front of you is trying to accomplish this year. Use the language they use to describe the problem, not your own terminology. Ask specifically, and early: “What does success on this initiative actually look like for you?” 2. Don’t lead with a discount Asking about goals is also how you qualify a deal.  A buyer with a clear, time-bound initiative and no objective evaluation criteria yet is a very different conversation than one already comparing vendors on price. If a buyer pushes for a discount before you’ve established value, start from a position of value, not concession. And never offer a discount before it’s asked for.  Buyers in 2026 are broadly more cautious with spend than in prior years. A rep who leads with price signals that price is the only thing worth discussing. 3. Use competitor comparisons as an opening, not a threat Buyers increasingly already know a competitor’s weaknesses before they talk to you. Independent research (reviews, analyst coverage, peer communities) surfaces vendor gaps that used to only come out in a sales conversation. Assume that, rather than trying to extract it.  Ask directly: “On a scale of 1 to 10, how well is [current tool] actually working for you?” Listen for where the gap is, then ask what would need to be true for it to be a 10. Use their own words to describe the gap, and confirm understanding before moving on. The goal is accuracy, not a gotcha. 4. Lead with a mutual action plan, sized to the real buying committee A mutual action plan maps out who needs to do what to close the deal, with dates attached. This matters more now than it did a few years ago, since the buying committee it needs to account for has grown. A MAP built for a single buyer doesn’t hold up against a committee that size. Cover three things explicitly: the realistic timeframe to close, what it costs both sides (due diligence, procurement, compliance), and who’s actually involved on each side. Send a written summary after the conversation and ask them to confirm it. That alone tells you a lot about how seriously the deal is being treated internally. 5. Use “we,” not “you,” when the stakes are shared A small technique, but a real one: replacing “you” with “we” when describing a shared goal (“we’re both trying to hit this timeline”) does more to build genuine partnership than most rapport scripts. It only works if it’s true. Use it when you’re actually aligned on an outcome, not as a rhetorical trick layered on top of a pitch. 6. Run a premortem before you ask for the close Before pushing a deal to the next stage, assume it’s six months from now and it falls apart. Work backward from there. Why did it fail? Did the champion lose internal support? Did budget get reallocated in Q3? Did a new stakeholder join and froze the decision?  This technique, borrowed from research psychology and increasingly cited in latest sales research, surfaces risks of a straightforward “any concerns?” question often misses, because it forces specificity instead of a polite “no, we’re good.” 7. Make the close easy, but only once the signals say it’s earned An assumptive close (“Would you prefer to start

