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RevOps

Enterprise AI Transformation
AI, RevOps

The Enterprise AI Transformation Playbook

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Playbook The Enterprise AI Transformation Playbook A practical framework to move from AI pilots to measurable business transformation. Only about 1 in 4 AI initiatives make it out of pilot mode. The models work. The pilots work too. And that’s the trap. What doesn’t survive contact with scale is everything around the AI: the data foundation, the ownership, the measurement, the change management nobody budgeted time for. This playbook is built from conversations with the leaders running AI transformation inside enterprises. 10 frameworks across the full transformation lifecycle, with worksheets you can run with your own team, so you can see exactly where your program is exposed before it stalls. What you’ll walk away with: A stage-by-stage framework covering data foundations, problem selection, workflow redesign, team structure, executive buy-in, measurement, and build-vs-buy, with a worksheet for each. Clarity on which gap kills momentum first for most enterprises. Hint: it’s rarely the model. A three-tier framework for proving ROI in dollars instead of adoption numbers, plus the exact build-vs-buy logic three enterprise operators converged on independently. A real example of the playbook in action: How Mimecast achieved $2M of directly attributed expansion revenue and identified upwards of $80M+ in additional pipeline by bringing together previously siloed signals and engagement data. Download The Playbook From The Revenue Lounge Podcast​ Other Downloadable Resources Ready to Turn your Data into Revenue Outcomes? Book a Demo

CRM, RevOps

Fix Your CRM Data Quality to Drive More Revenue

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Fix Your CRM Data Quality to Drive More Revenue Product 11 min Updated: August 17, 2026 Your CRM has more data in it than ever. Organizations run an average of hundreds of thousands of records through their CRM, and that number only grows: marketing collects data through campaigns, sales through client interactions, support through calls. The volume keeps climbing every quarter. Volume was never the problem. It’s the quality of that data that actually moves the revenue needle, and by that measure, most organizations are in real trouble. Fewer than half of organizations report that even 50% of their CRM data is accurate, and confidence in that data to actually drive go-to-market decisions runs lower still. Without trustworthy CRM data, go-to-market motions fail to deliver. Customer churn, lower employee morale, weak ROI on the rest of your tech stack, and misalignment between customer-facing teams all trace back to the same root cause: revenue leaking out through data nobody can fully trust. Get our latest insights into your inbox Inefficiencies Plaguing Your CRM Data Quality Even after decades of existence, CRMs still haven’t evolved from a system of record into a system of genuinely actionable insight. Most of that failure has less to do with how a CRM functions and more to do with the data it’s actually operating on. 1. Missing Data CRMs put the burden of data entry on reps manually uploading it, and most of it simply never makes it into the system. That leaves out critical revenue data, and every gap is a missed opportunity hiding in plain sight. 2. Data Decay CRM data decays fast. Current research converges around 22.5% annual decay for typical B2B contact data, with some sources citing figures as high as 70% depending on industry and record type, tech and healthcare contacts tend to decay faster than finance, for instance. Using data that’s no longer accurate sends sales and marketing down the wrong path entirely, targeting the wrong people at the wrong time with a message built on outdated context. 3. Data Silos Most CRM data sits in silos. Sales keeps its own data; marketing runs its own stack. That lack of coordination means both teams optimize for their own goals rather than a shared one, and without unifying data from every source into a single source of truth, campaigns can’t deliver the targeted, personalized value buyers now expect. 4. Poor Quality Data Incorrect or outdated data sits in a CRM even as the real world keeps changing underneath it: people leave roles, companies grow past a segment, mergers happen, a buyer moves to a competitor. Knowing about these changes is critical for a successful campaign, and stagnant CRM data simply never captures them. How Poor CRM Data Quality Affects Revenue Be careful what you feed your CRM, or you get a textbook case of garbage in, garbage out. You can’t expect meaningful insight from a system built on an unreliable source. Data is what gives you visibility into where to improve, which leading indicators to focus on, and where the revenue funnel is about to break before it actually does. If the underlying data is compromised, none of that visibility is real. 44% of organizations estimate they lose more than 10% of annual revenue to poor data quality, and that leakage shows up across the business in several distinct ways: 1. High Employee Turnover CRM users, your own employees, are hitting a saturation point. A majority say they’d consider leaving if their organization doesn’t invest in a real CRM data quality plan. In a market where talent is genuinely scarce, that turnover means real time and money lost to hiring, onboarding, and re-engaging replacements. 2. Poor Sales Forecasting Forecast quality has a direct line to revenue. A poor forecast is what happens when bad data feeds a system that’s supposed to predict what closes each quarter, and the result is resources wasted chasing outcomes that were never realistic to begin with. 3. Poor ROI From the Tech Stack Every tool in the stack, CRM included, only delivers ROI when it has good data to work with. Without it, those tools stay expensive shelfware, eating budget without delivering value anyone can point to. 4. Poor Targeting Pulling every contact and running one uniform campaign is long past its expiration date. Today’s buyers expect hyper-personalized messaging, which requires marketing teams to have real, high-quality data on their contacts, not just a name and an email address. CRM data tells you who to target; it rarely tells you why. Bad data compounds that gap, sending the wrong message to the wrong customer for a problem they may not even have, which puts brand reputation at real risk. How to Address the CRM Data Quality Issue A strong data foundation is the first step. Once you have one, the next step is a system that continually enriches, maintains, and updates that data going forward. If a contact leaves their organization, your system should catch that automatically. If a new stakeholder joins the buying committee you’re pitching, that contact should get captured without anyone manually uploading it. Raw data is still useless on its own. Layer revenue intelligence on top of high-quality CRM data, and you get the data-driven insight that actually generates more revenue. 1. Build a Strong Data Foundation Third-party data quality and compliance are both increasingly questionable, and third-party data is on its way out as privacy law gets stricter. First-party data, the information a user shares directly with you, with consent, an ebook download, a webinar registration, avoids the problem at its source. It’s unique to you, privacy-compliant, accurate, low-cost, and genuinely marketable. Organizations report up to a 66% increase in revenue from clean, enriched first-party data, with campaign response rates improving by roughly 20% and close rates by 15% within six months of real enrichment. 2. Automate CRM Data Capture Forcing reps to manually upload contacts has never worked and never will. Manual upload fills the CRM with inaccurate, incomplete

