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Product

How Nektar Puts CRM Data Hygiene on Auto Pilot

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How Nektar Puts CRM Data Hygiene on Autopilot Product 11 min Updated: August 17, 2026 Data automation isn’t new. Zoom out and you’ll find plenty of solutions in the category: workflow automation tools like Zapier, Workato, and Syncari; digital adoption platforms like WalkMe and Whatfix, which offer automation capabilities of their own; and a long tail of sales tools that sync website leads into a CRM. Fewer specialize in syncing emails into Accounts specifically, and fewer still handle syncing Contacts and Activity data into Opportunities, the handful that do generally offer it either through manual workflows or automated syncing that only reaches the Account level, not the Opportunity level. Mastering genuinely zero-adoption, fully automated CRM data syncing requires deep expertise in three things: the objects and fields of a CRM and how they interact, the sales process nuances that interact with those CRM elements, and the ability to connect the two into real logical inferences. That third piece is the hardest, and it’s where Nektar is purpose-built specifically for contact and activity capture, backed by an extensive library of logical inference under the hood. Here’s how it actually works. Get our latest insights into your inbox 1. Depth and Breadth of Data, With Unmatched Sync Accuracy Accurately Managing Data Sync in Accounts With Multiple Opportunities Automated capture of contacts, emails, and events for an account with a single open opportunity is table stakes. The real power shows up as sales process complexity increases. As companies scale, so do their sales motions. Teams start running multiple open opportunities on the same account: same product, new team; same product, new market; new product, same team; or some combination. Most existing data automation solutions, including tools outside revenue operations entirely, offer one of three syncing methods: Manual selection of the correct opportunity in an inbox sidebar, with some fields auto-populated and some not, requiring rep confirmation Automatic sync, but only at the Account level, and only if the contact already exists in that account Automatic sync at the Opportunity level, but into custom fields, which creates a reportability problem down the line All three introduce their own new headaches, and RevOps teams don’t need more of those. What’s missing from each is Nektar’s proprietary machine learning model, Opportunity Affinity AI, the core of Nektar’s sync accuracy. Opportunity Affinity AI weighs multiple inputs: the people in the From, To, and cc fields, the frequency of engagement with the people in To and cc, and the number of completed activities with everyone involved. A graph gets built connecting everyone associated with the current activity, alongside past activity across every possible Opportunity and Account those contacts touch. A confidence score gets assigned to contacts and activities based on that graph, which determines which Opportunity they actually sync into. Managing Complex Combinations: A Unique Capability Most sales tech that captures activity delivers on that specific promise. Capturing contacts alongside activity is rarer, and handling complex real-world combinations of the two is rarer still. Nektar handles scenarios most tools can’t: Leads and Contacts: Capturing independent activity for both is table stakes. Nektar checks whether the email domain matches an existing Salesforce Account. If it matches, Nektar prioritizes syncing to the Account and its Contacts over the Lead record. If the Account exists but a contact doesn’t, Nektar creates that contact automatically. Say a rep gets an email from John (a contact at Acme, which exists on Salesforce), with Barney (an existing Lead) and Jane (who doesn’t exist on Salesforce at all) in CC. Nektar syncs the activity to the Acme Account and creates a new Contact for Jane, automatically. Any combination of leads and contacts, one lead and two new contacts, three leads and one existing contact, three leads and no contacts at all, gets handled the same way. Rep in CC: In complex, multi-stakeholder evaluations, a prospect will often email a colleague directly and CC the sales rep. Nektar captures this too. If the colleague isn’t yet in Salesforce, Nektar creates the contact in the Account and associates it with the open Opportunity. Closed opportunities: A common Customer Success scenario: a deal closes and the Opportunity closes with it, but contacts keep emailing the CSM afterward. Nektar keeps capturing that activity, syncing it at the Account level since the Opportunity itself is closed. A mix of open and closed opportunities: If an outgoing email has the rep in From, a contact from an open Opportunity in To, and a contact from a closed Opportunity in CC, the activity syncs to the open Opportunity, since that’s the deal actually affecting the pipeline. Activity between a prospect and a rep’s colleague: If a rep’s colleague emails the prospect directly without the rep on the thread, that activity still syncs at the Account level of the rep’s open Opportunity. If the rep is CC’d instead, it syncs directly to the Opportunity. Activity to a non-sales contact at the seller’s own company, with the rep in CC, also gets captured. Activity involving both Salesforce and non-Salesforce users gets captured on both sides. Contacts and activity across multiple child domains under one parent domain get captured and correctly associated. 2. Sync Into Standard Objects for Better Reportability A simple but meaningful differentiator: Nektar syncs contacts, emails, and meetings into standard Salesforce objects, not a proprietary data structure sitting alongside your CRM. That matters directly for any operations professional who has to build reliable reports on top of this data. Custom objects are supported too, but standard-object sync is the default, precisely because it’s what makes the data actually usable by the reporting your team already runs. 3. Time Travel: Retroactive Context, Not Just Ongoing Capture The mark of a strong seller is that they’re always prospecting, which means engaging contacts long before those contacts exist anywhere in Salesforce. Eventually, after weeks or months of that groundwork, an opportunity lands and an Account gets created. This is where Time Travel does its work. Nektar senses the new Account through domain matching, connects it to

RevOps

A RevOps Guide to Conquer Bad Data

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Mastering the Data Battle: A RevOps Guide to Conquer Bad Data RevOps 12 min Updated: August 17, 2026 Most organizations, particularly those scaling quickly, face an extensive challenge with poor-quality data. It keeps businesses from maximizing opportunity, contact, account, and intent data to actually improve revenue growth. We discussed this directly with RevOps and data expert Melissa McCready, Founder and CEO at Navigate Consulting Group. Melissa has 20 years of experience across CRM, marketing automation, and customer success, and has consulted on more than 300 revenue and growth operations projects. From her experience, here’s what’s actually driving the bad data problem, and how to convert data from a liability into an asset. You can listen to the full conversation with Melissa here:  Get our latest insights into your inbox First, What Is Bad Data? Data is the fuel that keeps a revenue engine running, but it’s not about having tons of it. It’s about having data that’s clean and complete enough to draw the right insight and make good business decisions from. Leads being misrouted, pipeline growth failing, forecasts and accurate customer insights, plays and interactions are based on data. When hygiene isn’t prioritized, there’s a snowball effect, and it gets worse fast. Melissa McCreadyFounder & CEO, Navigate Consulting Group The specific things worth worrying about: inconsistent, incomplete, inaccurate, siloed, duplicate, and non-compliant data. Bad data doesn’t enrich the revenue process, it actively undermines it. Every decision made on flawed data is a step forward and three steps back. Why Is Bad Data Still a Challenge in 2026? Bad data isn’t a new problem. It’s one that still needs solving, and the volume of data involved keeps growing every year, which makes the problem harder to ignore, not easier. 1. Data Leakage For 48% of sellers, incomplete data is their single biggest challenge. Data is supposed to give you full visibility into your pipeline, your improvement areas, and your leading indicators, yet a large share of opportunity data never actually makes it into the CRM at all. A few reasons this happens consistently: reps miss entering data points manually, reps aren’t trained on all of a CRM’s functionality so they skip parts of it, and complicated workflows fail to capture key information in the first place. The result is missed, poor-quality data entering the tech stack, data leakage in practice, not just in theory. Clean data is what enables a lead’s seamless journey from first conversation all the way through to cash. It shows exactly what stage of the buyer journey a lead is actually at, and how to add value at each specific touchpoint. Situations change mid-deal too, a key stakeholder leaves the buyer’s organization, or the company gets acquired, and your contact data has to reflect that. Without regular updates, a CRM decays quietly, and you lose the ability to accurately validate who’s actually still in the buying group. 2. Disconnected Systems It depends on how things are structured, even from an organizational perspective. Where Sales is owning Salesforce, and customer success is owning Gainsight, and marketing is owning Marketo and Hubspot. And when they own that, what does that mean on these controls? Melissa McCreadyFounder & CEO, Navigate Consulting Group Tools across the tech stack capture large amounts of data from buyer-seller conversations. The problem is when those tools don’t talk to each other, and the data never flows into the rest of the stack. Quality data ends up stuck in inboxes, chats, calendars, meeting notes, and call transcripts, genuinely useful information trapped in a tool nobody else on the team can see. Without a single source of truth, a CRM connected to every adjacent tool actually uses, none of those tools deliver their full value, and you can’t build a complete picture of the buyer journey from fragments scattered across five different systems. 3. Missing Leadership Buy-In Number one reason that data goes in, is, it starts with decisions and it starts with people making decisions about it. It really comes back to making the decisions and it is the people making the decision decisions. It’s not a system where people like to blame. Who put the systems in they didn’t get there on their own so it’s the people. Melissa McCreadyFounder & CEO, Navigate Consulting Group Only 19% of business leaders consider CRM data a high-priority initiative for their organization. Compounding the problem, bad data restricts managers from coaching reps effectively and limits 27% of them from hitting quota at all. When leadership doesn’t prioritize clean data or regulate poor-quality data, the entire company bears the cost. Poor data culture trickles down from the top, and it snowballs into low-quality practices that hurt customer experience and trust well before anyone traces the problem back to its actual source. 4. No Data Governance Strategy in Place Self-reporting and recurring data inefficiencies amplify decay, feeding teams incorrect information and building distrust in the data itself. Reps also resist dropping dead leads from the pipeline, assuming a fuller pipeline looks better, but a bloated pipeline built on stale data just skews every insight built on top of it, and reps waste real time chasing opportunities that were never actually live. First of all, I think having control of the data is really the biggest data challenge. From knowing where the data originated to who can modify it, what process dirves the data collection, the data quality itself and data governance. Melissa McCreadyFounder & CEO, Navigate Consulting Group Not cleaning data at regular, consistent intervals is itself a sign of missing governance, and without a governance strategy, no one actually owns the data as a single source of truth. That’s a recipe for exactly the kind of disaster this whole guide is about. 5. Over-Reliance on Manual Processes The growing revenue tech ecosystem gives businesses more tool options than ever, and many organizations buy and deploy several at once. Reps don’t share leadership’s enthusiasm for this: 66% report feeling overwhelmed by the sheer number of revenue tools they’re

CRM

8 Steps to Maintain CRM Data Hygiene

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8 Steps to Maintain CRM Data Hygiene CRM 10 min Updated: August 17, 2026 A CRM is one of the steepest investments in your tech stack, and even the most expensive or functionally superior one won’t work if the data inside it isn’t clean. A CRM needs good-quality data to actually do its job, and that’s a question of quality, not volume. Dirty CRM data shows up in plenty of forms: incorrectly entered data, duplicate records, data that never made it in at all, or data that’s simply no longer relevant. Every one of these turns a CRM into a cost center that depletes value over time rather than creating it. Making CRM data hygiene a real priority is necessary to hit revenue goals, and manual cleanup sessions aren’t the answer. You need a genuine strategy for dealing with bad data, and the right technology to support it. This guide covers what CRM data hygiene actually means, why it matters, what ignoring it costs, and eight steps to a real strategy, drawn from RevOps practitioners who’ve made data hygiene their focus. Get our latest insights into your inbox What Is CRM Data Hygiene? Multiple sources push data into a CRM every day, and your GTM team uses that data to draw insight and make real decisions. CRM data hygiene is the ongoing process of making sure the data entering and staying in your CRM is clean, complete, and accurate, at all times, not just after a cleanup project. If the data is full of errors, every action sales, marketing, or customer success takes on top of it falls flat, or worse, leads directly to revenue leakage. A properly built CRM data hygiene strategy keeps data clean and enriched continuously, which means the workflows your GTM team runs on top of it deliver real returns consistently, not just right after a cleanup. The Impact of Poor CRM Data on Your Revenue Missing data is a large piece of the problem, but far from the whole picture. Your CRM is likely infested with several distinct data quality issues: 1. Stale Data CRM data decays fast. Current research puts typical annual decay at around 22.5%, with some industries and record types running as high as 70%, tech and healthcare contacts tend to decay faster than finance, for instance. 2. Incorrect Data Human data entry is inherently error-prone. Most organizations still depend on reps to manually update CRM data, and reps end up entering incorrect information as a simple matter of course, not because anyone’s being careless. 3. Irrelevant Data Customer data keeps changing. People leave roles, companies grow past a segment, mergers and acquisitions happen, or a buyer moves to a competitor, and none of these changes automatically make their way into the CRM. These inefficiencies compound into real financial cost. Gartner’s widely cited estimate puts the average cost of poor data quality at $12.9 million per organization annually, and IBM research, cited by Harvard Business Review, puts the total cost of bad data to US businesses at approximately $3.1 trillion a year. Asia CorbettSenior RevOps Manager, Bread Financial Another big challenge for Revenue Operations teams is missing data. It’s the manual versus automated piece. What information is our revenue teams having to manually enter into the system. And if they don’t do that, or do it incorrectly, that affects the data integrity of your operations. How Poor CRM Data Hygiene Makes You Bleed Revenue 1. Increasing Tech Debt Every tool in your stack performs only as well as the CRM data feeding it. Poor-quality data means those tools fail to deliver the value or ROI they were bought for, and over time, the stack bloats with tools quietly not earning their keep. 2. Scattered Buyer’s Journey Poor-quality CRM data creates a false picture of where a buyer actually is. A rep selling based on the CRM’s stage while the buyer is genuinely somewhere else entirely means both sides fall out of sync, and that mismatch is exactly where opportunities get missed. 3. Poor Forecasting Insight built on bad CRM data fails to predict revenue accurately quarter after quarter. That failure has a direct, compounding effect on resource allocation, and eventually on revenue itself. Asia CorbettSenior RevOps Manager, 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. 4. Poor Rep Productivity Reps spend real time on manual CRM entry, or hunting for data the moment a report is due. Bad data also distorts prospecting directly, since reps may be reaching out to the wrong people from the start based on what the CRM tells them. Rosalyn Santa ElenaFounder, The RevOps Collective The manual entry aspect has a huge impact on rep productivity. it’s not just the time that it takes for them to manually put the data, but the employee satisfaction and motivation factor gets affected too. With data not being in systems like CRM, reps have to spend a lot of time looking for that data. 5. Failed Marketing Campaigns Bad CRM data produces a string of campaign failures that can genuinely damage brand reputation. An ABM campaign built on a list of stale contacts, for instance, spends real budget on an idea that was never going to convert. Asia CorbettSenior RevOps Manager, Bread Financial You can’t run any marketing campaigns if you don’t have any contact information in your CRM. And it could be mixed with other data. And if there’s not some governance around it, your marketing manager just goes like – Oh! I’m just going to pull this list and I’m going to put them in a campaign or sequence. What about all the people that failed because they don’t have email addresses? There’s some downstream impacts there. If you don’t have good data, you can’t run marketing campaigns. That affects your funnel. Why Is