Best Revenue Intelligence Tools
RevOps

12 Best Revenue Intelligence Platforms for 2026

12 Best Revenue Intelligence Tools for 2026 RevOps 15 min Updated: July 21, 2026 Revenue intelligence uses AI to capture and analyze customer interaction data like emails, calls, and meetings across sales, marketing, and customer success. It turns raw activity into insight you can act on, throws light on which deals are actually healthy, which reps need coaching, and where the pipeline is quietly leaking. For most of this category’s history, revenue intelligence fed a human decision-maker who applied judgment before acting on what the data said. Increasingly, that same data now feeds AI agents that directly update CRM fields, flag risks, and trigger workflows with a lot less human judgment sitting between the insight and the action.  Gartner projects 40% of enterprise applications will include task-specific AI agents by the end of 2026. That raises what “good revenue intelligence” needs to mean: not just insight a person can use, but data clean enough for an agent to act on without making things worse. Get our latest insights into your inbox What Is Revenue Intelligence? Revenue intelligence is a data-backed, AI-driven approach to understanding and forecasting revenue. It pulls raw interaction data from across your revenue functions like sales, marketing, customer success, and turns it into insight: which deals are trending toward close, which are stalling, and what a rep should actually do next. How Revenue Intelligence Platforms Create Impact 1. It integrates siloed data Most organizations have sales, marketing, and customer success data trapped in separate systems. Revenue intelligence pulls it into a single, continuously updated source of truth rather than treating data cleanup as a one-time project. Rosalyn Santa ElenaFounder, The RevOps Collective I have seen a lot of companies try to clean up their data through third parties as a one-time event. But you can’t approach your data as a one-time action. It’s an ongoing and iterative process. 2. It closes the gap between what’s logged and what actually happened A large share of buyer-seller activity never makes it into the CRM at all. Meetings go unlogged, or nobody adds key stakeholders as a contact. Revenue intelligence automates that capture instead of relying on reps to remember. 3. It surfaces deal risk before it’s a lost deal Multithreading gaps, stalled engagement, missing buying-committee coverage are all visible in interaction data well before they show up as a lost opportunity in the pipeline report. 4. It improves rep coaching Instead of an interrogation-style deal review, revenue intelligence gives managers specific, data-backed coaching moments like this deal has gone quiet, or this rep hasn’t engaged the economic buyer, rather than generic advice. 5. It drives more predictable revenue As much as 80% of sales organizations miss the mark on revenue forecasting by 25% or more. The primary underlying reason is dirty data. Without an accurate forecast, your teams won’t have any direction for revenue strategies. Using revenue intelligence, you can create quality forecasts to help your team budget, strategize business growth, set long-term goals, and secure funding. Also, given their use of AI, your forecasts are void of bias resulting from less manual intervention. Asia CorbettSenior RevOps Manager, GTM, Bread Financial If you don’t have good data, you can’t forecast. If you can’t forecast, you can’t build a scalable and repeatable sales motion. You don’t know what your pipeline is going to be, or what money is going to come in. 12 Best Revenue Intelligence Tools for 2026 Nektar: GTM data foundation and AI signal layer Salesforce CRM Analytics: native Salesforce analytics and predictive insights HubSpot Sales Hub: CRM and sales engagement for HubSpot-native teams ZoomInfo Chorus: conversation intelligence backed by B2B data Xactly: revenue intelligence tied to incentive compensation Mediafly Intelligence360 (formerly InsightSquared): revenue analytics and forecasting Revenue.io (formerly ringDNA): sales engagement and conversation guidance Kluster: forecasting and pipeline process standardization Salesloft: sales engagement, now part of Clari + Salesloft Akoonu (RevWorks): native Salesforce forecasting and pipeline intelligence Cien: AI-driven sales performance analytics Aviso AI: agentic forecasting and revenue execution Overview of the 12 Best Revenue Intelligence Tools 1. Nektar Nektar is the GTM telemetry platform that automatically captures every customer interaction and delivers clean data to your CRM, data warehouse, and AI applications, all with zero manual entry or adoption friction. It makes Salesforce safe for AI execution. As more of your GTM stack, be it Agentforce, Clari, your own AI agents, starts acting on CRM data autonomously, the CRM has to be complete and correct, continuously, or every agent built on top of it inherits the error. Nektar flows this valuable data directly into core business systems like Salesforce, Snowflake, Claude and your entire stack, ensuring customer insights are accessible across all GTM teams & downstream AI initiatives. Pankaj GHead of GTM Systems, Nektar Nektar solved our biggest CRM data problem: incomplete and inconsistent activity data in Salesforce. Contacts were missing from opportunities, engagement history was spotty, and any report built on activity data was unreliable. Now activities flow into Salesforce automatically and land on the right accounts and opportunities, with contacts created and linked as opportunity contact roles without anyone touching a keyboard. Notable features: zero-rep-effort capture, buying-group intelligence, Time Travel™ retroactive correction, Daisy AI signal library (39 signals across 8 categories), vendor-neutral integration alongside your existing sales stack. Pricing: Custom, based on team size and scope. A free CRM scan will show how much of your own pipeline activity is currently missing. 2. Salesforce CRM Analytics Salesforce CRM Analytics (formerly Einstein Analytics, then Tableau CRM) remains Salesforce’s native analytics layer: predictive insights and next-best-action recommendations embedded directly in the flow of Salesforce work. It’s evolved to connect with Data Cloud and Tableau Next, positioning it as part of Salesforce’s broader agentic analytics push rather than a standalone BI tool. Notable features: predictive analytics natively embedded in Salesforce, Slack integration for surfaced insights, inherited Salesforce security and governance, connection to Data Cloud for agentic use cases. Pricing: Tiered by edition; the Revenue Intelligence-focused package has historically run around $200/user/month confirm current pricing directly with