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

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

Best RevOps Podcasts
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

Buying Group, RevOps

How Nektar Automates Buying Committee Engagement

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

RevOps

Revenue Operations vs Sales Operations

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

RevOps

10 Revenue Operations KPIs You Must Measure

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

Top 15 Revenue Optimization Tools
RevOps

Top 15 Revenue Optimization Tools to Consider in 2026

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

Best Revenue Intelligence Tools
RevOps

12 Best Revenue Intelligence Platforms for 2026

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

RevOps

RevOps Starter Guide – Building a Successful RevOps Roadmap

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

RevOps

5 Ways Siloed Data is Burning Your Revenue

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

RevOps, Sales

7 Elements of a Successful Deal Review

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

Top 5 Revenue Operation tools
RevOps

5 Revenue Operations Tools to Consider in 2026

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5 Revenue Operations Tools to Consider in 2026 RevOps 10 min July 20, 2026 Revenue operations exists to make sales, marketing, and customer success run as one connected system instead of three teams working off different numbers. The software behind that job has to do two things well: give the team accurate, complete data, and turn that data into action without adding more manual work. A growing share of RevOps tooling now includes AI agents that don’t just surface an insight for a person to act on, but they act directly, updating fields, flagging risk, or triggering workflows inside the CRM.  Gartner projects 40% of enterprise applications will include task-specific AI agents by the end of 2026. That raises the bar for what “revenue operations software” needs to guarantee: not just visibility, but data clean enough for both a human and an agent to trust. Get our latest insights into your inbox What is Revenue Operations? Revenue operations, or RevOps, is the function that aligns sales, marketing, and customer success around shared data, shared process, and a shared revenue goal, instead of each team optimizing its own metrics in isolation. Done well, it removes the guesswork from forecasting, shortens the gap between a problem showing up in the data and someone acting on it, and gives leadership one trustworthy view of the business instead of three conflicting ones. 5 Revenue Operations Tools to Consider in 2026 Nektar: GTM data foundation and AI signal layer Backstory (formerly People.ai): AI-driven activity capture and revenue answers SalesDirector.ai (a Bigtincan company): Automated activity capture and revenue insights HubSpot Data Hub (formerly Operations Hub): Data sync and hygiene automation Aviso AI: Agentic forecasting and revenue execution Overview: 5 Tools to Consider for Revenue Operations 1. Nektar Nektar is a GTM telemetry platform that automatically captures every customer interaction like emails, meetings, calls, and Slack, and delivers structured, clean data to your CRM, data warehouse, and AI applications. This requires zero manual entry from sales reps. Nektar ensures that what actually happens across your deals gets recorded. Most CRMs reflect only what reps remember to log. Nektar captures the rest, mapping interactions to the right accounts, contacts, and opportunities, then writing that data natively into Salesforce and Snowflake without any behavior change from the team. For revenue organizations adopting AI, this matters more than ever. AI agents act on CRM data. When that data is incomplete or stale, agents make wrong decisions at machine speed with no human in the loop. Nektar acts as the data foundation that makes AI-driven forecasting, deal intelligence, and agent workflows trustworthy. The platform also surfaces complete buying committees by identifying every stakeholder involved in a deal across email and calendar signals, not just the contacts a rep manually added. This gives RevOps and sales leaders a full picture of deal engagement, not fragments. Pricing: Nektar is priced based on team size and scope rather than a flat per-seat rate. A free, no-obligation CRM scan will show you how much of your own pipeline data is currently missing before you commit to anything. 2. Backstory (formerly People.ai) People.ai rebranded to Backstory in April 2026, repositioning itself as a “Revenue Answers Platform” — reasoning over activity data captured from email, meetings, and calls to answer natural-language questions about deal and account health. The rebrand adds a more conversational interface on top of the same underlying activity-capture approach of the product. Pricing: Not publicly listed as a simple rate card; contact the vendor for a quote. 3. SalesDirector.ai (a Bigtincan company) SalesDirector.ai automates capture of email and calendar activity between reps and buyers, layering on account-health scoring and stakeholder analysis. It’s operated as part of Bigtincan’s sales-enablement suite since a 2023 acquisition, and remains a reasonable fit for teams that want activity capture bundled with broader sales-enablement content and coaching tools. Pricing: Historically started around $29/month per user for the base activity-capture tier; confirm current pricing directly with Bigtincan, since packaging has shifted since the 2023 acquisition 4. HubSpot Data Hub (formerly Operations Hub) HubSpot renamed Operations Hub to Data Hub in 2026 as part of a broader push into data quality and AI-readiness — syncing, cleaning, and standardizing customer data across connected systems for teams running on HubSpot. It remains a strong fit for HubSpot-native RevOps teams and a weaker fit for anyone on Salesforce or a multi-CRM stack, since it’s built to serve HubSpot as the system of record. Pricing: HubSpot’s pricing structure changes often enough that a static table goes stale within months — current tiers run from a free entry point through Professional and Enterprise plans priced in the hundreds to low thousands per month, plus mandatory onboarding fees at the higher tiers. Check HubSpot’s official pricing page for current numbers before budgeting. 5. Aviso ai Aviso has repositioned from a pure forecasting tool into an agentic RevOps platform built around MIKI, a conversational orchestrator that can query pipeline data and trigger CRM updates directly, alongside a library of 50+ pre-built revenue agents and a no-code studio for building custom agent workflows. Pricing: Quote-based; contact Aviso directly. RevOps Tools Compared Why Use Revenue Operations Software The case for RevOps tooling hasn’t changed much in substance, even as the tools themselves have: More revenue. Companies with a dedicated RevOps function report meaningful revenue gains, largely from removing the friction between what sales, marketing, and CS each know about an account. Less manual work. Automating data capture and hygiene frees reps and RevOps analysts from reconciling spreadsheets and chasing missing CRM fields. Real visibility. Centralized, complete data lets teams spot problems — a declining conversion rate, a stalling deal, a churn risk — before they show up in a quarterly report. Better cross-functional alignment. Shared data removes the arguments about whose numbers are right, which is usually the real blocker to sales and marketing working well together. Frequently Asked Questions Q. What’s the difference between RevOps software and a CRM? A CRM stores contact and deal records that a rep