Marketing, Sales

How to Drive Sales and Marketing Alignment With Unified Data

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How to Drive Sales and Marketing Alignment With Unified Data Sales, Marketing 13 min Updated: August 14, 2026 Sales and marketing alignment is a huge, ongoing problem. Roughly 9 in 10 sales and marketing professionals say their teams are misaligned, worrying in a business environment that only moves faster every year. Because sales and marketing performance gets measured differently, both teams end up using different approaches and systems, producing disjointed content, maintaining a passive relationship, and setting disconnected goals. All of it leads to real revenue leakage. Get our latest insights into your inbox What Is Sales and Marketing Alignment? Sales and marketing alignment is the ongoing set of processes both teams use to collaborate seamlessly across the full range of revenue-generating activity, not a single initiative with a defined end date. It matters more in a digital, distributed business environment, where teams are scattered across geographies and increasingly remote. Alignment isn’t an outcome you reach once, it’s a continuing joint effort, and a large majority of sellers and marketers feel poor alignment actively hurts both the business and the customer experience. Misalignment most commonly shows up when marketing hands off leads without complete or current contact information, when both teams set disconnected revenue goals based on their own separate strategies, or when communication between the two is simply muddled. Cross-functional collaboration smooths these bumps out, and the first real step toward it is putting unified data at the center of the process. A Peek Into Unified Data Data volume keeps growing every year, and it’s growing faster than most estimates from even a few years ago suggested. The real challenge has never been generating data, it’s creating value from it. Nearly half of employees find it genuinely difficult to share information across teams because of poorly integrated systems, and the result is the same dirty data pattern showing up everywhere: incomplete, inaccurate, non-compliant, outdated, inconsistent. This doesn’t just hurt rep performance, it hurts the customer experience directly, a majority of reps believe organizational silos negatively affect how a prospect experiences their team. Unified data solves this by combining data from disparate sources and disconnected systems into a single, central view, capturing data across the sales and marketing tech stack so everyone works from the same picture. It also drives better customer engagement, reduced churn, and higher ARR. Forrester’s research highlights how much buyer engagement now happens before a deal closes: buyers commonly contact sellers five or more times before closing, expect instant answers to complex questions to shorten the cycle, and a large share do substantial independent research before ever talking to sales. That journey leaves behind data breadcrumbs with real insight in them, if a team is actually set up to see it. Making the most of that engagement means sales and marketing need continuous data exchange, not a periodic handoff. New prospect information keeps arriving, and it needs to be accumulated, managed, and kept current so both teams have it at their fingertips. That’s the specific problem unified data solves, and automation, paired with AI for contextual insight, is what makes it work at real scale. Marketing data sales can use: lead and organizational data, lead scoring and qualification results, customer behavior and intent analysis. Sales data marketing can use: connecting campaign objectives and performance directly to revenue, nurturing leads with genuinely relevant content, and building lookalike models of sales-ready leads for new prospecting. The Need for Sales and Marketing Alignment Misalignment between sales and marketing is estimated to cost businesses more than $1 trillion annually, a figure tracing back to Harvard Business Review and IDC research that’s still actively cited in current studies on this exact topic. Sales and marketing have conventionally operated as separate worlds, with different objectives, revenue goals, and processes, creating friction that keeps both from operating at their best. In today’s dynamic environment, where events like economic instability can shift the landscape quickly, a disconnected sales-marketing relationship simply isn’t viable for sustained growth anymore. The buying process itself keeps getting more complex, too. Businesses need to build trust and real relationships, and the buyer today is a buying group, key stakeholders across multiple departments, not one person. Gartner’s research puts the current average at 6 to 10 stakeholders per deal, with enterprise deals frequently reaching 17 or more, and a large majority of B2B deals now involve at least three buying-group members. That buying group wants to engage with multiple people on the seller’s side too, evaluating the company, its process, and its offering as a whole, not just one rep’s individual pitch. As the buying process evolves, so does the customer journey. Traditionally, that journey ended at deal closure. In the modern, bow-tie-shaped funnel, it continues well past close, into onboarding, driving real impact, and sustaining growth, and for subscription businesses specifically, post-sale service and retention matter as much as the initial close, sometimes more. Most SaaS businesses rank customer retention as a genuinely high priority, and for good reason: happy customers are highly likely to purchase again, and companies with strong post-sale support see meaningfully higher repeat-customer rates. At the same time, buyers increasingly want less direct involvement from a rep during the process itself. A majority prefer a rep-free purchase experience where possible, and while selling has become more remote than ever, virtual sales execution still tends to fall short of the win rates teams actually expect. Sales and marketing alignment is a real part of solving that gap, but it has to be more than an exchange of information between the two teams, it has to be a genuine exchange of ideas. Best Practices for Sales and Marketing Alignment With Unified Data Organizations with genuinely well-aligned sales and marketing functions see measurably higher customer retention and higher sales win rates, and strong cross-functional collaboration has been linked to significant increases in key customer spend. Here’s how to actually get there. 1. Define Shared Goals and Strategies Start by establishing common ground through shared Objectives and Key Results,

CRM, Sales

10 Ways Enriched CRM Data Improves Sales Productivity

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10 Ways Enriched CRM Data Improves Sales Productivity CRM, Sales 10 min Updated: August 11, 2026 A CRM is one of the most potent tools in a salesperson’s arsenal, and growing digitization should be making it stronger every year. The reality often falls short. CRM data decays fast, Dun & Bradstreet’s research puts annual CRM data decay at around 70%, with a large share of CRM data incomplete at any given time, and the cost of running a manual system on bad data adds up quickly. The impact on reps is real: combing through multiple tools to extract one useful insight for a single deal, and losing real selling time to making sure CRM information is even accurate to begin with. Could better CRM data actually fix this? This guide covers ten specific ways enriched CRM data drives sales productivity, and where AI fits into each. Get our latest insights into your inbox Enriched CRM Data = Better Data Reps want as much visibility on a prospect as possible for an effective deal: names, email, deal size, phone number, and more, and for multithreaded deals, that same information across every stakeholder involved. Raw data pulled from several sources may or may not actually be accurate, and about half of salespeople believe a more effective CRM system would directly improve their productivity. CRM data enrichment is what turns that raw pool into something usable, verifying existing information and adding the supplemental detail that’s actually crucial to closing a deal. It’s a different process than data cleansing: cleansing removes wrong or unusable information, while enrichment verifies what’s accurate and adds new, useful information on top of it. AI Plays a Key Role in Data Enrichment Gartner has identified CRM data entry as a task particularly well suited to AI. AI enriches CRM data by automating the structuring and filtering of raw data, work that’s genuinely time-consuming for a person, and can perform more complex tasks too: predicting, forecasting, recommending, transcribing conversations, qualifying leads, and feeding them into the CRM automatically. As the CRM market keeps growing, so does the practical need for AI to keep pace with the data volume involved. How Enriched CRM Data Improves Sales Productivity Nearly half of all reps feel their process and workflow is too complicated, and that complexity shows up directly as a productivity problem. Here are ten specific ways AI-enriched data helps. 1. Automate Sales Tasks Sales professionals spend a meaningful chunk of their week on CRM data entry alone, adding up to something close to a full day’s work. With data enrichment, reps can offload real tasks: automated, personalized nurture emails, lead reassignment when a rep is unavailable, scheduled follow-ups so nothing slips, engagement tracking (email opens, response rates, task completion), and pipeline management that flags deals sitting past a feasible time-to-convert. Time management is a well-established productivity lever, and giving reps tools that remove friction from the process lets them stay fully committed to it rather than context-switching constantly. Nektar’s Data Foundation, for instance, automates CRM data entry from multiple first-party sources, helps manage the pipeline, and frees up real time for reps to focus on what actually drives revenue: selling. 2. Get Higher-Quality Lead Capture and Predictive Scoring Tracking every stakeholder gets harder as deal size grows. A champion leaving mid-deal, through a role or job change, means re-nurturing every other stakeholder from scratch if a rep hasn’t tracked them all along the way. AI-powered enrichment updates contact details in real time. If a champion loops in a CMO, a CFO, and an IT head across several email threads over time, and then leaves the conversation entirely, a rep doesn’t need to comb through old threads to reconstruct who else is involved, the data is already captured. Enrichment covering demographic, geographic, and financial detail also supports higher-quality predictive lead scoring, letting reps focus on genuinely engaged prospects who fit the ICP rather than assigning scoring values by hand. Buying Group Intelligence is built specifically around this problem, automatically capturing contact data from first-party sources like email, calls, and meetings for accurate lead capture and scoring. 3. Understand Buyer Intent Single-level contact data, a name and an email, doesn’t hold enough transactional, demographic, or behavioral detail to build real trust and rapport. Reps need genuinely complete data to explore patterns, needs, and buyer personas at any depth. Enriched CRM data surfaces additional context too, transaction history, competitor detail, business model, and purchase triggers, all of which support segmentation, personalized interaction, and more targeted campaigns. 4. Enable Personalized Experiences Missing data, incomplete contacts, and mismatched records have long limited a traditional CRM to being a passive data repository. AI is what’s letting CRMs act as an actual personalization guide instead. Buyers expect brands to understand their priorities deeply, and confidence in delivering that kind of personalization at scale remains low across most organizations, which is exactly the gap AI-enriched data addresses. AI-enriched CRM data analyzes large datasets, recommends how to move a specific deal forward, and supports a real customer journey built around targeted segments rather than a single generic pitch for everyone. 5. Avoid Missed Opportunities A CRM that tells a rep whom to target without explaining why leaves real value on the table. AI closes that gap by organizing CRM data, avoiding duplication, and surfacing the small details that actually speed up a deal, plus cross-sell and upsell opportunities a rep might otherwise have missed entirely. With that visibility, reps can redirect focus toward the deals genuinely most likely to close. Daisy AI works the same way, flagging which prospects carry the strongest purchase likelihood, surfacing risk on a given deal, and recommending next steps grounded in real captured activity. 6. Refer to a Single Source of Truth A large share of prospect-facing teams still can’t access real-time, actionable insight, and instead move between several tools just to assemble one piece of analysis on a single customer. Data enrichment brings every data point under one roof, giving reps real-time visibility and

how revops can transform data hygiene
CRM, RevOps

How RevOps Can Transform Data Hygiene for Companies

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How RevOps Can Transform Data Hygiene for Companies CRM, Revops 10 min Updated: August 11, 2026 Organizations increasingly recognize the indispensable value of data in driving growth. But the sheer volume, velocity, and variety of that data pose real cleaning challenges, and data hygiene issues quietly hinder decision-making, customer experience, and operational efficiency across the board. Trent AllenRevenue Operations Manager, Maxio As a RevOps team, you need to be able to help all teams. A big part of it is making sure all the different tools and systems are connected. RevOps is there to plan, help with processes, building process paths and writing those out. It is also the keeper of truth. When it comes to numbers, we need to ensure that people have actionable data, and we help them with the best process to move forward. In this piece, we revisit our conversation with Trent Allen, Revenue Operations Manager at Maxio, the financial revenue operations platform, discussing how RevOps offers a strategic approach to data hygiene and unlocking the value trapped behind it. Listen to the full conversation here:  Get our latest insights into your inbox What Is Data Hygiene? Data hygiene refers to the practices and processes that keep data clean, accurate, and reliable, maintained and improved across its entire lifecycle, from creation to disposal. Implementing real data hygiene measures minimizes errors, inconsistencies, redundancies, and the other issues that quietly erode data’s integrity and usefulness. Data hygiene matters specifically in RevOps because it’s what makes accurate, reliable, high-quality data available across every revenue-related function. Clean data is what actually enables informed decision-making, since accurate insight depends entirely on the data feeding it. What Is RevOps, and What Role Does Data Hygiene Play in It? RevOps, short for Revenue Operations, is a strategic approach that aligns and integrates a company’s sales, marketing, and customer success teams, optimizing revenue generation by breaking down silos, improving collaboration, and streamlining process across all three functions. RevOps teams typically work on aligning sales and marketing strategy, implementing and optimizing sales process, managing and analyzing customer data, and applying technology to improve operational effectiveness. By aligning sales, marketing, and customer success, RevOps drives a cohesive, coordinated approach to revenue, better communication, fewer inefficiencies, and genuinely data-driven decisions. Coordinating across departments depends directly on the cleanliness and accuracy of the data those departments share, which is exactly where data hygiene comes in. RevOps recognizes that high-quality data is the precondition for good decisions, and works to cleanse and maintain data integrity, eliminating errors, duplicates, and inconsistencies along the way. Trent AllenRevenue Operations Manager, Maxio I think a big part is making sure all the different tools and systems are connected and that the data is passing between them fluidly, so that the end-user can save their time. How Can RevOps Facilitate Data Hygiene? A company’s data is like a garden, a vast expanse of potential that still requires meticulous care to actually thrive. RevOps steps in as the expert gardener, with the tools and strategy to keep data hygiene genuinely intact.  A few specific ways RevOps contributes: 1. Data Governance RevOps establishes data governance policies and standards across the organization, defining data quality metrics, validation rules, and ownership responsibilities. Clear guidelines are what make data management consistent and effective rather than ad hoc. 2. Data Integration and Alignment RevOps teams work to integrate data from sales, marketing, and customer success systems, identifying and resolving inconsistencies, redundancies, and inaccuracies as data from different departments comes together. This is what actually improves data integrity and produces a genuine single source of truth. 3. Data Cleanup and Enrichment Reviewing and updating customer and prospect information, eliminating duplicate records, and correcting errors or inconsistencies directly, this is what enhances data accuracy and reliability at the record level, not just in policy. 4. Data Analytics and Reporting Data analytics tools and techniques surface insight into customer behavior, revenue trends, and sales performance. Analyzing that data is also how RevOps identifies patterns, anomalies, and data quality issues in the first place, information that directly informs how to fix hygiene problems and improve overall data quality. 5. Training and Education RevOps trains employees across departments on data hygiene best practices, entry standards, maintenance procedures, and why data quality actually matters. Raising data literacy across the organization is what builds a genuine culture of data hygiene, rather than a policy nobody actually follows. Trent AllenRevenue Operations Manager, Maxio I think a big part is making sure all the different tools and systems are connected and that the data is passing between them fluidly, so that the end-user can save their time. Benefits of Having a Data Hygiene Strategy 1. Accurate Decision-Making Clean, accurate data is a reliable foundation for informed decisions. Trusting the data means trusting the insight built on top of it, at every level of the organization. 2. Improved Operational Efficiency Data hygiene minimizes errors, redundancies, and inconsistencies, which streamlines process and lets employees access and use relevant information quickly, saving real time and resources. 3. Enhanced Customer Experience Clean data gives a genuinely holistic view of the customer, supporting a personalized, tailored experience built on accurate understanding of their needs, preferences, and behavior. 4. Better Sales and Marketing Performance Clean, reliable data gives sales and marketing accurate insight into buying patterns and trends, enabling targeted campaigns, more effective lead generation, and better sales forecasting, which ultimately drives revenue growth. 5. Data-Driven Insights Data hygiene is what makes real data analysis and reporting possible. Clean data supports meaningful analytics, letting an organization identify trends, patterns, and opportunities that actually support strategic planning. 6. Compliance and Risk Mitigation Maintaining data hygiene matters directly for regulatory compliance, especially in industries with strict data protection and privacy requirements. Clean data reduces the risk of errors or breaches that could lead to real legal or financial consequences. 7. Cost Reduction Poor data hygiene wastes resources, time spent correcting errors or working around inaccurate information. Investing in real data hygiene practice reduces the costs tied