Sales

Top 15 Guided Selling Tools for 2026

Top 15 Guided Selling Tools for 2026 Sales 12 min Updated: July 20, 2026 Imagine planning to build a new house without knowing where to start. You have a rough idea of what you want, but you’re not an architect. A good architect takes your needs, preferences, and budget, and guides you through the decisions, materials, layout, features, that turn a vague idea into an actual house. A guided selling tool does roughly the same job for a sales rep facing a complex deal: it uses customer data and sales expertise to help reps (and increasingly, buyers directly) navigate a purchasing decision, offering personalized recommendations, answering questions, and steering the process toward a decision that actually fits. Get our latest insights into your inbox What Is Guided Selling? Guided selling is a structured approach to leading potential customers through the sales process, using current and historical sales data alongside customer information to help reps make tailored product recommendations and increase the likelihood of conversion. Picture shopping for a laptop online. A guided selling flow asks what you’ll primarily use it for, whether you need a large screen or something lightweight, how much battery life matters, and what your budget is, then recommends a shortlist based on your answers, with detail on specs, reviews, and ratings to help you decide. Guided selling works especially well for complex or high-value purchases, where buyers need more support to feel confident in a decision. Done well, it also builds the kind of trust that leads to a longer relationship rather than a one-time transaction. How Guided Selling Works in B2B SaaS In B2B SaaS, guided selling usually runs through a sales rep or customer success manager rather than a self-serve website flow. It starts with the customer describing their goals, and the guide using that context to recommend relevant products or plans, often supported by a live demo, case studies, or other content that helps the buyer understand how the product actually solves their problem. The Guided Selling Process Gathering customer information. Collecting data on the customer’s needs, budget, and constraints. Identifying pain points. Using that information, usually through targeted questions or a needs assessment, to surface what’s actually blocking the buyer. Providing recommendations. Matching the buyer’s needs to the specific product, plan, or configuration that fits. Presenting solutions. Walking through features, benefits, and pricing in the context of the buyer’s stated problem. Handling objections. Addressing concerns directly rather than avoiding them. Closing the sale. Facilitating the actual purchase once the buyer is confident. Follow-up and support. Making sure the customer is set up for success after the sale, not just after the signature. What Guided Selling Looks Like in Practice Automated sales playbooks. Guided selling depends on a consistent methodology, so the playbook itself needs to be automated and easy to follow, not a document reps have to remember to consult. Needs-identification questionnaires. A short, structured set of questions steers a buyer (or a rep) toward the right recommendation faster than an open-ended conversation would. Real-time responsiveness. A guided selling tool has to react to what’s actually happening in a deal as it happens. Responding to a live signal within minutes rather than hours meaningfully increases the odds of a sale. Pipeline visibility. A good guided selling tool gives reps and managers a clear view into pipeline health, surfacing at-risk deals and letting managers compare how reps are actually spending their time, not just what they report spending it on. Integration with the rest of the stack. Teams typically buy 10 to 15 tools but only actively use 3 to 6 of them, which makes integration a real constraint on a guided selling tool’s actual usefulness. A tool connected only to the CRM and email, with no visibility into content usage or engagement analytics, leaves reps guessing which sales material actually works at each stage. The more complete the data feeding it, the better a guided selling tool performs, which is exactly why the underlying data problem matters as much as the guided selling layer sitting on top of it. Top 15 Guided Selling Tools for 2026 Nektar, CRM data foundation and AI-guided deal signals HubSpot, marketing and sales analytics with guided reporting Salesforce CPQ, configure-price-quote guidance for complex deals Revenue.io, AI-guided sales methodology and real-time coaching ClickPoint, lead management and prioritization Aviso, AI-driven pipeline inspection and forecasting LevelEleven, performance scorecards and real-time coaching Zebrafi, cloud-based guided selling and pipeline insight DealHub, revenue workflow and guided selling playbooks Veelo, onboarding and content guidance in one platform Quark Docurated, content intelligence and recommendation Highspot, sales enablement with AI-guided content and coaching Seismic, enablement platform with AI-guided content and buyer engagement Tact.ai, edge AI for enterprise CRM guidance Vymo, guided selling for financial services Overview of the 15 Best Guided Selling Tools 1. Nektar Nektar’s role in guided selling is upstream of most of the tools on this list: it makes sure the CRM data a guided selling tool depends on is actually complete before that tool tries to guide anything. Data Foundation automatically captures every email, meeting, call, and calendar event across a team and writes it natively into Salesforce, HubSpot, or Dynamics, with zero rep effort required. Daisy AI then turns that captured activity into the specific signals guided selling depends on: deal risk flags, buying-group coverage, and MEDDPICC completeness, surfaced directly on the Salesforce Opportunity tab rather than a separate dashboard. Best for: Salesforce-first teams that need the underlying deal data reliable enough for both reps and any AI-guided tool sitting on top of it. 2. HubSpot HubSpot’s marketing analytics and dashboard tools let you track marketing and sales campaigns in one place, giving your team a shared, reliable data foundation to guide sales efforts from. 3. Salesforce CPQ Salesforce CPQ accelerates quoting, price management, and deal closing, giving reps structured guidance through configuration and pricing decisions on complex deals. Available in three editions, with the full Quote-to-Cash edition also supporting billing and collections. 4. Revenue.io Revenue.io uses AI to guide