RevOps

A 30-60-90 Day Guide for First-Time Directors of RevOps

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A 30-60-90 Day Guide for First-Time Directors of Revenue Operations RevOps 10 min Updated: July 17, 2026 Starting a new job as a Director of Revenue Operations is an exciting opportunity to make a significant impact on a company’s revenue growth. To ensure success in this role, it’s essential to have a strategic plan that guides your actions during the crucial first three months. Here’s a 30-60-90 day plan that will help you strategically manage revenue operations and drive sustainable growth. We recently spoke to Hassan Irshad, Director of RevOps at FEVTutor. He shared his approach to this powerful framework, demonstrating how each phase (30, 60, and 90 days) builds strategically upon the last to deliver alignment, trust, and sustained improvement. By breaking down complex goals into achievable milestones, the 30-60-90 day approach empowers RevOps leaders to initiate meaningful change without overwhelming teams. For all the RevOps leaders, it’s a way to approach change with purpose, driving measurable impact and laying the groundwork for long-term success.  The 30-60–90 day framework must be an indispensable tool and here is how you can implement progressive, sustainable growth strategies from day one. Get our latest insights into your inbox First 30 days for a Director of Revenue Operations The purpose should be to gather insights and understand the organization, especially the needs and challenges of different teams. 1. Goals for first 30 days: Meet Key Stakeholders Meet with cross departmental teams like Finance, HR, and Sales to understand their goals, challenges, and priorities. Document Everything Create a “lay of the land” document summarizing findings on different team goals, challenges, and processes. Hassan IrshadHead of Revenue Operations, Unify Whoever you work with, like finance, HR, not only your standard stakeholders, you want to understand where they are, what drives them, what their priorities are. What are they looking for as short-term and long-term goals? Understand their pain points, which is going to dictate how your next 60 to 90 days’ work will be. So a lot of the discovery work happens then. Create a document, something I call “lay off the land” document. Understand the Product Take product demos, listen to sales calls, and use tools that show how the product is sold. This helps in understanding the customer needs better. Dive into Your CRM Understand your CRM (whether Salesforce or HubSpot) to assess how the data is organized. This is to check whether it’s easy to use, and identify immediate improvements. The CRM should be the central source of truth, with other tools supporting it. The data should be unified with easier adoption for the teams. Build Trust Internally Establish trust within your teams by listening carefully, asking questions about how RevOps can help, and addressing quick fixes to show you’re there to help. Having this trust shows them that you’re here to support their success. Quick wins, such as small fixes that make people’s jobs easier, helps in establishing credibility early. 2. Establish Clear KPIs: Understanding Team KPIs It is important to ask you stakeholders about the KPIs that matter to understand their goals and what their expectations are. Aligning KPIs Across Teams Different departments oftentimes work in silos. RevOps should strive to align these departments and check if these KPIs match the overall business objectives. Gaps must be closed if their KPIs don’t align. Setting RevOps KPIs As you approach the end of the first 30 days, start establishing RevOps-specific KPIs that match company goals, which may involve metrics like revenue increase, conversion rates, or improvements in overall efficiency. 3. Tech Stack Audit Deep dive into the existing tools that your company is using. Identify all redundancies, and find opportunities to streamline the entire tech stack. Map Out Tools Compile a list of all tools used by teams, noting their purpose and how they work with the CRM. Evaluate Use and Cost Determine if tools are actively used or if there are duplicates. Look for cost-saving opportunities by consolidating tools when possible. Next 30 days – Alignment and Control The next 30 days marks a shift from discovery to alignment. The goals should be to create cohesion between departments (e.g., Sales, Marketing) and laying down effective controls. The improvements need to be implemented without overwhelming the teams. This phase combines further exploration with actionable improvements with the primary task being bringing the teams into sync. Hassan IrshadHead of Revenue Operations, Unify One of the core things that I feel like revenue operations need is that trust between the go-to-market teams and saying, yes, you are here and you’re going to help us achieve our goals. That requires trust. No one’s going to come in and say, yeah, all of your system is bad; let me just remove it, create something new. That doesn’t really create the trust building part. So what I try to do is listen. 1. Ways to bring your teams together Encouraging cross-team collaboration by addressing silos and ensuring all teams work toward shared quarterly or company-wide goals. By creating alignment, you help teams see RevOps as a support system rather than an enforcer. This keeps communication channels open and creates buy-in. Based on your findings, introduce controls wherever needed to improve workflow. Example: If close dates aren’t being recorded properly, this could skew reports. Meet with sales, identify the root cause (e.g., manual data entry that is taking too much of a reps’ time), and provide solutions or tools to make their tasks easier. Ensure that controls are practical and developed with the trust built in the first 30 days. Foster internal consensus within teams so that these improvements are adopted seamlessly. 2. Navigate Organizational Politics Barrier Removal Larger organizations may have internal politics or ingrained processes that resist change. Find an internal “sponsor” who trusts and supports RevOps initiatives and can authorize actions to navigate any resistance. Trust and Consistency As you implement changes, make sure your efforts consistently demonstrate how RevOps can make work easier and more efficient for everyone. 3. Evolving the Tech