RevOps

14 RevOps Podcasts Worth Listening to in 2026

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14 RevOps Podcasts Worth Listening to in 2026 Revops 18 min Updated: August 10, 2026 It’s genuinely hard to keep up with everything new in RevOps, and podcasts remain one of the best ways to hear the function’s past, present, and future directly from the people building it. If you want sharper revenue operations and more effective growth strategy, here are 26 shows worth your time, updated for what’s actually still running in 2026. Get our latest insights into your inbox 1. The Revenue Lounge The Revenue Lounge is Nektar.ai’s podcast, hosted by Randy Likas. The show focuses on real-world revenue leadership, interviewing senior RevOps, GTM, and AI leaders to unpack what’s actually happening inside modern enterprise organizations. What you’ll learn: Real field insight into how RevOps actually works inside high-growth companies, straight from the leaders building it, at the intersection of RevOps, AI, data, and go-to-market strategy specifically. Why you should listen: The guest roster speaks for itself, leaders from Carta, Palo Alto Networks, AlphaSense, Miro, Socure, 6sense, Gong, G2, Asana, Nasdaq, ThoughtSpot, and Cohesity have all appeared. With 100+ episodes, 3,000+ subscribers, 5,000+ downloads, and more than a million impressions, it’s become a trusted platform for exactly this conversation. Recommended episode: Aligning AI Initiatives With Business Goals, with Tim Seamans, VP of Business Transformation, AI Acceleration at Mimecast, on how the company unified CRM, product usage, and acquisition signals into a single expansion workflow, attributing $2M in expansion revenue and $30M+ in pipeline within 80 days. Tim’s own framing: “We are driving our business using AI, not a side project run by a small team, but a company-wide operating shift with board-level sponsorship.” Links to listen: nektar.ai/podcasts 2. RevOps Champions Hosted by Brendon Dennewill, CEO and Co-founder of Denamico, a Diamond HubSpot Solutions Partner based in Minneapolis, RevOps Champions targets customer success and RevOps teams leveraging technology to drive alignment across people, process, and tech. What you’ll learn: How to harmonize people, process, and technology in a scaling RevOps function, drawn from conversations with a different RevOps leader each episode. Why you should listen: It’s one of the more consistently active shows in this category, still publishing current episodes as of late 2025, with a format built specifically around practical, technology-forward RevOps advice. Recommended episode: “The RevOps Playbook: Mastering The Three Critical Elements,” with Alison Elworthy, EVP of Revenue Operations at HubSpot, a strong primer on RevOps fundamentals from one of the function’s most recognized practitioners. Links to listen: Apple Podcasts | Homepage 3. The GTMnow Podcast Produced by GTMnow, the media brand of venture fund GTMfund, this show features conversations with tech executives, VCs, and founders who’ve actually built fast-growing software companies, hosted primarily by Scott Barker, with co-host Sophie Buonassisi joining on rotating episodes. What you’ll learn: Unshared, specific detail on what worked and what didn’t in scaling a company’s go-to-market motion, drawn from operators GTMfund’s own network of 350+ GTM executives (from companies including DocuSign, Salesforce, LinkedIn, Snowflake, Okta, and Zoom) actually trusts. Why you should listen: Scott Barker previously ran revenue and partnerships for the original Sales Hacker (acquired by Outreach in 2018), so this show carries forward much of that same practitioner-first spirit under a new name and a VC-backed media operation. Recommended Episode: “GTM 96: The Three Pillars of a Modern Go-To-Market Strategy Every Revenue Leader Should Know,” with Kelly Hopping, covering brand building, customer-centric marketing, and the power of organic search. Links to listen: Apple Podcasts | Substack About the host: Scott Barker is a Partner at GTMfund, where he leads fundraising and runs the firm’s media arm. LinkedIn 4. The RevOps Show Hosted by Doug Davidoff and Jess Cardenas, this show works through the common scenarios, questions, and real problems companies actually face in revenue operations, with a format built around practical process over abstract theory. What you’ll learn: How to integrate sales, marketing, and customer success functions effectively, with a particular focus on turning overwhelming, inefficient reporting into something genuinely actionable. Why you should listen: Doug and Jess’s back-and-forth keeps genuinely dry RevOps subject matter engaging, a real differentiator in a category that can otherwise feel like a lecture. Recommended Episode: “Reporting and Analytics: From Overwhelming and Inefficient to Valuable and Actionable,” on transforming a chaotic reporting process into a streamlined, decision-ready one. Links to listen: Apple Podcasts | Spotify 5. AI to ROI (formerly Metrics That Measure Up) This show has been through a real, multi-step evolution, from “SaaS Talk with the Metrics Brothers” to “Metrics That Measure Up” to its current identity, AI to ROI, hosted throughout by Ray Rike, Founder and CEO of Benchmarkit. The current focus: how enterprises actually translate AI investment into measurable business value, a natural evolution of its original data-and-metrics DNA. What you’ll learn: How senior enterprise leaders are measuring AI ROI in practice, alongside the SaaS metrics and benchmarking content the show built its original reputation on. Why you should listen: Few shows in this category have tracked the industry’s actual shift from “measure your SaaS metrics” to “measure your AI ROI” this directly, making it a useful barometer for how the conversation itself has changed. Recommended Episode: “Measuring the ROI of Transitioning from Outbound to Inbound GTM,” with Aviv Canaani, CRO at Datarails. Links to listen: Apple Podcasts About the host: Ray Rike is Founder and CEO of Benchmarkit, a B2B SaaS benchmarking and metrics platform. 6. GTM Science A show for GTM and RevOps leaders that goes deep on which AI implementations actually drive revenue impact versus which just save time, with a real focus on separating substance from hype. What you’ll learn: The difference between AI that genuinely moves revenue (autonomous SDR agents booking qualified meetings, AI-powered competitive intelligence surfacing opportunities reps would miss manually) and AI that just looks impressive in a demo, plus what has to be true organizationally before AI can actually make an impact. Why you should listen: The show doesn’t shy away from the hard part of the AI conversation, why most

15 Sales metrics every revops leader
CRM

15 Sales Metrics Every Revenue Operations Leader Should Track

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15 Sales Metrics Every Revenue Operations Leader Should Track Sales, Revops 12 min Updated: August 10, 2026 If you’re in revenue operations, you already have more sales data within reach than you can realistically act on. The real question isn’t which data exists, it’s which numbers, tracked consistently, actually move revenue when you act on them. Tracking the right sales metrics helps you redefine your sales process and build strategies that increase revenue. Before getting into the specific fifteen, it’s worth being clear on what a sales metric actually is, and how it differs from a KPI. Get our latest insights into your inbox What Are Sales Metrics? Sales metrics are data points that show the sales performance of an individual, a team, or an organization. They tell you how well your sales initiatives are actually working. A metric falling outside its normal range signals a problem needing attention, and the same number usually points toward the fix. Sales Metrics vs. Sales KPIs The two terms get used interchangeably, but they’re not the same thing, and conflating them can distort your revenue strategy. KPIs, key performance indicators, are laser-focused on specific goals and objectives, acting as a compass measuring performance against a strategic target you’ve set. Sales metrics are numbers tracked over time that can be quantified into useful figures, used as guidance and benchmarks for growth. A metric can exist without a target attached to it, a KPI can’t. Every KPI is a metric. Not every metric is a KPI. If a business aims to grow sales 20% by capturing more leads, sales qualified leads (SQLs) might be the KPI, while sales revenue is the broader metric it rolls up into. Why Should RevOps Teams Track Sales Metrics? Tracking sales metrics gives revenue leaders a clear read on what’s working in the current sales process and what isn’t. Gaps revealed by the data are what let RevOps teams build real optimization strategies rather than guess. Cliff SimonCRO, Carabiner Group The must-have metrics always have to scale back to the actual company metrics. So the first and most important thing is having an understanding of your current state. Where are you today? Being real about those numbers and not fluffing them up. And then starting to track the progression over time. Metrics also show you where ROI is highest, and where you’re missing chances to grow revenue. Tracked over the long term, they’re a solid indicator of overall sales performance, customer satisfaction, and how efficiently your team is actually running. A declining quota attainment number, for instance, is a prompt to investigate why, and pivot strategy so reps can close more. Tracking sales metrics helps you: Improve team performance by addressing real bottlenecks Optimize sales processes by showing which strategies actually work Explore new opportunities in under-served areas Improve accountability across reps and managers Target sales coaching where it’s actually needed Keep buyers and sellers on the same page What Sales Metrics Should RevOps Teams Track? Which metrics matter most depends on your growth stage, your resources, and the strategic goals you’ve already set. If one of your goals is full quota attainment across the team, you’ll want to track sales activity metrics (calls, emails, follow-ups) alongside it. The metrics you track should always scale back to actual company goals, not exist in isolation. Keep it simple, focused, and targeted at genuinely meaningful data. Here are fifteen worth tracking, organized by what they actually tell you. 15 Sales Metrics to Track 1. Annual Recurring Revenue (ARR) ARR is the sales metric for subscription businesses, calculating the revenue a company expects to generate from customers annually. It’s predictable, expected to recur at regular intervals, and can be segmented by location, customer type, or product line to understand performance across each. It’s also useful for measuring value added through new sales, renewals, and upgrades, and value lost through downgrades and churn. Annual recurring Revenue (ARR) = Total Contract Value / Number of Years in the Contract For example, a $5,000 contract signed for 5 years produces an ARR of $1,000 per year. Monthly Recurring Revenue (MRR) is the same concept applied to shorter-term subscriptions, tracked monthly instead of annually. 2. Average Deal Size Average Deal Size is total revenue generated in a given period, divided by the number of closed-won opportunities in that same period. It helps project revenue and estimate how many deals a team needs to close to hit quota. Reviewing average deal size by rep also surfaces which large deals need close monitoring, or which reps need coaching to close one successfully. Average Deal Size = Total Revenue / Number of Closed-Won Deals Four deals closing at $20,000, $30,000, $10,000, and $20,000 in a quarter produce an average deal size of $20,000. 3. Average Revenue Per User (ARPU) ARPU, sometimes ARPA (Average Revenue Per Account), is the revenue a company generates per user or account in a given period. Rising ARPU suggests customers are increasingly willing to pay; falling ARPU might prompt a team to offer a higher-value tier or add-on to the existing subscription. Segmenting ARPU by location or customer group shows which segments generate the most revenue and which need improvement. ARPU = Total Revenue / Number of Customers $300,000 in total Q2 revenue across 3,000 customers produces an ARPU of $100. 4. Average Profit Margin Average Profit Margin measures how much of overall sales revenue actually converts into profit, what’s left after business expenses. It reflects both pricing strategy and cost efficiency, and can be measured across segments like product line, service, or geography. Average Profit Margin = Net income Net Sales x 100 $100,000 in net income against $400,000 in net sales for a specific product and territory produces a 25% profit margin. 5. Win Rate Win Rate is the percentage of proposals made that convert into actual sales. Calculating win rate per rep lets managers track individual performance and estimate how many future opportunities are needed to hit target. Win Rate = Deals

Nektar-Gong
Product

Nektar + Gong Integration

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Nektar & Gong Integration: Bring Every Sales Conversation Into Your Complete Customer Story Product Update 5 min August 10, 2026 We’re excited to announce that Nektar now integrates with Gong, making it easier for revenue teams to unify conversation intelligence with every other customer interaction. The integration automatically captures Gong call data, including participant attendance, call duration, and meeting transcripts, and combines it with email, calendar, meeting, and CRM activity already flowing through Nektar. The result is a complete customer engagement timeline that gives RevOps, Sales, Marketing, Customer Success, and AI agents far more context than conversation data alone. Whether you’re trying to improve CRM completeness, understand buying committee engagement, measure campaign influence, or power AI with better customer data, the Nektar + Gong integration brings another critical source of customer intelligence into your GTM data foundation. Get our latest insights into your inbox Why integrate Gong with Nektar? Sales conversations contain some of the most valuable information about a deal. They reveal customer pain points, objections, buying signals, and next steps. But that information often stays inside Gong while the rest of the customer journey lives elsewhere. With the Nektar integration, Gong conversation data is automatically captured and added to the same engagement layer as every other customer interaction. For every synchronized Gong meeting, Nektar captures: The participants who actually attended the meeting. The duration of the conversation. The complete meeting transcript. Attendance information reflects who joined the call, not just who accepted the calendar invitation, giving revenue teams a much more accurate view of stakeholder participation throughout the buying process. This works across Gong-recorded meetings on Google Meet, Zoom, and Microsoft Teams, bringing conversation intelligence together regardless of which meeting platform your teams use. View full Feature Comparison Nektar vs Gong: What’s the difference? Conversation intelligence is only one piece of customer intelligence. Compare both platforms side by side to understand what each one captures, where they overlap, and where they don’t. Turn conversations into structured business insights Capturing conversation transcripts is only the beginning. The real value comes from turning those conversations into structured data that revenue teams can act on. With Nektar, organizations can automatically extract insights from Gong transcripts based on their own business processes and map those insights into Salesforce. Instead of manually reviewing calls or relying on ad hoc notes, teams can standardize the information they care about and make it available across their CRM. For example, businesses can capture and populate fields such as: Competitors mentioned during sales conversations Product features discussed Customer objections or risks Next steps and agreed action items Budget, timeline, or implementation signals Any custom insight specific to their sales process Because these insights are written directly into Salesforce, RevOps teams can build dashboards, reports, workflows, and automations using conversation data that would otherwise remain locked inside transcripts. Instead of treating conversations as unstructured text, Nektar turns them into structured, reportable CRM data that can be analyzed alongside every other customer interaction. What can you build with Gong Data? By combining Gong conversation data with engagement across email, calendars, meetings, and CRM activity, Nektar gives revenue teams a much more complete understanding of customer relationships. Campaign-to-conversation attribution Marketing teams can finally connect demand generation efforts with sales conversations. Instead of manually matching campaign reports against Gong recordings, teams can see which campaigns, webinars, events, or content pieces ultimately resulted in customer conversations and pipeline progression. That creates much clearer visibility into how marketing engagement influences revenue beyond the initial lead conversion. Cross-channel engagement scoring Nektar combines Gong participation data with email responsiveness, meeting attendance, and other customer interactions to calculate engagement scores for both contacts and accounts. These scores reflect how customers engage across every touchpoint instead of relying on a single communication channel, making it easier to identify highly engaged buyers, stalled opportunities, and disengaging stakeholders. Buying committee visibility Modern B2B purchases involve multiple decision makers, influencers, and champions. Because Nektar combines Gong attendance data with CRM relationships and every other customer interaction, teams can understand how deeply an opportunity is multithreaded, which stakeholders remain engaged, and where additional relationship building is needed before a deal reaches a critical stage. Build AI on complete customer context As organizations adopt AI across sales, RevOps, and Customer Success, customer context becomes increasingly important. Conversation transcripts are valuable, but they don’t tell AI what happened before the meeting or how customer engagement changed afterward. By bringing Gong conversation data together with every other customer interaction, Nektar creates a richer customer intelligence layer that AI agents can use for forecasting, deal inspection, account planning, buying group analysis, customer health monitoring, and revenue intelligence. Instead of asking AI to reason over fragmented systems, organizations can provide a complete view of customer engagement across the entire buying journey. Connect Gong in minutes Availability Note: The Gong connector will be available in the Nektar Connectors UI in the upcoming release. Until then, if you’d like to enable the Gong integration, please reach out to your Customer Success Manager, who will help you get started. The Gong integration uses OAuth authentication, making setup both secure and straightforward. Getting started only takes a few steps: Open the Connectors page in the Nektar dashboard. Select the Gong integration and click Connect. Authenticate with Gong and approve the required permissions. Choose which Gong users should synchronize. Start automatically capturing conversation data. Administrators can independently control call synchronization and transcript synchronization for each user, providing flexibility over which information is ingested while maintaining centralized governance. Once connected, Nektar continuously synchronizes Gong data without requiring any additional work from sales representatives. Now available on the Gong Marketplace To make deployment even easier, the Nektar integration is now live on the Gong Marketplace under the AI Platform category. Organizations already using Gong can discover Nektar directly through the Marketplace, authorize the integration using OAuth, and begin enriching their customer engagement data in just a few minutes. Whether your goal is improving CRM completeness, understanding buying committee engagement, measuring campaign influence,