Product

Backstory (formerly People.ai) Alternatives: 10 Options for 2026

Backstory (formerly People.ai) Alternatives: 10 Options for 2026 Product 11 min Updated: July 20, 2026 People.ai rebranded to Backstory in April 2026, repositioning itself from an activity-capture platform into what it now calls a “Revenue Answers Platform” which is a conversational AI layer that reasons over captured activity data to answer natural-language questions about deal and account health. Same underlying company, same core capture technology, a meaningfully different pitch. That rebrand is also a useful marker for something bigger that’s happened across this entire category since the original version of this list. Every tool built in the last decade for “capture activity, show a dashboard” is now being measured against a different bar: can it feed an AI agent that acts on that data directly, not just a human reading a report. That’s the lens this update applies to all ten entries below. Get our latest insights into your inbox What Is Backstory (formerly People.ai)? Backstory automatically captures sales activity like emails, meetings, and calls, and structures it against CRM records. What’s new since the April 2026 rebrand is a conversational interface layered on top: instead of navigating dashboards, users can ask natural-language questions about deal or account health and get an AI-generated answer reasoning over the captured activity. Worth knowing before you evaluate it: a conversational layer is only as reliable as the data it’s reasoning over. Read detailed feature comparison Comparing Nektar & Backstory (People.ai)? Top Backstory / People.ai Alternatives for 2026 1. Nektar Nektar GTM telemetry platform that automatically captures every customer interaction and delivers clean data to your CRM, data warehouse, and AI applications, with zero manual entry or adoption friction. Unlike tools that lock your data in proprietary interfaces, Nektar acts as revenue signals infrastructure: capturing emails, meetings, calls, and Slack, then piping structured intelligence into Salesforce, Snowflake, Claude, and your entire stack. Data Foundation automatically captures every email, meeting, call, and calendar event and writes it natively into Salesforce, HubSpot, or Dynamics — zero rep effort, live in under two weeks. Time Travel retroactively corrects historical records as new context arrives, closing a gap no point-in-time capture tool (including Backstory) can touch. Daisy AI then surfaces 39 signals across categories like buyer visibility, deal risk, and rep performance directly on the Salesforce Opportunity tab. Key features: Zero-rep-effort capture across email, calendar, meetings, and calls Time Travel retroactive correction: up to 12 months of historical backfill Daisy AI signal library across buyer visibility, deal risk, and forecast-relevant flags Vendor-neutral as it sits alongside your existing sales stack rather than replacing it Best for: Salesforce-first enterprise teams that need CRM data reliable enough for AI agents to act on directly, not just a cleaner dashboard. 2. SetSail (now part of ZoomInfo) SetSail was acquired by ZoomInfo in 2024 and now operates within ZoomInfo’s broader platform rather than as an independent company. It still functions as an AI-powered sales data layer capturing activity across email, calendar, and call transcripts and surfacing the specific rep behaviors that correlate with winning deals, plus incentive mechanics to reinforce them. Key features: automated activity capture across email, calendar, and calls; behavioral pattern analysis tied to deal outcomes; MEDDPICC-style meeting-prep summaries; incentive and gamification layer for reinforcing winning behaviors. Best for: Teams already invested in the ZoomInfo ecosystem wanting behavioral analytics layered on top of activity capture. 