Top Revenue Forecast Tools
GTM, RevOps

13 Best Revenue Forecast Tools for 2026

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13 Best Revenue Forecast Tools for 2026 RevOps 11 min July 17, 2026 Forecast accuracy has been a stubborn problem for as long as there’s been a quota to hit. Most RevOps teams have lived through the gap between the number in the forecast deck and the number that actually closes, and the tools in this category all exist to shrink that gap. This list keeps to forecasting platforms genuinely built around pipeline prediction, deal-risk scoring, and forecast accuracy. Get our latest insights into your inbox What Is Revenue Forecasting? Revenue forecasting is the process of predicting future revenue based on historical data, current pipeline, and market conditions. It’s how a business turns “how much did we sell last quarter” into “how much should we plan to sell next quarter.” It helps RevOps and finance teams decide where to allocate budget, headcount, and resources with some confidence in the number. The mechanics haven’t changed much: gather historical data, identify the factors that actually drive revenue (leading indicators like pipeline conversion rate and engagement), build a model, and continuously validate it against what actually closes. What’s changed is what “the data” means. A forecast model built on CRM data with incomplete contact and activity records is still just a confident guess. No algorithm fixes an input problem, however good the model on top of it is. Why Forecasting Accuracy Is Still Hard Forecasting has always been difficult, but the reasons have shifted since the last version of this list: Data completeness, not just data volume.  Most CRMs are missing a large share of the activity that actually happened in a deal. Examples include meetings that never got logged, or stakeholders who were never added as contacts. A forecast model can only be as accurate as the pipeline data it’s built on. AI agents are now acting on forecast data, not just displaying it.  Where a forecasting dashboard used to be something a RevOps leader read and interpreted, more of that data now feeds directly into agents that flag risk, reprioritize pipeline, or trigger workflows. There is less human judgment sitting between the data and the action. Market and buying-committee volatility.  Longer sales cycles and larger buying committees mean more stakeholders whose engagement (or disengagement) can shift a deal’s trajectory without ever showing up as a stage change in the CRM. 13 Best Revenue Forecast Tools for 2026 Nektar: CRM data foundation that forecasting tools depend on Aviso AI: agentic forecasting and revenue execution Anaplan: enterprise financial and revenue planning Cien: AI-driven sales performance analytics Kluster: forecasting process standardization ZoomInfo Chorus: conversation intelligence backed by B2B data MadKudu: predictive lead and account scoring Celonis: process mining for sales-cycle bottlenecks Fullcast: territory, quota, and capacity planning Gryphon.ai: compliant call analytics and activity tracking SalesDirector.ai (Bigtincan): activity capture and revenue insights Upland Altify: account planning and opportunity management Vortini: forecasting and revenue-planning dashboards Overview of the 13 Best Revenue Forecast Tools 1. Nektar Every tool on this list predicts from the same underlying source: your CRM’s pipeline and activity data. If that data is incomplete with missing stakeholders, unlogged meetings, or contacts attached to the wrong opportunity, the forecast built on top of it is a confident guess dressed up as a number, no matter how sophisticated the model. Nektar addresses the layer underneath the forecast rather than the forecast itself. Data Foundation automatically captures every email, meeting, call, and calendar event across your team and writes it natively into Salesforce with zero rep effort and go-live in under two weeks. Time Travel retroactively corrects historical records as new context arrives, closing the gap that static, point-in-time capture tools can’t touch. Daisy AI surfaces revenue signals across categories including deal velocity, buyer engagement, and churn risk, giving forecasting and pipeline-inspection tools (including several others on this list) a materially more complete dataset to predict from. Nektar doesn’t compete with the forecasting and orchestration platforms on this list on prediction math. It’s vendor-neutral by design, sitting alongside those tools and making sure the data feeding their models is actually there. In production: Mimecast identified $80M in pipeline and $2M in incremental expansion revenue within 80 days of deploying Nektar. Brex built Nektar’s engagement data into daily CRO pipeline reviews. Key features: Zero-rep-effort capture across email, calendar, meetings, and calls Up to 12 months of historical backfill Revenue signals feeding downstream forecasting and pipeline tools Improves the data underneath other forecasting platforms rather than replacing them Best for: Teams whose forecast accuracy problem traces back to incomplete CRM data rather than a weak prediction model. 2. Aviso AI Aviso has moved from a pure forecasting tool to an agentic platform built around MIKI, a conversational orchestrator that can query pipeline data and trigger CRM updates directly, alongside 50+ pre-built revenue agents. The forecasting engine underneath is trained on historical deal and engagement data which remains the platform’s anchor. Key features: MIKI conversational orchestrator, predictive forecasting, real-time AI-driven deal coaching, no-code agent workflows. 3. Anaplan Anaplan is a connected-planning platform used well beyond sales for supply chain, workforce, and financial modeling, with revenue forecasting as one major use case. It’s been privately held under Thoma Bravo since 2022; the platform is worth knowing if procurement or vendor-stability questions come up in an evaluation. Key features: Hyperblock modeling engine, scenario and what-if planning, cross-functional connected planning, enterprise-scale collaboration. 4. Cien Cien uses AI to analyze historical sales data and benchmark rep performance against forecast outcomes, aiming to separate the deals that are genuinely likely to close from the ones that look healthy on paper but aren’t. Key features: AI-driven performance benchmarking, forecast accuracy analytics, sales coaching recommendations. 5. Kluster Kluster standardizes the forecasting and pipeline-review process itself: consistent cadences, repeatable reporting, and pipeline-funnel tracking so forecast calls run the same way every cycle rather than being rebuilt from scratch each time. Key features: standardized forecasting workflows, pipeline funnel tracking, automated reporting cadence. 6. Zoominfo Chorus Chorus has been part of ZoomInfo since 2021 and now runs on