CRM

How to Stop Your Reps From Dreading CRM Data Entry

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How to Stop your Reps From Dreading CRM Data Entry CRM 10 min Updated: August 7, 2026 CRM adoption is one of the most reliable ways to make a revenue leader wince. CRM implementation failure rates run as high as 55%, and poor user adoption, not a software limitation, is consistently cited as the primary cause. The single biggest driver of that poor adoption is manual data entry, the task reps resent most and the one most directly responsible for a CRM quietly falling out of use. The real cost is sharper than “reps don’t like typing.” Recent research puts the share of opportunity-related activity data that never makes it into the CRM at all at 79%, lost not because reps are careless, but because manual entry is structurally unreliable at the volume and pace modern selling actually requires.  Revenue leaders have to treat CRM usage as something reps find genuinely valuable, not a compliance task, and that starts with understanding exactly why reps dread it in the first place. Get our latest insights into your inbox Why Reps Dread CRM Data Entry 1. Disconnect from selling When reps spend a major chunk of their day punching data into the CRM, they feel pulled away from the actual job, selling and building relationships with customers. Time spent on data entry is time not spent in front of a prospect, and reps notice that tradeoff directly. 2. Perceived lack of value Many reps struggle to see a direct line between data entry and closing deals. If the benefit of the work isn’t obvious, it feels mundane and unrewarding, which breeds exactly the reluctance that makes CRM data unreliable in the first place. 3. Time-consuming and tedious by nature 32% of sales reps spend more than an hour a day on manual data entry, and that time comes directly out of the day they’d otherwise spend selling. Repetitive, detail-heavy work that has to be done carefully and doesn’t feel like progress is a recipe for reduced job satisfaction, regardless of how necessary the task actually is. 4. Increased workload on an already demanding schedule Sales reps carry some of the most demanding schedules in a company, and CRM upkeep sits on top of it as an additional burden rather than a core part of the job, creating a real sense of overwhelm when the two compete for the same hours. 5. Data privacy concerns Handling customer data carries real responsibility, and reps are conscious of the consequences of mishandling sensitive information or sharing something they shouldn’t. That awareness adds a layer of caution and stress to a task that’s already unwelcome. 5 Ways to Stop Reps From Dreading CRM Data Entry 1. Simplify the process, and automate what you can The most effective fix for reps’ fear of data entry is removing the entry itself. Automation tools work quietly in the background, capturing activity without requiring a rep to manually input it, freeing up meaningful time every week that would otherwise go to typing updates into fields. Mobile-compatible tools that let reps update information on the go help close the remaining gap without adding friction. 2. Incorporate voice-to-text and AI assistants Typing detailed notes after every call or meeting is a genuine time sink. Voice-to-text functionality lets reps dictate interactions, follow-ups, and insights directly, and current AI assistants can transcribe and categorize that input accurately, preserving data integrity without asking a rep to type a word. 3. Integrate the CRM with the rest of the sales stack Connecting the CRM to other sales tools closes gaps by eliminating duplicate manual effort and giving a genuinely holistic view of customer interactions. A meeting scheduled on a calendar should update the relevant contact’s record automatically. An email sent from a connected inbox should log itself against the right opportunity without a rep copying and pasting it in. 4. Use real-time alerts instead of a dashboard nobody opens Real-time alerts and notifications prevent data entry and follow-up tasks from piling up unnoticed. Nektar Buzz, for instance, pushes the right insight to the right person at the right time, directly into Slack or Microsoft Teams, so reps get alerted about deal activity without adopting yet another dashboard they have to remember to check. 5. Show reps the actual value of the data they’re generating Communicating why accurate, timely CRM data matters, and sharing real examples of how it directly contributed to closing a specific deal or catching a specific risk early, turns data entry from an abstract compliance task into something reps can see the point of. Ownership follows once the value is genuinely visible, not before. Why You Should Care About Accurate CRM Data Data entry alone isn’t enough. The data has to actually be accurate once it’s in the system, and accurate data changes outcomes in ways that compound. Higher rep productivity. Removing the burden of manual entry gives reps back time for the activities that actually generate revenue: relationship-building, opportunity identification, and strategy, rather than admin. Clean insights. Reliable data gives clear visibility into which deals in the pipeline actually need attention, letting reps and managers spot bottlenecks and prioritize the opportunities most likely to close, rather than guessing. Better sales coaching. Accurate data lets managers pinpoint exactly where a rep or a stage in the pipeline is actually struggling, targeting coaching at a real, specific gap instead of generic advice. More closed deals. Well-organized data directly supports faster, more efficient prospecting and closing, which shows up in the only metric that ultimately matters: revenue. Higher ROI from the CRM itself. A CRM investment, Salesforce or otherwise, only pays off when the data inside it is actually trustworthy. 76% of CRM users report that less than half of their organization’s CRM data is accurate, which means most companies are working from a system that isn’t yet delivering the return it was bought to provide. Tools that maintain clean data with zero rep adoption required are what actually close that gap. Why This

Maintaining SF Data Hygiene
AI, CRM

Top 10 CRM AI Use Cases for 2026

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10 CRM AI Use Cases for 2026 CRM 12 min Updated: August 7, 2026 91% of companies with more than 11 employees use a CRM. The gap between adopting a CRM and actually running AI on top of it well is still real: only 24% of B2B suppliers currently run true agentic AI, the autonomous, workflow-driving kind that actually replaces manual processes, even though 45% say they use some form of AI in their sales function. Most of that gap is point-tool automation dressed up as transformation. What’s changed since this category was first written about is the shape of the ambition. The conversation used to be “can a CRM chatbot answer a support question.” It’s now “can an AI agent update a record, prioritize an account, and trigger a workflow inside Salesforce without a person reviewing it first.”  Gartner projects 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5% just two years ago. This guide covers what AI in CRM actually means today, and 10 real, current use cases, including where Nektar fits into several of them directly. Get our latest insights into your inbox What Is AI in CRM? A CRM manages relationships with customers, prospects, and other business contacts. AI in CRM means integrating AI technologies into that system to analyze customer data, predict behavior, automate tasks, and personalize interactions, moving a CRM from a passive record-keeping system toward one that actively surfaces insight and, increasingly, takes action on its own. The distinction that matters most in 2026 is between AI that assists a person (drafting an email, summarizing a call) and AI that acts autonomously (updating a field, triggering a workflow, prioritizing an account without a human approving each step).  PwC’s survey of 308 senior executives found 79% say AI agents are already being adopted at their companies, and 66% of those report measurable productivity gains, a genuinely strong result, but one concentrated specifically among companies running the second kind of AI, not the first. 10 CRM AI Use Cases for 2026 1. Automated Contact and Activity Capture AI can build comprehensive contact lists for every account by extracting them directly from a rep’s email inbox, calendar, and meetings, rather than relying on manual entry. Contacts and Opportunity Contact Roles get categorized by actual engagement and relevance to a live opportunity, and enriched automatically with current job titles and phone numbers as they change. Mimecast used exactly this kind of automated telemetry to identify $80M in pipeline and $2M in incremental expansion revenue within 80 days, signals that were sitting in email and meeting data the whole time but had never been structured or surfaced before. Why it matters: Every use case below (scoring, forecasting, personalization) is only as good as the underlying activity data. A predictive model reasoning over incomplete contact data produces a confident, plausible-sounding answer that may have nothing to do with what’s actually happening in the account. 2. Agentic SDR and Outreach 41% of marketing organizations now run at least one SDR agent, and companies running agentic outreach report roughly 19% of net-new pipeline sourced through it, with 2 to 3x improvements in pipeline velocity compared to manual prospecting alone.  SaaStr’s own published experiment running an inbound AI agent generated $1M in closed revenue within 90 days, with 71% of that quarter’s closed deals sourced from AI-qualified inbound leads, though SaaStr’s founder Jason Lemkin has also been candid that fully autonomous outbound agents perform “better than a mid-pack rep, but not better than a top performer,” a useful caution against over-claiming what this category actually replaces. Why it matters: This only works well when the agent has real, current account and contact data to personalize from. An agent working from a stale or incomplete CRM record produces generic outreach that undermines the exact personalization it’s supposed to deliver. 3. Qualified Pipeline Expansion AI can detect the absence of pre-engaged contacts or leads within the CRM and run targeted, compliant outreach campaigns to expand the pipeline and shorten sales cycles, identifying contact roles automatically to sharpen targeted outreach rather than a generic blast.  Qualified’s published case study on Demandbase’s deployment of its AI SDR reports 2x pipeline sourced and 2x more meetings from target accounts, while saving roughly 100 SDR hours and $80,000 in costs per month, a concrete illustration of expansion built on data the team already had rather than a new list purchased from outside. Why it matters: Expansion built on incomplete contact data just recycles the same blind spots at greater volume. The gains above depend on the underlying account and role data being accurate before the campaign logic runs on top of it. 4. AI-Powered Account-Based Marketing Recover inactive and lost deals, and influence active opportunities, by running ABM campaigns against current, first-party buyer contacts sourced directly from sellers’ inboxes and calendars rather than purchased third-party lists. Precisely targeting buyers based on real engagement within high-priority accounts, their actual buying role, and current sales stage measurably increases funnel conversion versus a generic account list. Palo Alto Networks saw a 15x pipeline impact after moving from MQL-centric marketing to a genuine buying-group model built on this kind of first-party engagement data. Why it matters: ABM targeting built on firmographic fit alone misses the signal that actually predicts conversion, which stakeholders are engaging right now, and how deeply. That signal only exists if the underlying activity data is captured in the first place. 5. Predictive Analytics and Churn Prediction AI algorithms can predict customer behavior, flagging potential churn risk or purchase intent before it becomes obvious, so teams can act proactively rather than reactively. Zendesk describes predictive prioritization, ranking accounts by usage intensity, ticket volume, sentiment, and communication frequency, as the single highest-impact CS use case, since it’s what actually changes a CSM’s day-to-day motion: which accounts need attention now, which can run on automation, and which are healthy. Why it matters: This depends entirely on having enough real behavioral and

Maintaining SF Data Hygiene
Salesforce

Maintaining Salesforce Data Hygiene: Do You Trust Your SFDC Data?

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Maintaining Salesforce Data Hygiene: Do You Trust Your SFDC Data? Salesforce 9 min Updated: August 7, 2026 As a Salesforce user, you already know how important it is to keep your data clean and current. Maintaining that hygiene can be tedious and time-consuming, but it’s a task that has to be addressed to actually maximize the value of your Salesforce investment. Understanding why Salesforce data hygiene matters, and how it impacts the business, is genuinely critical, and that’s exactly what this guide covers, along with practical, actionable tips for keeping data accurate and trustworthy. Get our latest insights into your inbox What Does Salesforce Data Hygiene Mean? Salesforce data hygiene is the ongoing process of keeping Salesforce data accurate, complete, and current. Good hygiene is what makes the information trustworthy enough to actually inform business decisions. Without it, decisions get made on incomplete or inaccurate data, and Gartner’s widely cited estimate puts the average cost of poor data quality at $12.9 million per organization annually. Good Salesforce data hygiene practice generally involves: Regularly reviewing and cleaning up data Making sure every field is filled in correctly Removing duplicates Updating records as circumstances change Setting up real processes and automation, rather than relying on manual review alone Think of Salesforce data hygiene like brushing your teeth: not the most exciting task on the list, but skipping it consistently causes real, compounding damage over time. Why Does Salesforce Data Hygiene Matter? Data hygiene touches every part of the business. Sales, marketing, customer service, and support all depend on accurate, complete data to make good decisions, and research on the cost of poor data quality puts the impact at roughly 15-25% of a business’s revenue. Consider a sales leader at a company that relies heavily on Salesforce to manage its pipeline, preparing for an important team meeting to review progress against quarterly goals. Pulling reports to gather the data, they notice real discrepancies: accounts with missing information, contacts with incorrect email addresses, opportunities still marked open weeks after they actually closed. Data hygiene in Salesforce is critical precisely because it directly impacts business growth and success. An automated data hygiene policy is what turns Salesforce data into a genuine asset that helps a team hit its goals, rather than a liability quietly holding the business back. Perils of Bad Data in Salesforce A few specific hazards that bad data creates for sales teams: 1. Long Sales Cycles Bad data makes deals take longer to close. Reps end up chasing leads that were never actually qualified, or pursuing deals with no real chance of closing, time that’s especially costly given the average B2B sales cycle already runs several months long. 2. Stalled Deals Inaccurate, outdated information causes deals to stall or collapse outright. A team unaware that a key decision-maker has left a prospect’s company, for instance, may keep pursuing a deal that was never going to close, wasting effort that could have gone toward a live opportunity instead. 3. Inaccurate Forecasts Forecasts built on inaccurate data throw off the entire sales strategy built on top of them, leading to missed revenue targets, thinner margins, and a genuine lack of visibility into what’s actually happening in the pipeline. 4. Poor Customer Experiences Outdated, inaccurate customer information leads directly to mistakes, sending the wrong product, missing a support ticket follow-up, that damage the relationship. These mistakes compound into unhappy customers, negative reviews, and real lost business. 5. Churn Teams working from inaccurate customer data struggle to identify and address dissatisfaction before it’s too late to act on it, which shows up eventually as lost revenue and a damaged reputation. 5 Best Practices to Ensure Salesforce Data Hygiene 1. Conduct Regular Data Audits Auditing Salesforce data regularly is essential to catching issues before they compound. Start by reviewing data fields, identifying duplicates, and cleaning up outdated or inaccurate information, on a real, recurring cadence rather than a one-time project. 2. Automate Data Cleaning Processes Automation is one of the most reliable ways to keep Salesforce data hygiene intact over time. Tools that automatically detect and remove duplicates, validate data, and standardize formats save real time and keep data consistently clean, without depending on someone remembering to do it manually. 3. Adopt a Minimalist Data Stack Collecting and storing only what’s genuinely essential for business operations reduces the risk of errors and simplifies data management considerably. More data isn’t automatically more valuable, especially if most of it never actually gets used. 4. Enforce Quality Standards Establishing and enforcing real data quality standards, validation rules, data entry guidelines, proper training on how to input and manage data, keeps information consistent and reliable whenever it’s actually needed. 5. Delete Unnecessary Data Regularly removing outdated, duplicate, or no-longer-relevant records reduces clutter and streamlines data management. A CRM doesn’t need to keep everything forever to be useful, it needs to keep what’s actually current and relevant. Supercharge Your CRM Data With Nektar Nektar’s Data Foundation enables AI-assisted automation that keeps CRM data genuinely data-packed and reliable. Nektar automates the process of enriching Opportunity Contact Roles on Salesforce, no more spending hours manually updating contact roles, it happens automatically in the background. Buying Group Intelligence goes further, automatically building out the buying committee map as an opportunity progresses, a genuine advantage for businesses navigating complex sales cycles with multiple decision-makers. Daisy AI keeps learning and adapting as CRM data changes, ensuring everything stays current and accurate, particularly important for businesses handling large data volumes that need to stay reliably up-to-date. If Salesforce data hygiene is on your radar this quarter, give Nektar a try. Frequently Asked Questions Q. How do I clean up data in Salesforce? Tools like Nektar’s Data Foundation help identify and remove duplicate records, standardize data formats, and improve data accuracy automatically, without requiring a manual cleanup project to get there. Q. What is dirty data in Salesforce? Dirty data refers to inaccurate, incomplete, or inconsistent data, records that can negatively impact sales forecasting, customer relationship management, and