3. Einstein Activity Capture (EAC) EAC remains Salesforce’s native tool for syncing email and calendar activity into Salesforce records. It’s a reasonable baseline for teams that want activity visibility without adding a third-party vendor, though it lacks the AI-driven signal layer — deal risk scoring, buyer engagement analysis — that dedicated revenue intelligence tools build on top of similar capture data. Key features: captures email and calendar events from Microsoft or Google accounts, logs activity to the Salesforce timeline, native to Salesforce with no separate vendor relationship required. Best for: Teams wanting basic activity capture natively inside Salesforce without additional AI features or vendor cost. 4. MatchMyEmail MatchMyEmail automates email and calendar logging into Salesforce, working with any email client or host rather than requiring a specific inbox provider. It’s a narrower, lighter-weight tool than most others on this list — no AI signal layer, just reliable, automated capture. Key features: automatic email and calendar capture, permanent storage of historical communication data, compatible with any email client. Best for: Teams wanting straightforward, dependable activity logging without a broader intelligence platform attached. 5. Revenue Grid Revenue Grid combines activity capture with guided-selling and forecasting features — 360-degree pipeline visibility, forecast-to-actual comparison, and revenue signals aimed at improving process consistency across a sales team. Key features: 360-degree pipeline visibility, forecast accuracy tracking, guided-selling signals embedded in Salesforce. Best for: Teams wanting activity capture bundled with broader guided-selling and forecasting tools in one product. 6. Aviso AI Aviso’s forecasting engine is now paired with MIKI, a conversational orchestrator that can query pipeline data and trigger CRM updates directly, alongside a library of 50+ pre-built revenue agents and a no-code studio for building custom agentic workflows. Key features: MIKI conversational AI orchestrator, predictive forecasting, 50+ pre-built revenue agents, no-code agent workflow builder. Best for: Teams wanting AI agents built directly into forecasting and pipeline execution, not just activity capture with a chat interface layered on top. 7. Collective[i] Collective[i] has repositioned itself around what it now calls “applications and agents” for sales forecasting and CRM optimization, explicitly framing its mission around helping organizations “operate with the speed and precision required to compete in an AI-first world.” Underneath the updated positioning, its core capability remains automated activity capture combined with AI-driven forecasting and opportunity scoring. Key features: AI-driven forecasting and opportunity-win probability, automated activity and contact capture into CRM, professional-network intelligence for surfacing warm relationship paths. Best for: Teams wanting forecasting and relationship-network intelligence combined in one platform. 8. LinkPoint360 LinkPoint360 focuses specifically on email integration for Salesforce and Microsoft Dynamics — one-click email logging, custom object and field detection, and client-side deployment for teams with stricter data-residency requirements.