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

5 Reasons for Low AI Sales Tool Adoption (And How to Fix It)
AI, RevOps, Sales

5 Reasons for Low AI Sales Tools Adoption (And How to Fix It)

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5 Reasons for Low AI Sales Tool Adoption (And How to Fix It) RevOps 11 min Updated: July 16, 2026 AI sales tools are everywhere in the stack now. AI SDRs for outbound, conversational assistants that summarize calls, AI-powered forecasting layers, AI note-takers, AI enrichment tools bolted onto the CRM. Adoption of the category has grown fast: 43% of sales reps now actively use AI tools in their daily work, up from 24% in 2023, a real jump in two years. It still hasn’t grown as deep as the buying pattern suggests. 42% of sales and marketing professionals report real dissatisfaction with the AI tools they’ve used, mostly citing data quality and hallucination issues. Gartner projects more than 40% of current AI sales pilots will be cancelled outright due to unclear value or runaway costs. Teams are buying AI sales tools faster than they’re getting reliable value out of them. That gap, bought fast, adopted slowly, is the story of this post. It maps onto five specific, well-documented reasons, each with a fix that doesn’t require waiting for a better model. Get our latest insights into your inbox The AI Sales Tool Adoption Gap, in Numbers 70% of sales organizations say data quality is the single biggest obstacle to getting real value from AI sales tools, ahead of cost, integration difficulty, or which vendor they picked. 42% of sales and marketing professionals report dissatisfaction with the AI tools they’ve used, citing data quality, security, and generative AI “hallucinations” as the main drivers, per ZoomInfo’s State of AI in Sales & Marketing 2025 report.  56% of sales professionals use AI daily, and those who do are roughly twice as likely to exceed their targets than reps who don’t, so the upside is real for the teams that get past the adoption barrier. 24% of sales organizations report low user adoption specifically, with 41% of reps actively resisting the AI tools they’ve been given, a rep-level resistance rate well above what most other sales tech categories see. None of these are model-quality problems. They’re data, trust, and rollout problems that happen to be wearing an AI label. 5 Reasons for Low AI Sales Tool Adoption (and How to Fix Them) 1. The Problem: The AI Tool Is Only as Good as the CRM Data Feeding It This is the most consistently cited barrier specifically for AI sales tools, and it’s the least visible until something visibly breaks. An AI forecasting tool, AI deal-risk flag, or AI-generated account summary built on stale contacts, missing stakeholders, and unlogged activity doesn’t produce a cautious, hedged answer. It produces a confident, wrong one, since the AI tool amplifies whatever data it’s given rather than correcting for what’s missing from it. This is also where an old, familiar problem gets new stakes. Dirty CRM data used to just slow a rep down doing a manual lookup. Fed into an AI sales tool that surfaces a recommendation or, increasingly, acts on the data directly, the same dirty record can now produce a wrong output at machine speed, before anyone reviews it. The Fix: Fix the Data Foundation Before You Add an AI Layer on Top Don’t bolt an AI sales tool onto a stack you already know has gaps in contact and activity data. Fix your data foundation as a first step.  Nektar’s 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. Whatever AI sales tool sits on top of that data, Nektar’s or anyone else’s, only gets more reliable once the foundation underneath it is complete. 2. The Problem: Multiple AI Sales Tools Lead to Mixed Priorities Selling doesn’t get easier just because more of the stack is now labeled “AI.” MuleSoft’s 2026 Connectivity Benchmark found the average organization now runs 957 applications, and only 27% of them are actually integrated. And organizations already using AI agents run even more on average, 1,103 apps versus 957.  Adding an AI SDR, an AI note-taker, and an AI forecasting layer on top of a stack that already doesn’t talk to itself just gives a rep three more disconnected tools to check, each with its own partial view of the deal. The same research found this is now a governance problem specifically, not just a sprawl one: 50% of AI agents currently operate in isolated silos, disconnected from any cohesive system, and 86% of IT leaders agree that without proper integration, AI agents introduce more complexity than value rather than less.  If the head of sales asks which AI tool actually flagged a deal as at-risk, a rep might have to check three separate AI features across three separate tools to find out, which defeats most of the point of automating it in the first place.   The Fix: A Unified Data Layer the AI Tools Actually Share An AI sales tool is only as useful as the data it’s working from, and that data has to be the same data every other tool in the stack sees, not a fourth silo with a chatbot interface on top. A unified data layer automatically captures contact, activity, and intent data, the same underlying record every AI tool in the stack should be reasoning over, instead of each one working from its own fragment. Platforms like HubSpot’s Dashboard and Reporting Software show what this looks like when it’s done well: sales, marketing, service, and revenue data centralized under one dashboard, so an AI-generated forecast or attribution report is drawing from the same complete picture a rep sees, not a narrower slice of it. That consistency is what determines whether an AI tool layered on top of the stack actually reduces the number of places a rep has to check, or just adds one more. 3. The Problem: Reps Who Get Burned Once Stop Trusting the Tool at All Trust, not raw capability, is the actual bottleneck for most AI sales tools, and

Top Relationship intelligence tools
RevOps

Top 10 Relationship Intelligence Tools for 2026

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Top Relationship Intelligence Tools for 2026 RevOps 12 min Updated: July 14, 2026 Gartner puts the average B2B buying group at 6 to 10 stakeholders, most of whom a rep will never speak to directly. Forrester’s research on self-serve buying shows a growing share of that group would rather research on their own than sit through a sales conversation.  These new realities mean that reps get less face time with more people who all have a vote. CRMs were supposed to solve this. In practice, they’ve solved storage, not visibility.  Salesforce only sees what a rep manually logs, and reps log a fraction of what actually happens in a deal. Most estimates put manually-captured activity at 20-30% of the real picture. The other 70-80% (the champion who went quiet, the executive who joined one call and never came back, the procurement contact nobody added to the opportunity) stays invisible until it costs you the deal. Relationship intelligence closes that gap. It automatically captures every meeting, email, and call tied to an account, structures it, and turns it into a map of who’s actually involved and how engaged they are, instead of asking reps to remember to write it down. Get our latest insights into your inbox The AI Shift: Why Relationship Intelligence Is No Longer Optional For most of CRM history, bad data was a hygiene problem. A rep mis-logged a meeting, a manager caught it in a pipeline review, someone fixed it. Humans sat between bad data and bad decisions. That buffer is disappearing. Salesforce and every major CRM vendor spent 2025 and early 2026 shipping AI agents that read CRM data and act on it directly. Tasks like updating opportunity stages, drafting follow-ups, reprioritizing pipeline,or  flagging churn risk, without a person checking the work first. Gartner projects that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from under 5% just a year earlier. That’s a real shift in what “clean data” is for. When a human interprets a messy CRM, they apply judgment and usually catch the obvious errors. When an agent acts on that same CRM autonomously, it doesn’t pause to sanity-check. It executes. A wrong contact role, a missing stakeholder, an activity logged against the wrong opportunity: these used to produce a bad report. Now they can produce a wrong decision, at machine speed, with nobody in the loop to catch it. The industry’s own numbers show how far the data foundation lags the AI ambition. 76% of organizations report that less than half their CRM data is accurate and complete, and 45% say their CRM data isn’t prepared for AI use at all. This is happening despite 92% of leaders calling data strategy critical to AI success. That’s the gap relationship intelligence tools now have to close: not just “give reps better visibility,” but “make the CRM trustworthy enough for an agent to act on unsupervised.” It also changed what these tools need to do. Three years ago, “relationship intelligence” mostly meant a dashboard: here’s who’s engaged, here’s who’s gone quiet. In 2026, the category is splitting between tools that still stop at surfacing insight and tools that structure data well enough to feed the agents now running on top of it like Agentforce, Copilot, a custom LLM pipeline, whatever your stack runs. The tools built only for human dashboards are starting to look thin next to the ones built to be a trustworthy data layer underneath autonomous execution. What Is a Relationship Intelligence Tool? A relationship intelligence tool automatically captures interaction data like emails, calendar invites, meetings, and calls across every stakeholder tied to an account. It then structures it into a usable picture: who’s involved, how engaged they are, and where the relationship is trending. It replaces the manual, incomplete version of this that lives in a rep’s memory (or doesn’t) with a system that captures it whether or not anyone remembers to log it. The best platforms do three things reasonably well: Capture passively. No rep has to open a new tab or fill in a field for the data to exist. Structure it against the CRM. Raw activity is useless until it’s mapped to the right contact, opportunity, and role. Surface it as a decision, not just data. A list of emails isn’t insightful. “Your economic buyer hasn’t been on a call in 34 days” is. In 2026, add a fourth: hold up as a source an AI agent can act on. If the data underneath your relationship map is wrong, every downstream agent, be it CRM-native or third-party inherits that error. Why a relationship intelligence tool matters 1. It shows you the whole buying committee, not the one or two contacts a rep happened to add Most opportunities in Salesforce list one or two contacts. The real buying group is usually 6 to 10. That gap is where deals quietly stall. A champion changes roles, a new VP joins a call and never gets a follow-up, and nobody notices until the deal is already cold. Relationship intelligence tools auto-detect new stakeholders from actual email and calendar activity and map them to the opportunity, so multithreading stops depending on a rep’s memory. 2. It tells you which relationships are actually strong, not which ones look strong on paper Meeting count isn’t engagement. A relationship intelligence platform weighs recency, frequency, and who’s actually responding, so you can tell the difference between a champion who’s still driving the deal and one who’s gone quiet. 3. It recovers deals you already wrote off Not every lead converts, and pipeline math means most won’t. But “lost” and “dead” aren’t the same thing. Relationship intelligence tools retain historical engagement data even for closed-lost opportunities, so when a prospect’s priorities shift six months later, you can see who was engaged and pick the relationship back up instead of starting cold. 4. It’s the data layer your AI initiatives are quietly depending on This is the part that’s new. If