10 Best Sales Intelligence Tools
Sales

10 Best Sales Intelligence Tools for 2026

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10 Best Sales Intelligence Tools for 2026 Sales Tech Stack 12 min Updated: July 30, 2026 Sales intelligence tools and CRMs play different roles in supporting sales. A CRM nurtures customer relationships and tracks interactions; sales intelligence tools give sales teams and leaders the data insight to actually improve performance, how buyers make decisions, how prospects behave at each stage, which tech stack a target account runs, and how to build a forecast and pipeline you can actually trust. The category has consolidated meaningfully since this list was first written. Several tools that used to be independent are now subsidiaries of larger platforms, and the AI layer on top of sales intelligence has moved from a novelty to table stakes. This guide covers what a sales intelligence tool actually is, the current state of 11 major players, and how AI has changed what “good” sales intelligence needs to mean. Get our latest insights into your inbox What Is a Sales Intelligence Tool? A sales intelligence tool gives you the data and insight to make smarter sales decisions: generating high-quality leads, defining your ideal customer profile, and keeping that data accurate and current. The best ones don’t operate as a standalone island, they integrate with your existing CRM and sales stack, so the intelligence they surface actually reaches the workflow a rep is already using rather than sitting in a separate dashboard nobody checks. 10 Best Sales Intelligence Tools for 2026 Nektar, GTM data foundation and AI signal layer LinkedIn Sales Navigator, social and professional relationship intelligence HubSpot Sales Hub, CRM-native sales intelligence and automation ZoomInfo Chorus, conversation intelligence backed by B2B data Traq.ai, AI-powered call recording and coaching Winmo, media and advertising sales intelligence Apollo.io, prospecting, engagement, and sales intelligence in one platform Demandbase One, account-based intelligence and orchestration 6sense, predictive account intelligence and journey orchestration Cognism, compliant contact data and intent signals Overview of the 10 Best Sales Intelligence Tools 1. Nektar Nektar’s role in sales intelligence is upstream of most tools on this list: every platform is only as good as the underlying CRM data it’s reasoning over, and most CRMs are missing a large share of what’s actually happening in a deal. 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, something no other tool on this list does. Daisy AI then surfaces 39 signals across categories including buyer visibility, deal risk, and rep performance, directly on the Salesforce Opportunity tab rather than a separate interface. Key features: zero-rep-effort activity capture across email, calendar, meetings, and calls; automatic buying-group mapping with persona and role enrichment; Time Travel retroactive correction of historical CRM records; Daisy AI signal library spanning deal risk, buyer engagement, and rep performance; vendor-neutral integration alongside your existing sales stack. Pricing: Custom, based on team size and scope. A free CRM scan shows how much of your own pipeline activity is currently missing before you commit to anything. Best for: Salesforce-first revenue teams that need the underlying deal data reliable enough for both reps and any AI layered on top of it. 2. LinkedIn Sales Navigator LinkedIn Sales Navigator helps you find and connect with the people who matter most to your business on LinkedIn specifically, using advanced search and lead recommendations to surface prospects based on criteria you actually care about. Key features: personalized lead recommendations, advanced search filters across LinkedIn’s full network, InMail messaging to reach prospects outside your existing connections, real-time alerts on saved leads and accounts, CRM sync for Salesforce and Dynamics. Pricing: Three tiers (Core, Advanced, Advanced Plus), plus a free trial. Current pricing is best confirmed directly with LinkedIn, since tiers and included seats have shifted more than once in recent years. Best for: Prospecting and warm-path discovery through LinkedIn’s own network graph, best paired with a CRM-native tool rather than used as a standalone system of record. 3. HubSpot Sales Hub HubSpot Sales Hub is a full sales platform combining CRM, sales analytics, email tracking, and automation in one place, with an increasingly broad AI toolset layered on top for outreach drafting, prospect research, and deal scoring. Key features: sales analytics and forecasting, contact and deal management, email tracking and sequencing, sales automation for repetitive tasks, AI-assisted lead and deal scoring. Pricing: Free CRM tier available; paid tiers (Starter, Professional, Enterprise) scale with seats and automation depth. Confirm current tiers directly with HubSpot, since per-seat pricing and included features have changed structurally since 2023 and a fixed table here would likely be stale again quickly. Best for: Teams already running HubSpot as their CRM who want sales intelligence and automation bundled into the same platform rather than a separate tool. 4. ZoomInfo Chorus Chorus has been part of ZoomInfo since 2021 (not Zoom, a common mix-up) and now runs on ZoomInfo’s broader B2B data layer, the GTM Context Graph. It records and transcribes sales calls, using AI to detect sentiment, keywords, and key moments in a conversation, giving managers real-time coaching visibility into calls as they happen. Key features: call recording and transcription, real-time sales coaching during live calls, AI-detected sentiment and keyword analysis, conversation analytics backed by ZoomInfo’s B2B data layer. Pricing: Custom, quote-based. Best for: Teams wanting conversation intelligence with the added weight of ZoomInfo’s data behind it, particularly if you’re already on ZoomInfo for prospecting. 5. Traq.ai Traq.ai is an AI-powered platform that records, transcribes, and analyzes sales calls, extracting objections, opportunities, and next steps automatically rather than requiring a rep to take notes during the call itself. Key features: automated call and video recording and transcription, AI-driven buyer sentiment and risk analysis, centralized deal intelligence storage, custom analysis tailored to your specific industry or sales process, integrations with HubSpot, Zoho, Microsoft Teams, and Google Meet. Pricing: A free tier is available; paid individual and professional monthly plans exist, with enterprise pricing available on request.

what is revenue operations
RevOps

What Is Revenue Operations and Why Is It So Important?

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What Is Revenue Operations and Why Is It So Important? RevOps 9 min July 16, 2026 Revenue operations (RevOps) is an operating model that runs sales, marketing, and customer success as one connected system with shared data, shared goals, and shared accountability. Its job is to make revenue predictable by closing the gaps where deals, data, and context get lost between teams. RevOps has gone from an emerging idea to something close to the default operating model in B2B. A 2026 survey of over 1,200 B2B companies found 78% now have a dedicated RevOps function, up from 48% in 2023 and just 30% in 2021. The remaining companies without one are disproportionately early-stage (sub-$5M ARR), where operations responsibilities are still distributed across individual department heads rather than unified.  The trajectory is clear even if the exact endpoint isn’t. RevOps has moved from a bet growth-stage companies made to a baseline expectation. Get our latest insights into your inbox What is Revenue Operations? RevOps is an end-to-end operating model that aligns sales, marketing, and customer success around a shared view of the customer and shared accountability for revenue, instead of three departments each running their own tech stack, their own metrics, and their own version of what’s actually happening with a given account. Historically, these functions operated in silos: marketing generated leads and handed them to sales with little context, sales closed deals and handed customers to CS with even less, and each team was measured on its own slice of the funnel rather than the outcome as a whole. That structure made sense when the buyer’s journey was simpler and more linear. It doesn’t hold up against a B2B buying process where the average committee runs 6 to 10 stakeholders, deals loop rather than progress in a straight line, and most of the buyer’s research happens before a rep is even in the room.  RevOps exists because no single function can own an outcome that complex alone anymore. Read the Blog Are you a first-time RevOps Leader? If you’re building a RevOps function from scratch, this 30-60-90 day playbook will guide you The Four Pillars of Revenue Operations Most current RevOps frameworks converge on the same four pillars, each acting as a load-bearing part of a predictable revenue engine: 1. Process The workflows, handoffs, and stage definitions that move a prospect from first touch to closed revenue and beyond: lead routing, opportunity stage criteria, renewal and expansion motions. These need to be consistent and documented, not reinvented by each rep or team lead, or the process itself becomes a source of variance rather than a source of predictability. 2. platforms The technology stack: CRM, marketing automation, sales engagement, customer success tooling, that runs the process above. RevOps owns the decisions about which tools to add, and just as importantly, which to consolidate: 67% of RevOps leaders name tech stack consolidation their top priority for 2026, a sharp reversal from the “add a tool for every new problem” instinct that defined the last several years of GTM tech buying. 3. Data Clean, complete, and connected data across every customer-facing system is the foundation the other three pillars run on. And it’s the pillar that’s changed the most since this post was first written.  It used to be enough to say “data quality matters.” In 2026, the more specific and more useful framing is data completeness as a measurable RevOps metric in its own right: teams that actively track and manage CRM data completeness see 23% higher win rates than teams that don’t, because reps work from better information, automation runs on a foundation that’s actually accurate, and forecasts reflect what’s really in the pipeline rather than what got manually logged. 4. People The team responsible for running all of the above is the fourth pillar. Sizing varies by company, but a common current benchmark is roughly one RevOps professional per 25-30 revenue team members, with top-performing organizations investing closer to 1-in-15-20. Regardless of team size, RevOps only works if the rest of the organization trusts the data and processes it produces, which is a change-management problem as much as a technical one. Why Revenue Operations Matters More in 2026 The basic case for RevOps hasn’t changed: aligned teams outperform siloed ones. What’s changed is the stakes attached to getting the “Data” pillar specifically right. AI adoption inside RevOps functions hit 61% in 2026, concentrated in forecasting, data enrichment, and lead scoring. That number is a floor, not a ceiling, given how fast agentic tooling is being layered into CRMs generally. That shift changes what “clean data” needs to mean.  For most of RevOps’ history, a data gap was a coordination problem: a manager working from an incomplete pipeline view made a slightly worse decision, and a person further up the chain usually caught the obvious error before it compounded. Increasingly, that same data feeds AI agents that act on it directly by updating fields, flagging risk, or triggering workflows. There is no person checking the work first.  A wrong stage or a missing stakeholder used to produce a misleading report. Now it can produce a wrong automated decision at a speed no manager can catch in time. This is why CRM data completeness earning its own place as a top-tier RevOps metric in 2026 isn’t a cosmetic shift. It reflects the actual change in what’s riding on the data being right. The Business Case for RevOps The performance gap between companies with mature RevOps functions and those without has stayed wide and, across most current research, gotten wider: Companies with mature RevOps functions report 19% faster revenue growth and 15% higher win rates than peers without one. Forrester research on aligning people, process, and technology across the revenue engine has linked that alignment to 36% more revenue growth and up to 28% more profitability. Public companies with dedicated RevOps functions have shown meaningfully stronger stock performance than peers without one. Frequently Asked Questions Q. What is the difference between RevOps

Marketing, RevOps

10 Best Account Based Marketing Tools for 2026

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10 Best Account Based Marketing Tools for 2026 Marketing 10 min Updated: July 16, 2026 Account-based marketing tools have gotten better at execution but are not easier to run at scale. Multi-channel ABM campaigns still require real coordination between sales and marketing, and the tools in this category exist to make that coordination less painful, whether that means better targeting, better personalization, or better reporting on what’s actually working. Two things are worth knowing before you evaluate anything on this list. First, this category has consolidated meaningfully in the last few years, several tools that used to be independent are now part of larger platforms, and it’s worth knowing which is which before you sign a contract expecting the standalone product. Second, ABM’s underlying data problem has gotten a new dimension: as more marketing and sales tools add AI features on top of account and contact data, that data has to be genuinely accurate, not just directionally useful, or the AI layer amplifies whatever gaps are already there. Get our latest insights into your inbox What is ABM? Account-based marketing flips the traditional funnel. Instead of casting a wide net and qualifying leads down to a smaller set, marketing and sales work together from the start to: Identify high-value accounts that fit ICP criteria Engage them with personalized content Find and map the actual decision-makers involved Move them toward closure together Stay engaged post-sale to capture expansion opportunities ABM isn’t one-and-done selling. It’s built around customer lifetime value, which is also why the data behind it has to stay accurate well past the initial close. Marketing Attribution Usecase Uncover hidden first-party contacts to drive your ABM efforts with Nektar Map buyer group intelligence hidden in sales conversations Create personalized ABM Campaigns Discover hidden pipeline from first-arty contacts 10 ABM Tools for 2026 6sense Revenue AI, predictive account intelligence and journey orchestration HubSpot Marketing Hub, omnichannel personalization for HubSpot-native teams Demandbase, account-based experience across three integrated modules Terminus (now part of DemandScience), multi-channel ABM with native email-signature marketing RollWorks, ABM built around paid ad execution Foundry Intent (formerly Triblio), intent data and web personalization bundled with Foundry media Vainu, sales intelligence and account data for list building Apollo.io, prospecting, engagement, and ABM in one platform Uberflip, content personalization and distribution for ABM Alyce by Sendoso, AI-personalized corporate gifting for account engagement Overview of the 10 Best ABM Tools 1. 6sense revenue AI 6sense helps marketing teams identify high-value accounts, predict where they are in the buyer journey based on account activity, and engage them with the right message at the right touchpoint. Features: automatically updates contact lists with additional firmographic information, segments accounts into behavioral cohorts, tracks activity across channels and attributes it back to the account. Pricing: Custom, based on users and use case. 2. HubSpot Marketing Hub HubSpot Marketing Hub is an omnichannel marketing solution with particularly powerful personalization tools. You can use them to set up and automate hyper-targeted messaging across multiple touchpoints to reach and engage with specific prospects. Features: automatically segments contact lists based on customer criteria, spots prospects who mirror your top customers through lookalike lists, personalizes messaging across landing pages, emails, socials, and more. Pricing: Marketing Hub Starter: $7/user/month Marketing Hub Professional: $800/month Marketing Hub Enterprise: $3,600/month Free marketing tools with limited features are also available 3. Demandbase Demandbase runs on the Account-Based Experience concept across three connected modules: ABX Cloud for ABM strategy, Advertising Cloud for campaign management, and Data Cloud for integration support. Features: account-level insights for campaign execution, support for multiple ad formats across global markets, straightforward integration with existing stacks. Pricing: Custom, based on use case and team size. 4. Terminus (now part of DemandScience) Terminus merged into DemandScience in November 2024. The product continues to operate under the Terminus name, now backed by DemandScience’s broader B2B data and demand-generation assets, and still includes the native email-signature marketing (via its earlier Sigstr acquisition) that differentiates it from most other platforms on this list, turning every outbound employee email into an addressable ABM surface. Features: in-depth segmentation including buyer intent, multi-channel campaign support (ads, chat, email signatures, web personalization), a built-in B2B CDP. Pricing: Quote-based; third-party buyer data puts mid-market packages around $40,000 to $80,000 annually, with enterprise tiers higher. 5. AdRoll ABM (formerly RollWorks) RollWorks was fully rebranded to AdRoll ABM in August 2025, when parent company NextRoll unified its AdRoll and RollWorks brands into one platform. It’s the same product, team, data, and pricing as before, just operating under the AdRoll name, and remains a good fit for marketers who rely primarily on paid ads for account-based lead generation. Features: targeting recommendations based on historical campaign performance, account-to-decision-maker mapping with contact information, contextual account signals like org changes, mergers, and acquisitions. Pricing: Starter plan around $975/month; contact sales for other tiers. 6. Foundry Intent (formerly Triblio) Triblio was acquired by IDG, now Foundry, back in 2020, and the product has been sold as Foundry Intent for several years. If you’re evaluating this expecting the independent Triblio product, know upfront that pricing and packaging now tie more closely to Foundry media-spend commitments than the standalone product used to. It’s a strong fit specifically for enterprise tech and IT vendors already buying Foundry/IDG content syndication, since its intent data draws from IDG’s own editorial coverage areas (security, cloud, enterprise software) and is noticeably weaker outside them. Features: visual, drag-and-drop campaign builder, intent- and activity-based conversion probability scoring, web personalization bundled with Foundry’s media inventory. Pricing: Tied to media-spend commitments; contact Foundry directly. 7. Vainu Vainu is a sales intelligence tool for finding high-value accounts from its global company database, speeding up list-building with contextual account information. Features: targeted contact lists built from ICP filters, automatic updates as new contacts are found, a single consolidated view of contacts stored across other tools. Pricing: Free trial available. Team plan around €4,200/year, Business around €9,900/year, Global around €12,000/year, custom Enterprise pricing. 8. Apollo.io Apollo.io combines prospecting, campaign orchestration, and sales engagement, letting you