RevOps

RevOps Starter Guide – Building a Successful RevOps Roadmap

RevOps Starter Guide – Building a Successful RevOps Roadmap RevOps 15 min Updated: July 20, 2026 Consider a soccer game. We know the key parts of a team are the players and the coach. If we’re to draw a comparison, “sales” is the striker, while “RevOps” is the coach that analyzes, strategizes, and develops the game plan to win more games. In the real world, RevOps helps your organization run an interconnected business. It streamlines the end-to-end revenue process and GTM functions. It, consequently, breaks down operational silos and improves efficiency and predictability. Businesses today understand what RevOps is better than ever, and adoption has followed. A 2026 survey of over 1,200 B2B companies found 78% now have a dedicated RevOps function, up from 48% in 2023 and 30% in 2021. RevOps has gone from an emerging bet to close to the default operating model for B2B companies with real growth ambitions. With data’s growing significance and an increasingly complex tech stack, organizations are relying on RevOps to maximize revenue generation by strategically removing sales roadblocks. And align the entire organization towards a single goal – revenue generation. That’s where a RevOps roadmap comes in. If you don’t know how a RevOps roadmap helps, let the expert rein you in. We spoke to Briana Yarborough on The Revenue Lounge podcast to find out what is a RevOps framework and how businesses can create one. Briana is a seasoned RevOps leader and co-founder of a RevOps solution in development. She serves as an advisor and executive leader for several high-growth startups. She’s also an active thought leader of RevOps in multiple communities and was recognized as one of the Top 25 trailblazers in the space. Look at the full discussion below and keep reading to know more about building a revenue operations roadmap. What is a RevOps Roadmap? A RevOps roadmap, in its simplest form, is a strategic visualization of your team’s upcoming projects. For leaders, it’s a goal-oriented tool communicating the clear scope of activities and outlining how these activities tie back to revenue. It enables managers to align the team and focus on activities that maximize the value of converting prospects to buyers. For reps, it is a source of truth to highlight “what” work is being done and “why.” Briana YarboroughVP, RevOps at Pontoon Solutions Revenue operations is about aligning the entire organization across the customer’s life cycle. A RevOps roadmap can be followed seamlessly when everyone on the team understands it. Therefore, it’s important to get these 5 attributes right when creating an effective roadmap: Strategic: Focus areas and strategies Simple: Short, crisp, and visual Goal-oriented: Key deliverables and activities Easy to communicate: No jargon, straightforward Collaborative: Cross-functional cooperation Get our latest insights into your inbox A RevOps Roadmap Clears The Path To Revenue Success Adding a strategic layer of RevOps to your Go-to-market functions connects all activities which otherwise exist separately in a vacuum. Here are some key reasons why you need a RevOps framework: 1. Prioritization One of the primary benefits of having a RevOps roadmap is giving teams the necessary visibility. They can prioritize high-impact projects and focus on those that positively affect revenue. Teams can avoid off-plan requests that distract them from hitting predictable targets. Additionally, it doesn’t let your weekly meetings run in divergent directions based on unhinged queries from Sales or Marketing teams. 2. Alignment A GTM alignment is possible when teams improve buying experience by breaking down cross-functional silos. Through sales and marketing alignment, the RevOps roadmap serves as a single source of truth to unify people, processes, and platforms. This alignment drives full-funnel accountability and helps you grasp inconsistencies and develop a baseline for improvement. Mark HudsonPrincipal Consultant, RevOps Consulting LLC Without a roadmap, your path to success is fraught with dangers, and you do not have a clear sense of direction and can make a wrong turn or fail to reach your destination. 3. Understanding A roadmap helps you dive deeply into the “why” behind revenue generation activities, including business goals and supporting resources. Start as early as you possible can, even if you have a one-person team. You can understand what your priorities are and then begin to earmark things to accomplish in Q1, Q2, Q3, Q4. Briana YarboroughVP, RevOps at Pontoon Solutions It provides clear definitions for and sets up the priority of each project, timeline, and initiative to measure progress effectively. Simultaneously, a RevOps roadmap restricts confusion among different departments. Also, a roadmap empowers leaders to develop a vision for the business and ensure a solid system is in place to make this reality. Now that you know what a RevOps roadmap is, are you inspired to build one for your business yet? Let us help. Creating a Successful RevOps Roadmap You can strategically and tactically achieve the roadmap to a successful RevOps plan with several key steps and considerations in place. For beginners, it’s best to start with the 4 primary phases. But remember – a RevOps roadmap will differ for each organization based on its maturity stage and resources. You can’t truly start to just come in and do what worked at another company. Every business model is different; every organization is different. If you don’t have the context, you can’t implement (the RevOps roadmap). Briana YarboroughVP, RevOps at Pontoon Solutions For this blog, we’ll dive into a summarized version of a beginner’s RevOps framework. Phase 1: Discover Research is pivotal in understanding the problems in relevant operational areas before solving them. The initial analysis, aka discovery, seeks to lay down the “state of play” before designing the roadmap. Discovery presents a comprehensive awareness of stakeholder expectations and gaps in the customer journey, starting with a thorough audit. Use these questions to set the direction of your roadmap for stakeholders: Does each team clearly understand what they’re working on? How does the team determine the next best steps? Can each operational initiative be mapped back to a gap felt by customers? Is