10 Best Revenue Operations Software
RevOps

Best Revenue Operations Software for 2026

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Best Revenue Operations Software for 2026 RevOps 12 min Updated: July 15, 2026 Revenue operations exists to make sales, marketing, and customer success run as one connected system instead of three departments passing spreadsheets back and forth. The software behind that job has consolidated hard over the past two years. Several of the platforms on the 2025 version of this list don’t exist anymore in the form it described them, and the ones that remain independent are increasingly competing against combined entities with a lot more scale. That consolidation is worth understanding before you evaluate anything on this list, because it changes the buying question. It used to be “which point solution fits my stack.” Increasingly it’s “which of these platforms actually get maintained and improved after their acquisition, and which of them are the foundation the others depend on.” Get our latest insights into your inbox What is Revenue Operations? Revenue operations, or RevOps, is the operating model that runs sales, marketing, and customer success as one interconnected system instead of three functions working in silos. Done well, it drives visibility, accountability, and predictable revenue across the entire funnel.  RevOps has always cared about data quality. What’s new is that AI agents inside Salesforce, your sales engagement platform, or whatever custom tooling your team builds, are now reading that same CRM data and acting on it directly, without a human checking the work first.  When a human RevOps analyst worked from messy data, they applied judgment and caught the obvious errors. An agent doesn’t pause to sanity-check; it executes. That turned “Is our CRM data clean?” from a hygiene question into a governance question. And it’s reshaping what RevOps software actually needs to do. 10 Best Revenue Operations Software for 2026 Nektar – GTM data foundation and AI signal layer Gong – conversation intelligence and deal risk Groove, now part of Clari + Salesloft – sales engagement and revenue orchestration HubSpot Operations Hub – data sync and hygiene automation for HubSpot-native teams Aviso AI – agentic forecasting and revenue execution Kluster – forecasting and pipeline process automation Fullcast – territory, quota, and capacity planning Mediafly Intelligence360, formerly InsightSquared – revenue analytics and guided selling Breadcrumbs, now part of MadKudu –  predictive lead scoring ZoomInfo Chorus –  conversation intelligence backed by ZoomInfo’s B2B data Overview of the 10 Best Revenue Operations Software 1. Nektar Nektar the GTM telemetry platform that automatically captures every customer interaction and delivers clean data to your CRM, data warehouse, and AI applications. It does so with zero manual entry or adoption friction. As more of your GTM stack starts executing autonomously (Agentforce, your own AI agents, a forecasting model, anything reading CRM data and acting on it), the CRM has to be complete and correct continuously, or every agent built on top of it inherits the error. Nektar does this in two layers. Data Foundation captures every email, meeting, call, and calendar event across your team automatically and writes it natively into Salesforce, HubSpot, or Dynamics. Daisy AI sits on top of that foundation, surfacing revenue signals across multiple categories (buyer visibility, deal velocity, churn risk, marketing impact, and more) and putting a live engagement canvas directly on the Salesforce Opportunity tab. Nektar is vendor-neutral by design. It sits alongside Gong, Outreach, or Salesloft rather than replacing them, making sure the CRM data those tools depend on is actually complete. Unlike tools that lock your data in proprietary interfaces, Nektar acts as revenue signals infrastructure: capturing emails, meetings, calls, and Slack, then piping structured intelligence into Salesforce, Snowflake, Claude, and your entire stack. Enterprises & AI focused companies rely on Nektar to see complete buying committees (not the incomplete fragments in most CRMs), power AI systems with trustworthy data, and preserve institutional knowledge when people leave. In production: Mimecast identified $80M in pipeline and $2M in incremental expansion revenue within 80 days using Nektar’s telemetry. Chainguard’s CRO credits Nektar with the data foundation behind a 5x team scale-up. Brex built Nektar data into daily CRO reviews across sales, CS, and presales. Key features: Zero-rep-effort activity capture across email, calendar, meetings, and calls Time Travel retroactive data correction up to 12 months of historical backfill Daisy AI: 39 revenue signals across buyer visibility, deal risk, churn, and rep performance Vendor-neutral: works alongside Gong, Outreach, Salesloft, and Clari rather than replacing them Best for: Salesforce-first revenue teams, typically 800–5,000 employees, running a multi-threaded enterprise motion who need CRM data reliable enough for both reps and AI agents to act on. 2. Gong Gong built its category on conversation intelligence: recording, transcribing, and analyzing sales calls, and has extended into deal-risk scoring and forecasting. It gives RevOps teams a strong view of what happened on recorded calls, though its visibility stops at the edge of what Gong itself records; email and calendar activity outside a call require a separate capture layer. In 2026, Gong has pushed further into AI-native forecasting and coaching. Key features: call recording and transcription, deal risk warnings, closed-lost analysis, AI-assisted coaching. Evaluating Nektar against other platforms? Explore our in-depth comparisons with leading alternatives across features, data quality, AI readiness, and implementation. Nektar vs Gong Nektar vs Clari Nektar vs People.ai 3. HubSpot Operations Hub HubSpot Operations Hub keeps customer data synced and clean across connected systems for teams running on HubSpot. Its data-quality automation and sync tooling remain a strong fit for HubSpot-native RevOps teams; it’s not built to serve as a data layer for teams on Salesforce or a multi-CRM stack. Key features: Bi-directional data sync, automated data-quality rules, programmable automation. 4. Aviso AI Aviso has repositioned itself as an agentic AI platform for GTM teams, built around an orchestrator called MIKI that accepts natural-language queries and executes CRM updates directly, alongside a library of 50+ task-based revenue agents and a no-code GTM Agent Studio for teams to build their own agent workflows. Its forecasting engine still anchors the platform, now paired with real-time AI avatars for role-specific coaching and deal guidance.