RevOps Agencies
RevOps

Top RevOps Agenices

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Top 9 B2B SaaS RevOps Agencies RevOps 10 min Updated: July 14, 2026 RevOps is the backbone for driving sustainable growth and maximizing revenue. By breaking down silos between sales, marketing, and customer success teams, RevOps fosters seamless collaboration and alignment, ensuring a unified approach towards revenue generation.  Even though the importance of RevOps has been largely understood by organizations, one bone of contention remains: RevOps agencies.   RevOps agencies promise to align sales, marketing, and customer success around shared data and process usually faster than building that muscle in-house from scratch. Whether that promise holds up depends entirely on which agency you hire, and this category has no shortage of firms making similar claims with very different track records behind them. But first – What is a RevOps agency, and what do they do? When should you consider hiring a Revenue Operations agency, and what are the top agencies in the market? We answer all this and a lot more in this blog. Get our latest insights into your inbox What Is a RevOps Agency? A RevOps agency is an external team that designs and implements the systems, data architecture, and cross-functional processes connecting sales, marketing, and customer success. This typically includes CRM architecture, tech-stack integration, forecasting methodology, and the handoffs between teams that most commonly break as a company scales. Most operate on a retainer or fractional model rather than a one-off project, since RevOps is an ongoing function, not something you fix once and leave alone. Top RevOps Agencies in the US (2026) 1. RevPartners RevPartners, headquartered in Miami, FL, holds a 5.0 rating across 450+ reviews on the HubSpot Solutions Directory. It is the only agency to simultaneously hold HubSpot Elite Solutions Partner and Clay Elite Studio Partner status. Its work spans CRM architecture, HubSpot implementations and migrations (including from Salesforce and Marketo), embedded fractional RevOps, and a Clay-driven outbound layer they call “allbound.” Best for: HubSpot-centric B2B SaaS teams wanting the deepest pure-RevOps bench in the category. 2. New Breed New Breed, headquartered in Vermont, carries a 5.0 rating across 580 reviews on the HubSpot Solutions Directory. Their work combines demand generation with RevOps implementation, and they built Distributely, a lead-distribution app purpose-built for HubSpot users. Best for: HubSpot-native B2B SaaS teams that want RevOps and demand generation handled by the same partner. 3. Aptitude 8 Aptitude 8, headquartered in New York, NY, holds a 5.0 rating across roughly 250 HubSpot Solutions Directory reviews and was named the #2 Global HubSpot Solutions Partner in 2024. They position themselves specifically as a technical consulting firm rather than a marketing agency. They do no campaign or content work, and have an in-house US-based delivery team. They are focused on complex HubSpot architecture and systems design. Best for: Companies that have outgrown standard HubSpot onboarding and need deep technical/architectural work, not marketing services. 4. Winning by Design Winning by Design, headquartered in Menlo Park, CA, created the widely-referenced Revenue Architecture framework and the “bowtie funnel” model now taught across much of the B2B SaaS GTM world. Their client list includes Adobe, Uber Eats, Calendly, among others, which signals the scale of engagement they typically handle. Best for: Series B+ SaaS companies wanting the full customer lifecycle re-architected as one engineered system, not just a CRM cleanup. 5. Go Nimbly Go Nimbly, headquartered in San Francisco, CA, provides fractional RevOps teams of analysts, Salesforce admins, and marketing automation specialists, with a particular strength in product-led growth motions. They’ve worked with brands like Intercom, Watershed, and Superhuman. Best for: PLG or hybrid PLG/sales-led SaaS companies needing flexible, subscription-style access to a full RevOps bench. 6. Carabiner Group Carabiner Group, headquartered in Los Gatos, CA (acquired by growth advisory SBI in 2024), bills itself as the only fully platform-agnostic RevOps-as-a-Service agency, supporting 150+ tools across the revenue tech stack rather than specializing in one CRM. Best for: Teams with a genuinely fragmented, multi-tool stack where no single platform is the core problem. Best for: PLG or hybrid PLG/sales-led SaaS companies needing flexible, subscription-style access to a full RevOps bench. 7. RevPal RevPal, headquartered in Bend, OR, was ranked the #1 RevOps agency on Reply.io’s 2026 list and placed in the top three by Revenue.io. Their proprietary diagnostic tool, OpsPal, connects to a prospect’s CRM and produces a scored health report before any engagement begins. Best for: B2B SaaS teams wanting a diagnostic-first engagement or proof of what’s broken before committing to a scope of work. 8. Remotish Remotish, headquartered in Cincinnati, OH, runs a Monthly RevOps Program purpose-built for HubSpot portals, alongside onboarding, consulting, and WebOps support. Best for: HubSpot-native teams wanting an ongoing, monthly-cadence RevOps partner rather than a large upfront implementation project. 9. Domestique Domestique is a fractional RevOps firm that, unlike many agencies in this category, does hands-on implementation work directly rather than handing over an audit deck. They build targeted tech stacks and go-to-market alignment for early-stage through Series B companies. Best for: Early-stage to Series B SaaS companies wanting foundational RevOps systems built, not just advised on. RevOps Agencies Compared When to Hire In-House vs. an Agency Strengthen your in-house team when your processes are genuinely company-specific, when data security or compliance requirements make outside access impractical, or when tight day-to-day collaboration with other departments matters more than outside expertise. Consider an agency when you need specialized knowledge you don’t have in-house, when you need results faster than a from-scratch hire-and-train cycle allows, or when your needs will flex significantly over the next year. An agency can scale engagement up or down in a way a full-time hire can’t. Most companies land somewhere in between: an agency to build the initial system and train the team, with an in-house hire eventually taking over day-to-day ownership once the foundation is in place. Frequently Asked Questions Q. How much does a RevOps agency cost? Retainers typically run $3,000–$30,000+ per month depending on scope, with project-based engagements (a full CRM migration, for instance) often priced between $40,000–$200,000. Diagnostic-first engagements

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AI

The AI Data Readiness Checklist

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Checklist The AI Data Readiness Checklist Built for teams evaluating or expanding Agentforce, Microsoft Copilot, or any AI agent built on top of Salesforce Your AI agent is only as reliable as the data it runs on. Most enterprise Salesforce instances aren’t ready. 88% of enterprise AI agent pilots fail to reach production. The models are capable. The agent frameworks work. The bottleneck is almost always the same: the CRM data underneath is incomplete, fragmented, and only half-trustworthy. This checklist gives revenue and RevOps teams a practical, honest assessment of where their Salesforce data stands before deploying Agentforce, Copilot, or any AI agent built on top of CRM. 33 checkpoints across 5 categories to give you a clear picture of what’s in place and what isn’t. What you’ll walk away with: A category-by-category audit of your data foundation across completeness, identity resolution, unification, governance, and monitoring. Clarity on which gaps will cause your AI agents to fail first. And in which order to fix them. Specific checkpoints on the data problems most teams don’t know to look for: partner-attributed activities, deduplicated activity records, meeting intelligence capture, bounced contact suppression, and junk filtering. A reference your team can return to as you scale AI use cases across the GTM lifecycle. See how Nektar closes the gaps this checklist surfaces automatically. Learn More About Nektar’s Revenue Telemetry Nektar captures every customer interaction and writes it into Salesforce without any rep input. Download The Checklist From The Revenue Lounge Podcast​ Related Resources Ready to Turn your Data into Revenue Outcomes? Book a Demo

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AI

Why Your Salesforce Data Isn’t Ready for AI Agents

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Why Your Salesforce Data Isn’t Ready for AI Agents AI 8 min July 3, 2026 You’ve started evaluating Agentforce, or Copilot, or one of the dozen AI tools now plugged into your GTM stack. The demo looked great. The pilot got greenlit. And somewhere in week three, things started going sideways. Wrong recommendations, missed context, an agent confidently citing a contact who left the company eight months ago. Before you conclude the AI isn’t ready, it’s worth asking a different question: Is your data ready for AI? Get our latest insights into your inbox The Problem isn’t the Model Across the Salesforce ecosystem right now, a consistent pattern is emerging in post-mortems on stalled AI deployments. It is rarely the algorithm. A widely cited industry estimate puts the figure starkly: 88% of enterprise AI agent pilots fail to reach production, not because the agents are weak, but because the CRM data underneath produces confidently wrong outputs at scale. That’s a different failure mode than what most teams plan for.  Bad data has always been a CRM annoyance. Duplicate records, an outdated phone number, a stale job title. Humans navigate around these problems instinctively. A sales rep glancing at an incomplete contact record fills in the blanks from memory. A sales manager catches an obviously wrong forecast before it reaches the board deck. AI agents don’t do that. As one analysis of Salesforce data quality puts it, garbage in, garbage out was the old principle. The 2026 version is sharper: garbage in, confidently wrong out. Agents do not pause to verify a stale record the way a human would. They act on it, then propagate the action across thousands of records before anyone notices. Salesforce’s own product marketing has converged on the same message. As the company’s Tableau product marketing director put it, an AI strategy without a data framework is just a wish list. Attempting to deploy AI agents without one leads to inconsistent results, security risks, and a lack of user trust. What “data readiness” actually means It’s tempting to treat data readiness as a vague hygiene goal. “Clean up the CRM” without a concrete definition. Salesforce’s own guidance on the topic is more precise, and worth using as a working checklist before evaluating any agent deployment: Is your data unified and harmonized? If your data is fragmented across Sales Cloud, Service Cloud, spreadsheets, and a dozen point tools, the agent will deliver fragmented and inconsistent experiences. Unification isn’t optional. It’s the precondition. Have you resolved identities and is the information current? The same contact often exists as three different records: full name, abbreviated name, email-only. And each one tells the agent something slightly different. Old, incorrect data leads to frustrating experiences for customers and unreliable outcomes, including outright hallucination. Do you have governance and security in place? An agent should only access the data it needs to do its job, and that access needs to be auditable. Can you activate the data in real time? Data sitting in a warehouse, updated weekly, doesn’t power an agent that needs to act now. Is there a feedback loop? Agents need humans in the loop checking whether they’re acting on the right information, not a “set and forget” deployment. Separately, a widely referenced breakdown of what “good” CRM data looks like for AI purposes narrows it to three properties: data needs to be complete (the full picture, not partial context), structured, and effective for the specific task the agent is meant to perform. Without completeness, AI models miss vital context: what stage a contact is at, what previous interactions occurred, who else is involved in the decision. The numbers behind the problem are larger than most teams expect This isn’t an edge-case concern. Recent industry data paints a fairly stark picture of how unprepared most enterprise data actually is for agentic AI. Fewer than one in five companies has a high level of data readiness, and only 9% are fully prepared for the data integration and interoperability that AI requires, according to a 2025 Capgemini report on AI agents.  A separate analysis found that 81% of companies say fragmented data is preventing them from unlocking AI’s potential. Service agents miss complete customer histories, sales agents miss signals because marketing interactions aren’t visible, and analytics agents produce unreliable insights that undermine decision-making. The trust problem compounds this. Industry surveys cited by Salesforce found that nearly six in ten AI users say it’s difficult to get what they want out of AI right now, with over half saying they don’t trust the data used to train the systems they’re working with. Separately, a survey found that 90% of high-level data professionals believe company leadership isn’t paying enough attention to bad or inadequate data, even as AI initiatives accelerate. Only 9% of organizations report fully trusting their data which directly affects their confidence in CRM reporting. The forecasting impact is direct and measurable. Inaccurate forecasting tied to poor data quality affects a meaningful share of sales organizations, and several industry analyses tie data quality directly to financial loss. Duplicate or incomplete customer records cause missed opportunities, double-booked engagements, and wasted marketing spend when AI-driven outreach unknowingly targets the wrong contacts or duplicates effort. Why this is structurally different from past CRM problems Traditional CRM issues included duplicate records, missing fields, outdated contact info. A salesperson could work around a few mistakes in a report. A direct mail piece sent to an old address was a minor, contained error. When an AI agent built on top of that same data starts making autonomous decisions, the stakes change entirely. The agent doesn’t know it’s working from a flawed record. It acts with full confidence on whatever it’s given. The moment AI starts acting on it, a small inaccuracy in CRM data gets magnified, not corrected. This is also why simply buying a better AI agent product doesn’t solve the underlying issue. As one technical breakdown of Salesforce AI failures put it plainly: it’s not the