RevOps

5 Ways Siloed Data is Burning Your Revenue

5 Ways Siloed Data is Burning Your Revenue RevOps 12 min Updated: July 20, 2026 There are plenty of visible reasons revenue underperforms. A slow quarter, a competitive loss, a stalled deal. Most of those show up somewhere in a QBR deck. Siloed data rarely does, and it’s usually a bigger problem than any of them. In MuleSoft’s 2026 Connectivity Benchmark, surveying 1,050 IT leaders, 90% respondents said data silos are creating business challenges for their organization. This has risen to 94% among companies actively using AI agents.  Gartner has long pegged the average cost of poor data quality at $12.9 million a year per organization; more recent Gartner research adds a sharper, more current number on top of it: 60% of AI projects are expected to be abandoned through 2026 due to data that isn’t ready for AI to use. Siloed data isn’t a new problem. What’s new is what it’s now blocking. Get our latest insights into your inbox What Is Siloed Data? Siloed data is information from revenue-generating activity in sales, marketing, customer success departments that’s trapped in disconnected systems. These activities are visible to the team that owns it and effectively invisible to everyone else.  Marketing builds strategy on data sales never sees. Sales logs activity customer success has no visibility into. Each team optimizes its own numbers because that’s the only complete picture available to it. This is exactly the gap revenue operations exists to close. But RevOps as a function can only align teams around data that’s actually complete and shared in the first place. How Data Silos Form Three forces reliably create them: Siloed incentives – When sales, marketing, and customer success are measured on separate goals, they optimize for those goals rather than the shared outcome. Misalignment between sales and marketing specifically has been estimated to cost businesses over $1 trillion annually,  a figure that’s been widely cited since a 2021 HBR analysis and, if anything, undersells the problem now that buying committees and tech stacks have both grown since then. Cultural resistance – Legacy systems persist because switching feels riskier than staying, even when staying is quietly more expensive. Teams that don’t have a shared data culture struggle to turn the data they do have into anything actionable. Tech stack sprawl – MuleSoft’s 2026 research found the average organization now runs 957 applications, up from 897 the year before. And only 27% of them are actually integrated. Organizations already using AI agents run even more: 1,103 applications on average, 45% more than organizations without agents. More tools, adopted faster than they’re connected, is the direct mechanical cause of most data silos. 5 Ways Siloed Data Is Damaging Your Revenue 1. Missed business opportunities When teams default to protecting their own data rather than sharing it, prospecting and pipeline nurturing both suffer. A lead handed from marketing to sales without the context behind it is a colder lead than the data actually supports. Internal competition for credit compounds the problem: teams optimize for defending their own numbers rather than looking for revenue opportunities that fall between them. 2. A worse customer experience Disconnected touch points mean sales often can’t see where a prospect actually is in their journey, leading to repetitive conversations, generic follow-ups, and a buyer who has to re-explain their situation to every new person they talk to. It also distorts cost measurement: when a deal that closed on a call gets attributed to an email instead because the systems don’t talk to each other, marketing’s cost-per-acquisition numbers become unreliable. And decisions get made on top of that unreliable number. 3. Inaccurate revenue forecasts Siloed data means no single leader has the complete picture, and different departments’ partial views rarely reconcile cleanly. The result is a forecast built by stitching together incomplete team-level reports rather than one grounded in what’s actually happening across the full customer journey. This is a large part of why forecast accuracy remains a persistent, well-documented problem across B2B sales organizations. 4. Lower productivity and weaker cross-functional trust Sales and marketing misalignment isn’t just a data problem. It’s a trust problem that compounds over time. When teams can’t see each other’s data, blame-shifting becomes the default response to missed targets, and each function starts optimizing for its own win rather than the business’s. Employees also notice when leadership doesn’t seem to understand how data actually gets used day to day, which corrodes morale in a way that’s hard to trace back to a specific number, but real all the same. 5. Compliance and security exposure Each isolated system typically runs its own security posture, multiplying the number of places a breach or leak can originate. Manually re-entering the same data across disconnected systems. A rep logging the same lead in a spreadsheet and a CRM, for instance, also introduces the kind of error that erodes trust in the numbers even before any compliance issue arises. On the privacy side, the landscape has shifted since this post first published: Google reversed its plan to phase out third-party cookies in Chrome in 2024, moving to a user-choice model rather than a full deprecation. Safari and Firefox still block third-party cookies by default, so the underlying trend that first-party data is becoming the more durable, more compliant asset hasn’t changed. Organizations with siloed data are still worse-positioned for this shift than ones with a unified, first-party data strategy, regardless of exactly which browser does what on which timeline. Data Silos in the Agentic AI Era For most of the last decade, a data silo was primarily a coordination cost. Teams making worse decisions because they couldn’t see each other’s information. That’s still true. But MuleSoft’s 2026 Connectivity Benchmark surfaces a sharper problem: 50% of AI agents currently operate in isolated silos, disconnected from any cohesive multi-agent system, and 86% of IT leaders agree that without proper integration, AI agents introduce more complexity than value rather than less. Only 54% of organizations have a centralized governance framework for the