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 Agency HQ Review Record Best Fit RevPartners Miami, FL 5.0 / 450+ (HubSpot Directory) HubSpot-centric SaaS, deepest RevOps bench New Breed Vermont 5.0 / 580 (HubSpot Directory) HubSpot + demand gen combined Aptitude 8 New York, NY 5.0 / 250 (HubSpot Directory) Complex technical HubSpot architecture Winning by Design Menlo Park, CA Named enterprise clients (Adobe, Uber Eats, Calendly) Series B+ full lifecycle redesign Go Nimbly San Francisco, CA Named clients (Intercom, Watershed, Superhuman) PLG / hybrid GTM motions Carabiner Group Los Gatos, CA Part of SBI since 2024; enterprise clients Multi-tool, platform-agnostic stacks RevPal Bend, OR #1 Reply.io 2026, top 3 Revenue.io Diagnostic-first engagements Remotish Cincinnati, OH HubSpot-focused client base Ongoing monthly HubSpot RevOps Domestique Denver, CO Cited across multiple 2026 agency guides; Platinum HubSpot Partner Early-stage hands-on implementation 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

Enterprise revops playbook thumbnail
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

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.

Andy Mowat
GTM, RevOps

Future of RevOps: GTM Systems, Hiring Tactics and Career Strategies

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A RevOps Playbook on the GTM Power, Careers and Hiring Strategies A conversation with Andy Mowat Executive Summary Andy Mowat has navigated the go-to-market journey from every angle—entrepreneur, operator at four tech unicorns (Box, Culture Amp, Carta), and now founder of Whispered. In this conversation, Andy shares hard-earned lessons on what separates strategic RevOps leaders from tactical executors, why the GTM tech stack is dying, how to take control of executive interviews, and why most people are dangerously cheap about investing in their careers. This isn’t theory. It’s a playbook built from someone who’s been the “wrong person for the job” four times and figured out how to win anyway. Readers will learn: RevOps is evolving from an execution function into a strategic GTM decision engine. The best RevOps leaders earn trust by forcing trade-offs, not by saying yes to everything. Legacy GTM stacks are breaking. Data fluency and AI-ready systems are becoming mandatory. GTM engineers are emerging under RevOps to automate execution and scale insight. Most executive roles are never posted. Senior hiring happens through networks and backchannels. Hiring favors builders who can get into the weeds, not just managers of managers. Strong candidates take control of interviews and show how they think, not just what they’ve done. Career leverage now comes from networks, reputation, and visible thinking, not applications. Facebook Twitter Youtube From Accidental RevOps to GTM Architect: Andy’s Career Arc Andy did not plan to end up in revenue operations. He stumbled into it the way many of the best RevOps leaders do. Early in his tech career, he joined Upwork. There was no CRM. So he built one. There was no outbound engine. So he figured out how to send a million emails. There was no formal RevOps function. So he became it. This pattern repeated. At Box, post-IPO, he took over post-sales operations, then marketing ops. At Culture Amp, he helped scale revenue from roughly $5M to $150M. At Carta, he entered during another inflection point, surrounded by leaders who understood that GTM decisions compound quickly, for better or worse. Across these roles, Andy learned something that most operators learn too late. RevOps is not a service desk. It is the economic engine room. The Real Difference between Tactical & Strategic RevOps Most RevOps leaders think their job is to execute requests efficiently. Andy believes that is how RevOps loses credibility. The inflection point in his thinking came at Box, when the company’s CCO told him “he’s not getting headcount unless the business gives it to him”. Instead of asking for budget, Andy began forcing trade-offs. He showed Sales, Support, and Customer Success how RevOps leverage could outperform incremental hiring. When leaders realized that one RevOps hire could unlock more growth than two frontline hires, budget appeared quickly. Andy’s rule is simple. If RevOps says yes to everything, it is not strategic.If RevOps forces prioritization conversations with executives, it is. “The wrong answer is ‘We got it.’The right answer is ‘Here’s the priority order I see. If we disagree, let’s take it to the CRO.” — Andy Mowat Where RevOps is Actually Headed Andy does not believe today’s GTM stack survives the next five years. He is tracking more than a dozen “CRM 2.0” challengers. His core criticism of legacy tools is structural, not cosmetic. Current GTM systems have: Clunky user experiences Data models not designed for AI Endless bolt-ons that fragment signal quality The Non-Negotiable Skills a RevOps pro Must Have Data literacy. Know what ETL and DBT actually do. Tight partnership with product and data teams. Embedded analytics and BI inside RevOps. Automation ownership, not tool babysitting. He also pushes back on the myth of the “GTM Engineer” as a shortcut. There is no shortcut. But there is a new function emerging. “GTM Engineers should live under RevOps.Their job is to automate the business and innovate for the reps.” — Andy Mowat https://youtu.be/H3CesaCmKWA What is Whispered? Whispered did not start as a company. It started as a survival mechanism. After a failed startup, Andy found himself asking a question many senior leaders never admit out loud.“Will anyone Hire me again?” At senior levels: Roles are rarely posted. Recruiters control access. Company quality is opaque. Networks go stale quietly. “Your next role won’t be posted. It’ll be whispered.” — Andy Mowat Whispered is designed for VP+ GTM leaders who are curious but cautious. It combines: Career playbooks Company backstory intelligence Unposted role discovery Network swarming across 300,000+ first-degree connections A private community that trades signal, not hype People join for roles. They stay for the network. Strategies for Hiring Senior Executives Through Whispered Hiring, Andy has interviewed dozens of CEOs, CROs, and CMOs about how they evaluate senior talent. Several patterns repeat. 1. Back-Channels Are the Highest Signal Everyone uses them. Everyone admits it. The best advice he heard recently: “Back-channel before you fall in love with a candidate.” 2. Builders Are in Demand Even at senior levels, companies want leaders who can still get into the weeds.AI has increased this expectation, not reduced it. 3. Rigid Thinkers Lose Andy calls it anti-rigidism, not ageism. Leaders who cannot adapt get filtered out quickly. 4. Slope Matters, but Only with Pattern Recognition High-growth companies love people who can outgrow their role. But leadership teams need both: Builders with slope Operators with scars Out perform your next Interview call Andy comments, treat your interview as a Sales call. Andy’s favorite interview opener is disarming. “I’m excited to meet you. What questions do you have?” Then he watches. Great candidates take control. Weak candidates wait to be prompted. Lazy questions kill momentum. Deep questions reveal how someone thinks. Interview Question Upgrade Instead of:“What’s your strategy?” Ask:“Here are three GTM constraints I see. Which one worries you most right now?” The goal is not to impress. It is to create signal. Personal Brand Without Becoming an Influencer Andy draws a line between thought leadership and performance. He writes because he cares and because writing clarifies thinking. It