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RevOps

The Enterprise RevOps Playbook

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Playbook The Enterprise RevOps Playbook​ How to Build, Scale and Optimize Revenue Operations for Sustained Growth RevOps isn’t back-office support. It’s the command center of modern revenue. The old GTM funnel (lead to opportunity to closed-won) doesn’t hold up against a business built on renewals, expansion, and retention. This playbook draws insights from enterprise leaders on what it actually takes to build RevOps that scales with the business instead of scrambling behind it. What you’ll take away: The four categories of metrics that power the GTM engine. How to hire for RevOps at every stage. A framework for building your RevOps data stack. Why win-loss analysis is more than a checkbox, and how to turn it into pricing, messaging, and product input. Where AI actually earns its place in RevOps today. The operating rhythms that keep teams aligned without adding meetings for the sake of meetings. Turn Buying Group Theory Into Revenue Impact​ Explore Buying Group Usecase See how leading GTM teams build complete buying groups without relying on manual CRM updates with Nektar Download The Playbook From The Revenue Lounge Podcast​ Related Webinars Ready to Turn your Data into Revenue Outcomes? Book a Demo

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Buying Group

The Buying Group Playbook

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Playbook The Buying Group Playbook​ A practical guide to adopting opportunity-based marketing.​ Stop chasing MQLs. Start tracking the committee that actually buys. Only 1% of MQLs convert to revenue. Meanwhile, the average B2B deal runs through 6-10 stakeholders who never show up as a lead score. This playbook pulls together conversations from The Revenue Lounge with GTM leaders at Palo Alto Networks, Reltio, and G2 on why they walked away from lead-centric marketing and rebuilt their funnel around buying groups instead. What you’ll take away: Why coverage, campaign-to-opportunity influence, and buying group completeness are replacing MQL volume, lead scores, and email opens as the metrics that matter. The four buying signals to check for any account. A stakeholder role map and buying signal tracker template you can put to use immediately. Real numbers: 2.4x larger deal sizes, 22-23% faster sales cycles, and 60% less pipeline fallout when buying groups are fully mapped. How to handle the objections you’ll get internally with pilot-first responses that don’t require new budget. Get the full breakdown, templates, and results included. Turn Buying Group Theory Into Revenue Impact​ Explore Buying Group Usecase See how leading GTM teams build complete buying groups without relying on manual CRM updates with Nektar Download The Playbook From The Revenue Lounge Podcast​ Related Webinars Ready to Turn your Data into Revenue Outcomes? Book a Demo

Uncategorized

How Nektar Powered Mimecast’s Agentic GTM AI

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How Nektar Powered Mimecast’s Agentic GTM AI with Complete Customer Signals INDUSTRY Cybersecurity HEADQUARTERS London, England WEBSITE https://www.mimecast.com/ Hear directly from Tim about the challenges, decisions, and lessons behind Mimecast’s AI and GTM transformation. Watch Webinar $10M in total expansion revenue $150M+ in additional pipeline identified 2K+ new activities logged through historical data backfill As enterprise AI evolves, one thing is becoming clear: models are getting easier to access, and agent frameworks are becoming easier to build. The real advantage comes from something harder to replicate — proprietary data. Mimecast recognized this early in its AI journey. The company was investing in internal generative AI capabilities to surface better customers and prospect insights across the go-to-market lifecycle. The vision was ambitious: build AI applications that could support teams across acquisition, expansion, retention, renewal, and prospecting. But like many enterprises, Mimecast ran into a familiar challenge. The data needed to power those applications was spread across disconnected systems, inconsistent workflows, and siloed teams. Valuable engagement signals existed, but they were difficult to access, difficult to standardize, and hard to use at the level of granularity required for meaningful AI outcomes. That challenge made one thing clear: if Mimecast wanted AI to create real business value, it first needed a stronger data foundation. The Challenge: AI is only as strong as the data behind it Mimecast had already built its own internal generative AI engine to identify customer and prospect insights. But success depended on capturing and organizing the right GTM data inside its own data model. That was easier said than done. Critical information about customer engagement, buying committee members, and deal influence was spread across multiple processes and systems. Some GTM tools did not provide access to the data Mimecast needed. In other cases, the data was available, but not in a form detailed enough to support the outcomes the team was after. As Tim Seamans, VP of AI Acceleration at Mimecast, explained: Tim SeamansVP, Al Acceleration & Transformation We build our own generative AI engine internally to identify customer and prospect insights. What’s paramount for success is capturing available data and aligning it in operational systems. He added: “It’s really difficult to access data across disparate processes and systems so that we can get the right data, in the right place, at the right time.” Mimecast’s challenge was not a lack of AI ambition. It was the difficulty of bringing together the underlying data required to make AI applications accurate, useful, and scalable. Why proprietary data became central to Mimecast’s AI strategy Mimecast’s approach was rooted in a clear belief: while models and agents continue to improve, proprietary data is what ultimately creates a durable advantage. That thinking shaped the company’s AI roadmap. Mimecast began building 8–10 specialized AI applications and agents across the customer lifecycle, including applications for: Acquisition Expansion Retention Renewal management Prospecting These applications were designed to help teams act on customer and prospect signals more intelligently. But for them to work well, Mimecast needed better access to engagement data and customer context across the business. The priority was not simply generating more output. It was making sure AI systems had the right inputs to produce accurate, trustworthy, and business-relevant outcomes. The Solution: Unlocking the GTM data layer with Nektar Nektar helped Mimecast access the data and metadata it needed to strengthen the foundation behind its AI strategy. By capturing GTM engagement data that had previously been fragmented or unavailable, Nektar helped Mimecast unify important customer signals and make them available downstream. Just as importantly, that data could be delivered into the systems where Mimecast needed it most — including its CRM and data lake. That meant Mimecast could use Nektar not as another destination system, but as a data layer that supported its existing architecture and internal AI applications. Nektar helped us get the data (and metadata) we needed that was previously locked up or not available. And they can pipe it to our CRM or our data lake. Tim SeamansVP, Al Acceleration & Transformation This was a meaningful shift. Instead of relying on incomplete signals or inaccessible information, Mimecast could work with a richer and more structured view of customer engagement. Nektar helped Mimecast turn fragmented GTM data into an AI-ready signal layer Mimecast’s AI strategy depended on one thing: having complete, usable customer and prospect data inside its own systems. Nektar helped make that possible by unlocking and structuring engagement data that had previously been siloed, incomplete, or inaccessible. With Nektar, Mimecast was able to add meaningful scale and depth to the data powering its internal AI applications, including: 24K+ net-new contacts added 200K+ historical and ongoing emails captured and enriched 1K+ hours of manual rep work saved annually That data foundation gave Mimecast a much richer signal layer for customer insights, prospect intelligence, feature engineering, and AI-driven workflows. Instead of working from partial records and missing context, the team could feed its AI applications with complete interaction history across the customer lifecycle. Building for accuracy, not just automation For Mimecast, the goal was never to deploy AI for its own sake. The goal was to make AI outputs reliable enough to drive action. That required more than models. It required structured data, stronger context, and the ability to capture the signals that shape real customer outcomes. With Nektar helping fill those gaps, Mimecast was able to improve the quality of inputs behind its AI applications. That, in turn, supported more accurate insights across critical GTM workflows and spending more time acting on actionable signals and less time finding and structuring the data. This was especially important for a company building specialized applications across the customer lifecycle. Better data meant better context, better context meant better output, and better output made it easier to tie AI efforts to tangible business value. The business results came quickly. In the first 80 days after launching just one of Mimecast’s Proprietary AI tools, “Expansion AI”, the company achieved: The impact: $2M in expansion revenue and

AI, GTM, RevOps

Unlocking Revenue Intelligence: Bridging Data Gaps with AI & GTM Strategies

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In this episode of the Revenue Lounge Podcast, host Randy Likas and guest Uday Sharma discuss the critical importance of data trust and hygiene in modern revenue operations. They explore how fragmented data can lead to poor decision-making and the necessity of building a centralized data system to enhance revenue intelligence. Uday emphasizes the role of analytics in shaping strategy rather than merely reporting metrics, and the conversation also delves into the implications of AI on data quality and governance. Uday shares insights on how to effectively advocate for funding data initiatives and the importance of changing organizational behavior to improve data practices.

GTM

PLG to Enterprise GTM: Playbook for Experimentation, Signal Discipline and AI

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PLG to Enterprise GTM: RevOps Playbook for Experimentation, Signal Discipline, and AI That Actually Works A conversation with Stephanie Couzin. Executive Summary Lucid’s GTM evolution is not a “PLG vs Sales” story. It’s an operating model story. In this episode, Stephanie Couzin (VP, GTM Strategy & Ops at Lucid) breaks down how modern revenue teams can scale experimentation, build enterprise sales muscle, and adopt AI without turning their tech stack into a Frankenmonster. Readers will learn: PLG → Enterprise is a signal shift: you move from optimizing for users to optimizing for accounts, buying committees, and expansion readiness. Experimentation only works if comp risk is managed: GTM tests touch variable pay, so pilots must be designed as true win-wins. Psychological safety is a growth lever: teams ship better ideas faster when people can speak up, fail fast, and share learnings without fear. Data is the cost of entry for GTM testing: if upper-funnel metrics and activity data are messy, your “experiments” turn into opinions. Standardize AI or suffer whack-a-mole: pick a core AI platform to reduce tool sprawl and enable repeatable adoption. Agentic vs Copilot AI are different games: agentic is replacement (parity + cost savings), copilot is augmentation (productivity + more customer time). Start with the use case, not the tool: the fastest path to value is defining the workflow problem first, then deciding buy/build. One priority beats twelve “priorities”: focus drives execution, and execution is the only feature that matters. Facebook Twitter Youtube The Hidden Shift: From “Users” to “Accounts” Early Lucid was deeply product-led, built on a mature self-serve engine. Then came the layering: Sales motion Segmentation Enterprise complexity Multi-threaded post-sales workflows Stephanie describes it as moving from user-centric growth to account-centric expansion. PLG gives you usage.Enterprise GTM demands orchestration. The real challenge isn’t adding sales reps.It’s building the infrastructure to know when, where, and why sales engagement should happen. Experimentation Built Lucid’s Sales Muscle Stephanie points to Lucid’s early experimentation with a Product Qualified Motion. Most PLG companies start with lead scoring at the user level. Lucid evolved it further: Not just which user is engaged But what is the account telling us? And who should we reach out to now? That shift is everything. “Where that really evolved over time was identifying at the account level, what are the signals we need to outreach at the right time with the right messaging.” — Stephanie Couzin That’s the marriage of: First-party product signals Third-party intent and context Segment-aware targeting Persona-aware outreach Stephanie calls it the “special sauce” of modern lead scoring. The Account Expansion Checklist (Lucid’s Internal Recipe) Stephanie hints at what many GTM teams lack: an internal definition of “ready.” Lucid has an internal checklist: “A set of account signals where we say: this account is locked and ready to expand.” If the checklist isn’t met, Lucid doesn’t stop. They reverse-engineer value: You use Slack? You use documentation tools? You have workflows Lucid integrates into? Then Lucid doesn’t sell harder. They sell smarter: “This is how you get more value.” Cross-Functional Alignment: Experimentation Without Chaos Testing in GTM is not like testing in product. Because in revenue… someone’s commission is always involved. Stephanie puts it bluntly: “Testing something new will impact someone’s variable compensation.” — Stephanie Couzin So experimentation requires designing for buy-in. Lucid ran a coverage pilot by: Using lower-risk accounts Making rep trades fair Ensuring nobody felt punished for participating A GTM experiment only works if it’s a win-win. Otherwise reps sandbag, ignore, or resist. And your “pilot” becomes theater. Psychological Safety Is the Real GTM Scaling Lever Stephanie goes deep here, referencing Google’s Project Aristotle – Google studied what makes teams high-performing. The #1 factor wasn’t IQ.It wasn’t experience.It wasn’t process. It was: psychological safety. “They could speak up without fear of consequence. They felt freedom to fail fast.” — Stephanie Couzin Stephanie teaches this internally at Lucid. And she’s clear: This isn’t about politeness.It’s about intentional leadership. Practical mechanisms Lucid uses: Weekly project show-and-tells “Smart Fridays” where reps share plays Normalizing learnings over perfection The GTM Psychological Safety Loop Safe to speak → More ideas → Faster experiments → Better learning → Higher trust → Stronger execution Data Discipline: The Unsexy Requirement for GTM Experiments Stephanie delivers one of the hardest truths in RevOps: Lower funnel data is solid. Upper funnel data is… suspicious “As you move further up funnel… fewer eyes are on those metrics.” — Stephanie Couzin Testing requires: Full-funnel accuracy Integrated activity capture Shared KPI definitions Governance (tagging, segmentation discipline) AI in GTM: Standardize or Suffer Stephanie’s AI governance advice is refreshingly simple: “Standardize on something. Otherwise you’re in whack-a-mole.” Lucid standardized on Google Gemini. Not because it solves every revenue use case. But because: It reduces fragmentation It sparks shared experimentation It creates repeatable workflows It prevents reps from duct-taping random tools together The goal is not AI everywhere. The goal is AI that fits into systems. The Most Important GTM AI Rule: Start With Use Cases, Not Tools Stephanie nails this: “What use case are you trying to solve, not what tool do you want?” Because shiny object syndrome is real. Most orgs buy AI like toddlers choosing cereal: “Ooh, the box is shiny.” Lucid forces the opposite: Define workflow pain first.Then evaluate tooling. AI Use Case Intake Form What GTM workflow breaks today? What manual effort exists? What does “better” look like? Is this assistive or agentic? Can current stack solve 80% already? What data sensitivity is involved? What adoption friction will occur? Agentic AI vs Copilot AI: Stop Confusing the Two Agentic AI Replaces human work Measured in cost savings Goal is parity (“do no harm”) Copilot / Assistive AI Enhances workflows Measured in productivity and customer time Harder to quantify, but more transformative “Most AI we adopt for revenue teams is assistive. It makes teams more productive.” That’s where the real gains are: Better prep Faster follow-up Less manual CRM work More customer-facing time The GTM Takeaway Stephanie Couzin’s playbook isn’t about