RevOps, Sales

7 Elements of a Successful Deal Review

7 Elements of a Successful Deal Review RevOps 13 min July 20, 2026 Knowing the ins and outs of your deals is what makes revenue predictable. A good deal review tells you what’s actually happening in your pipeline, where to pivot, and which risks to get ahead of before they cost you the quarter. It’s also one of the most commonly botched rituals in sales. Most deal reviews are unplanned, ad-hoc sessions that interrogate a rep instead of helping them win. The result is the same as it’s always been: inaccurate forecasts, missed targets, and reps who dread the meeting instead of using it. The first question to ask is what’s riding on getting deal reviews right. Before the advent of AI, the data a deal review runs on used to be interpreted by a human. Probably a manager reading a stage field, applying judgment, and catching the obvious gaps.  Cut to present times, that same data now feeds AI agents that update opportunity stages, flag deal risk, or trigger next steps directly inside Salesforce, with a lot less human judgment sitting between the data and the action. A deal review built on incomplete data used to produce a bad meeting. Today it can produce a bad decision made by software, at a speed no manager can catch in time. This guide presents a seven-element framework for what a deal review actually needs to look like now. Get our latest insights into your inbox What Is a Deal Review? A deal review is a meeting between a sales manager and a rep about the deals in that rep’s pipeline. It assesses the probability of closing, and agreeing on next-best actions for anything that’s stuck. Done well, it’s a coaching tool. Done badly, it’s an interrogation that produces a status update nobody trusts. What’s Actually Changed The mechanics of a deal review haven’t changed. What has changed is the environment it runs in: Buying committees are bigger, and reps see less of them. Gartner puts the average B2B buying group at 6 to 10 stakeholders, most of whom your rep will never speak to directly, and none of whom show up in Salesforce unless someone manually adds them as a contact. AI agents are now acting on the data a deal review used to just discuss. Salesforce’s April 2026 Headless 360 release made every core Salesforce capability available as an API or MCP tool specifically so agents can read, write, and execute workflows without a human in the loop. When a stage field, a close date, or a forecast category is wrong, it’s no longer just misleading a manager in a Friday pipeline review. It’s potentially misleading an agent that acts on it before anyone notices. The data gap deal reviews have always fought is now measurable at scale. Most CRMs are missing a large share of what actually happens in a deal: meetings that never got logged, stakeholders who were never added, activity that lives in someone’s inbox instead of the opportunity record. That gap used to just make forecasts optimistic. Now it’s the input layer for automated decisions. None of this changes what a good deal review is for. It changes what “good data going into the review” needs to mean. Why You Still Need Deal Reviews Selling has only gotten harder to do by “feel” alone. Longer cycles, bigger buying committees, and more channels for a deal to quietly go sideways all mean a manager’s instinct is a weaker substitute for actual pipeline data than it used to be. Here’s what a deal review still gives you that nothing else does: 1. Identify risks and opportunities early A good deal review surfaces deal risk before it’s a lost deal. It answers questions like which stakeholders have gone quiet, which deals haven’t had a meeting in weeks, or which “commit” deals don’t actually have the engagement to back that up. Sales teams that catch this early can act on it; teams that find out at quarter-close can’t. 2. Align with cross-functional teams Deal reviews often surface why a deal is stuck for reasons the rep alone can’t fix. Maybe it needs a solutions engineer in the next call, a piece of marketing collateral, or executive air cover. A good review turns that into an action item instead of a shrug. 3. Increase rep accountability Every deal review should end with a clear next step for the rep, and a regular cadence to follow up on it. That consistency, not the interrogation, is what actually makes reps more accountable over time. 4. Gain executive support Executive deal reviews are where a rep can borrow leverage they don’t have alone. An exec-to-exec relationship, a strategic sponsorship, a connection nobody on the account team knew existed are few examples. That only works if the review actually surfaces who’s in the room on the buyer’s side, which depends on the buying committee being visible in the first place. 5. Develop sales reps through targeted coaching A deal review tells a manager exactly where a rep needs help, not in the abstract, but on this specific deal, this specific gap. A rep who hasn’t followed up in 30 days needs different coaching than one who’s engaged the wrong stakeholder. Specific coaching, from specific data, is what actually moves a rep’s win rate. Why Most Deal Reviews Still Fail 1. Poor data to begin with This is still the root cause behind most failed deal reviews, and it matters more now than ever. Most organizations’ deal data lives in silos across sales, marketing, and customer success, and a large share of what actually happens in a deal never makes it into the CRM at all. That used to mean a deal review ran on an incomplete picture. Now, with AI agents reading and acting on that same CRM data, an incomplete picture doesn’t just produce a bad meeting. It produces bad automated decisions with nobody checking the work first. 2. No consistent process Deal

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