How Nektar helps AI Hypergrowth companies move even faster
AI

How Nektar helps AI Hypergrowth companies move even faster

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How Nektar Helps AI Hypergrowth Companies Move Even Faster Artificial Intelligence 10 min Fast-moving AI companies are having a moment. Every week a new AI-native startup crosses $100M ARR in what feels like record time. Accel’s 2025 Globalscape report shows a “new breed of AI-native applications” hitting scale much faster than previous generations of SaaS, with some reaching $100M ARR in just a few years. That velocity is backed by unprecedented capital. Prominent AI companies like Cursor, Writer, Groq and Fireworks are raising huge rounds, hiring at triple-digit growth rates, and building products that spread virally from individual builders into the world’s largest enterprises. AI application categories like developer tools, finance, cybersecurity and vertical AI each attracted multiple billions of dollars in 2025 funding alone. Nektar sits right in the middle of this wave. Over the past year, we’ve partnered with some of the fastest-growing AI companies in the US – including Writer, Cursor, Groq, Chainguard and Fireworks  to help them turn raw go-to-market activity into clean, structured, AI-ready data they can actually execute on. This blog looks at why AI companies grow differently, what that does to their GTM data, and how Nektar helps them grow even faster. The new AI growth curve: Speed, Efficiency and Youth Funding and company maturity Accel’s data makes one thing clear: AI is no longer a niche category. It’s the new centre of gravity for software investing. Total EU/US/IL cloud & AI funding (excluding models) has climbed into the ~$180B+ range annually, with 2025 setting fresh records.   AI model funding is heavily concentrated in the US, but on the application side, EU/IL funding now represents roughly two-thirds of US levels, showing how global this wave has become. The winners look very different from the last SaaS cycle: over 65% of the Accel US & Europe AI 100 are 0–3 years old, and US winners skew especially young at 2.4 years on average. Put simply: AI companies are raising big, hiring fast, and still figuring out their GTM motion on the fly. Bottom-up adoption and insane efficiency AI-native tools are spreading from the bottom up: Developers using AI coding assistants jumped from 36% in 2023 to 90% in 2025 – in just two years. Tools like AI IDEs, agents and copilots are hitting milestones such as “$100M ARR in 8 months” and “10x YoY growth,” according to Accel’s case studies of leading AI-native apps. This isn’t just fast growth – it’s efficient growth. Accel estimates that leading AI applications now generate 3–10x more ARR per employee than prior generations of SaaS companies. But that speed and efficiency create a GTM paradox: You can scale product adoption and revenue incredibly fast. But your GTM data, process and tooling often lag badly behind. The hidden tax of hypergrowth: messy GTM data Most fast-growing AI companies share a few traits: They sell into large, multi-person buying committees (Fortune 500, Global 2000, high-growth tech). They run hybrid motions – PLG bottoms-up adoption plus enterprise sales, often with heavy founder-led or executive-led outbound. Their GTM stack is complex and evolving: Salesforce + Gong + Snowflake + ABM + sequencing tools, changing every few quarters. They are young – which means processes, definitions and data hygiene were rarely “designed,” they just happened. That shows up in four chronic problems: Invisible buying groups Activity sits at the account or activity object level, not tied to which humans are actually influencing a deal. Contact roles are incomplete, incorrect, or simply not used. Multi-threading that’s impossible to measure Leadership wants reps and CSMs to multi-thread. But nobody can answer basic questions like: “How many net new stakeholders did this SDR actually bring in?” “Which deals progressed because we pulled in the economic buyer early?” Broken marketing attribution for enterprise deals First-touch and last-touch models collapse when there are 10–20 stakeholders, dozens of events and campaigns, and long sales cycles. “Marketing sourced” covers only a small fraction of reality. No shared view of the customer journey Pre-pipeline engagement, in-pipeline meetings, onboarding, success reviews, expansion conversations – they live in different systems owned by different teams. This is exactly the gap Nektar is built to fill. Nektar as the data backbone for AI GTM At its core, Nektar is a revenue data platform that: Harvests metadata from communication tools (email, calendar, meetings, sequences). Cleans and transforms that data. Writes it into Salesforce against the right opportunities, accounts, contacts and leads. Automatically creates and updates Opportunity Contact Roles (OCRs) with accurate personas (economic buyer, champion, influencer, etc.). Generates revenue signals that help teams act – from “missing exec sponsor” to “multi-threading risk” to “QBR overdue.” Writer is a great illustration of how fast-moving AI companies use this foundation Writer: building an AI-ready GTM engine on top of Nektar Writer is an enterprise AI platform selling into Fortune 500 and Global 2000 organizations. Their GTM complexity is huge: multi-persona deals, long cycles, and a mix of PLG, partner, and enterprise motions. One activity capture layer for Sales, CS and Marketing Writer started with Nektar in sales, then expanded to sales engineering, customer success and now marketing. Nektar: Captures emails, meetings and other activities from tools like Gmail and calendar. Associates them correctly with accounts, opportunities and contacts in Salesforce. Backfills historical data by “travelling back in time” across past emails and calendars, so data isn’t limited to post-implementation activity. Creates missing contacts and writes them into Salesforce as OCRs with mapped personas. Compared with their previous setup (Gong plus internal workarounds), Writer’s RevOps leaders called out that Nektar simply does a better job of capturing and correctly associating activities, especially in complex account structures with multiple open opportunities. This gives Writer a single, reliable activity dataset they can push into their warehouse (GCP) and model in Omni for analytics – a critical enabler for AI-driven GTM. Making multi-threading measurable (and compensable) Writer wants SDRs and AEs to multi-thread aggressively – and they want to pay them for doing it. The problem: Nektar was so good

Nektar.ai vs People.ai: A Buyer's Guide
RevOps

Nektar vs Backstory (formerly People.ai): A Buyer’s Guide

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2026 Guide for Enterprise GTM Teams Seeking Backstory (People.ai) Alternatives Buyer’s Guide 11 min Jan 27, 2026 Updated: August 13, 2026 A quick note before you read further: People.ai rebranded to Backstory in April 2026, repositioning as a “Revenue Answers Platform” with a conversational AI layer on top of its existing activity-capture technology. Same underlying company and core capture technology, new name and pitch. This guide refers to it as Backstory throughout, noting the People.ai history where it’s directly relevant (its Gartner recognition, for instance, was announced under the People.ai name). Introduction: Two Different Approaches to the Same Problem Both Backstory and Nektar operate in the revenue data capture category, helping enterprises automatically capture GTM activity and enrich their CRM using AI. However, they solve fundamentally different problems for different buyers. Backstory (formerly People.ai) is an established revenue intelligence platform with strong analytics capabilities, recognition as a Visionary in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration (awarded under the People.ai name, prior to the April 2026 rebrand), and a mature suite of tools including ClosePlan, account planning, and leadership dashboards, now layered with a conversational query interface as part of its Backstory repositioning. Nektar is an advanced data-first GTM telemetry solution focused on delivering clean, accurate, AI-ready CRM data directly into standard Salesforce objects, designed specifically for enterprises that want to power their existing BI stacks rather than adopt another analytics platform. This guide is intended for GTM leaders, RevOps leaders, Sales Operations teams, and Data teams evaluating both solutions. It draws on direct enterprise evaluation feedback, product analysis, and independent research to help you determine which solution fits your specific needs. Get our latest insights into your inbox Who This Guide Is For This comparison is most relevant if your organization: Already operates a mature BI stack (Databricks, Snowflake, Looker, Tableau) Has dedicated RevOps or SalesOps teams building custom analytics Prioritizes CRM data accuracy over out-of-the-box dashboards Needs granular control over what data syncs to Salesforce Requires specific detail around internal and external participation or meeting attendance intelligence (not just invitee data) If your priority is comprehensive analytics UI, pre-built dashboards, and account planning tools, Backstory may be the stronger fit for your organization. But if you’re looking to solve the data problem at its core without the additional enablement effort of new training, Nektar is a better bet. This guide focuses on scenarios where data infrastructure is the primary buying criterion. The Core Difference: Analytics-First vs Data-First The fundamental difference between these platforms comes down to philosophy, and the April 2026 rebrand sharpened rather than changed this distinction: Backstory is built around the premise that revenue teams need better analytics and insights delivered through their platform, now explicitly reframed around conversational, natural-language answers rather than dashboards alone. Data capture exists to power those answers, scorecards, and AI-driven recommendations. Nektar is built around the premise that enterprises already have analytics tools they trust. What they lack is clean, accurate, complete, unified rep activity data in the CRM to feed those tools. Nektar focuses on being the best possible data layer, not an additional interface to learn. Neither approach is inherently superior; they serve different organizational needs. The question is which approach matches your GTM infrastructure strategy, and whether a conversational interface actually solves your problem or just adds a new way to ask a question the underlying data still can’t fully answer. Considering an Alternative to Backstory? See how Nektar delivers 90%+ attribution accuracy directly into your Salesforce, without the Backstory price tag. Check Side-by-side Feature Comparison Why Enterprises Evaluate Backstory Alternatives Based on conversations with enterprise buyers evaluating both platforms, several consistent themes emerge: Existing Analytics Investment Many large enterprises have already invested significantly in Databricks, Snowflake, Looker, or Tableau. Their internal ops teams build custom dashboards tailored to their specific sales motions. For these organizations, adopting another analytics platform, conversational interface or not, creates redundancy rather than value. They want the underlying data, not another UI. Salesforce Integration Model Backstory (like People.ai before it) uses a managed package approach that creates custom objects in Salesforce. While this provides rich functionality within Backstory’s own ecosystem, some enterprises report challenges including: Additional automation required to map data into standard Salesforce fields Complexity when using captured data in existing workflows or forecasting Duplicate participant records requiring cleanup Nektar writes directly to standard Salesforce objects (Events, Tasks, Contacts), which can simplify integration with existing processes but may offer less specialized functionality. Meeting Attendance Requirements A significant differentiator for some buyers is meeting attendance intelligence. Backstory’s meeting data typically relies on calendar invites and recorded calls via conversation-intelligence platform integrations. Nektar captures both invitees and actual attendees, along with meeting status (completed, cancelled, no-show, under 10 minutes), without requiring recording. For organizations focused on coaching, churn analysis, or executive involvement tracking, this distinction can be decisive, and it isn’t something a conversational query layer on top of the same underlying capture gap actually solves. Data Volume Control Some enterprises express concern about data volume and Salesforce storage costs. Nektar offers granular sync controls that let administrators define which activities to capture, which contacts to create, and what thresholds to apply. Backstory’s capture approach may generate higher data volumes, which can be beneficial for analytics but challenging for storage-conscious organizations. Category Nektar.ai Backstory (formerly People.ai) Salesforce data model Standard objects (Events, Tasks, Contacts) Managed package with custom objects Opportunity Matching AI/ML graph-based, self-learning Rule-based, configurable Meeting Attendance Invitees + actual attendees + meeting status Primarly invitee-based, recorded calls via CI partners Meeting Intelligence Source Direct Zoom/Teams integration (no recording required) CI platform integration (Gong, Zoom IQ, Webex) Engagement Scoring Customizable, writes to Salesforce fields Pre-defined, displayed in analytics UI Multi-user Attribution All internal users + external contacts Primarily organizer-focused Noise Control Granular sync rules and filters Comprehensive capture approach Analytics & Dashboards Minimal (Data-focused) Comprehensive built-in analytics Account Planning Not a primary focus Strong (ClosePlan, org charts) Data Portability Standard objects, no lock-in Managed package migration required Best for Data-first

AI, Customer Success

Transforming Customer Success in the Age of AI

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Rebuilding Customer Success for the AI Era: Lessons from a VP of Customer Success A conversation with Chad Gorman. Executive Summary This article examines how customer success leaders should rethink AI adoption, using insights from an in-depth conversation with Chad Gorman, VP of Customer Solutions and Success (North America) at LivePerson, on the Revenue Lounge Podcast hosted by Randy Likas. Rather than focusing on automation or AI features, Gorman argues that AI-ready customer success is fundamentally about visibility, data discipline, and relationship intelligence. Readers will learn: Why most AI initiatives in customer success fail before deployment  How unifying fragmented CS data is a prerequisite for AI or automation What an effective early warning system looks like  Why engagement and relationship depth are stronger leading indicators than product usage alone How AI can expose relationship “white space” across complex buying committees Where buy vs build decisions actually differ across enterprise and mid-market segments How to embed AI into CSM workflows over-relying on automation Which metrics matter when measuring AI’s impact on retention, risk, and productivity Why the real promise of AI in customer success is reclaimed time for strategic customer work Facebook Twitter Youtube From Call Centers to Customer Outcomes Gorman’s perspective is shaped by an unusual career arc. He started in contact center operations, moved into IT at DirecTV, and then crossed over to the vendor side after a colleague recruited him to Splunk. “I didn’t even know what a CSM was,” he admits. “But once I saw how customer success could be built as a scalable engine, I was hooked.” — Chad Gorman From Splunk, he went on to lead global cloud customer success at VMware, before joining LivePerson, where he now oversees customer success and professional services across North America. That mix of operator, builder, and enterprise leader shows up in how he thinks about AI. Practical. Outcome-driven. Skeptical of hype. ​​AI Adoption Fails Before Deployment Most AI initiatives stumble long before a model is ever deployed. According to Gorman, the real friction points show up earlier in the buying and approval cycle. The Hidden Gates to AI Adoption Governance reviews and AI councils Legal, compliance, and security documentation Industry-specific scrutiny, especially in financial services Undefined success metrics “You can sell software all day long. But if you are not there to shepherd customers through governance, compliance, and approval gates, adoption will stall.” — Chad Gorman Customer Success Has Become Revenue Insurance In volatile markets, customer success is no longer a post-sale support function. It is a revenue protection layer. That shift forces CS leaders to answer harder questions: Where is risk building right now? Which accounts look healthy but are quietly disengaging? Where is expansion hiding in plain sight? The answer, Gorman says, is an early warning system built on stitched data. https://youtu.be/sDdV747jBJA?si=fVE8O2bqTcf2yTeN The Anatomy of an Early Warning System Gorman is blunt about the prerequisite. “Data is non-negotiable. Full stop.” — Chad Gorman Before AI enters the picture, organizations must understand what their book of business actually looks like. Engagement Is the Most Underrated Risk Signal Product usage is table stakes. Engagement is the differentiator. Gorman emphasizes that many churn events are preceded not by usage decline, but by relationship decay. “If engagement drops and you do not notice, you end up ghosted and surprised later.” — Chad Gorman What Engagement Actually Means Engagement is not email volume or meeting counts alone. It is relationship depth across the buying group. Who shows up to meetings? Who stopped showing up? Which roles are missing entirely? Who influences decisions but never engages directly? This is where Gorman believes AI has its most immediate impact. Relationship Intelligence: Where Art Meets Science Gorman describes relationship intelligence as the intersection of human judgment and system-derived insight. “We think we know our accounts. AI shows us the white space we missed.” — Chad Gorman AI-Assisted Relationship Mapping AI can analyze: Calendar data and meeting attendance Email and collaboration patterns Role changes and stakeholder turnover Sentiment from meeting notes and transcripts At LivePerson, Gorman’s team increasingly relies on workspace-level intelligence using Google Gemini to surface patterns across meetings, documents, and communications. You can literally ask, ‘Who used to attend and no longer does?’ and get an answer.” — Chad Gorman Buy vs Build Is No Longer Binary Enterprise customers increasingly want flexibility. Some bring their own LLMs. Others rely on vendor-provided AI. Most land somewhere in between. Enterprise: Build and bring your own models Upper mid-market: Hybrid Down-market: Out-of-the-box AI The common denominator remains the same: clean, structured, accessible data. Embedding AI Into CSM Workflows Even the best insights fail if CSMs do not trust them. Gorman stresses three adoption levers: Data transparency Always link insights back to source systems. Prescriptive guidance Do not just flag risk. Recommend next steps. Respect experienceAI should augment gut instinct, not override it. Measuring AI Impact in Customer Success AI success is not measured by novelty. It is measured by outcomes. What’s Next: Agentic AI and Time Reclaimed The next wave, according to Gorman, is not better summaries. It is execution. The thing CSMs hate most is admin. AI agents that actually do the work change everything.” — Chad Gorman Examples include: Auto-generated QBRs with live data Scheduled reporting without manual pulls Automated follow-ups and task execution The payoff is not speed. It is reclaimed time for strategic customer work. Leadership Lessons From the Field When asked what advice he would give his younger self, Gorman’s answer is simple. “Know your book. Be curious. Admit what you do not know.”” — Chad Gorman Growth mindset Curiosity within and beyond the “box” Meticulous organization Executive presence Tight partnership with the AE  Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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