RevOps

Building Revenue Engines that Scale: Lessons on Forecasting, Alignment and Multi-Product Complexity

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Building Revenue Engines that Scale: Lessons on Forecasting, Alignment and Multi-Product Complexity A conversation with Jeff Perry Executive Summary This article unpacks the operational blueprint behind scaling a revenue org from $15M to $500M+ ARR, while steering three parallel businesses at once. It’s distilled from an in-depth conversation with Jeff Perry, Chief Revenue Officer at Carta, who brings more than two decades of GTM leadership experience across Oracle, DocuSign, and Carta. Readers will learn: How Jeff’s career evolved through three distinct phases: growth, scale, and building What the “What’s Your Gut?” forecasting method reveals about improving accuracy Why cross-functional alignment is the real competitive advantage in revenue operations How to manage multi-product complexity with different ICPs and buying motions The characteristics that separate great sellers and managers from good ones Where AI fits (and doesn’t fit) in modern revenue operations   Facebook Twitter Youtube The Journey: Three Career Arcs That Shaped a Revenue Leader 1. Oracle: The Growth Phase Jeff started his career at Merrill Lynch depositing physical stock certificates—ironic given he now leads Carta, the company eliminating physical certificates. After a brief stint, he spent many years at Oracle where he learned: Sales fundamentals through structured training programs Leadership principles by observing great (and not-so-great) mentors Scale operations as Oracle grew from 30,000 to 120,000 employees Resources were abundant. Revenue operations, sales strategy, and training programs just happened. You operated inside a well-oiled machine. 2. DocuSign: The Scale Phase Jeff made what many considered a lateral or downward move: from leading 250+ AEs at Oracle to managing a 20-person SMB team at DocuSign. Why he did it: He needed to prove he could operate in a smaller, scrappier environment where: You’re hands-on with planning and execution Resources aren’t automatic You build the machine, not just run it Over four years, Jeff doubled his team size and took on additional verticals. This experience opened the door to Carta. 3. Carta: The Building Phase Jeff joined Carta in late 2018 when the company was at ~$15M ARR with 275 employees. Today: $500M+ ARR 1,800 employees Three distinct businesses operating under one roof “Sometimes you have to get one door opened up to lead to the next door. Oracle opened the DocuSign door. DocuSign opened the Carta door.” — Jeff Perry The Multi-Product Challenge: Managing Three Companies Inside One When Jeff joined Carta, it was a single-product cap table business. Today, it’s three distinct revenue engines. Venture-backed companies Venture Capital firms Private Equity The Growth Strategy: Classic Spreadsheet-to-Software Carta’s playbook: Identify a spreadsheet problem (cap tables, back office GL, ownership tracking) Build software to solve it Add adjacent products that create cross-sell and upsell opportunities “I can’t have one AE that sells cap tables to venture-backed C-Corps and fund administration to private equity firms. We’ve built teams within that align to the ICPs” — Jeff Perry Building Multi-Segment GTM Systems Without Chaos Each market Carta serves has its own logic: Startups care about cap tables, compensation benchmarks Venture firms care about GL automation, fund admin Private equity teams want scenario modeling and ownership accuracy The Challenge Different products have different: ICPs (leading to data segmentation issues) Sales cycles (transactional vs. enterprise) Buyer journeys Conversion benchmarks Example issue:A prospect reaches out to Jeff about their product. Jeff searches the CRM. The company isn’t there. Why? They were doing business under a different name. The data doesn’t connect. The RevOps Implication: As you add products with different ICPs, you can’t force unified systems. You need: Separate sales teams aligned to buyer personas Different quota structures Distinct sales motions (transactional vs. enterprise) The “Don’t Lose Alone” Philosophy: Why Lone Rangers Fail Jeff flips the classic advice: people say “Don’t try to be the hero.” He says don’t lose alone. Early-career reps want to be the savior who lands the big deal at the end of the quarter, makes a bunch of money, and gets recognized as the hero. Why this backfires: You limit your access to support and expertise Leadership lacks visibility to help you close You give yourself a lower probability of winning You don’t build career capital beyond the one deal The better approach: Involve your leadership team, product experts, and delivery teams early. “It does no good to be at the end of a quarter and say, I delivered this X hundred K deal and be the hero. You give yourself a better chance to win by involving the right people along the way.” — Jeff Perry Your future relationship builders are the SDRs learning your business today. If you automate away those roles, you lose the talent pipeline that becomes your future AEs and managers. Cross-Functional Alignment: The Secret Competitive Advantage Carta’s gone from 275 employees in 2018 to ~1800 today. Alignment usually decays with scale. But Jeff argues the opposite is possible, if leaders treat every metric like a shared asset. Marketing isn’t feeding leads to sales.Marketing and sales are feeding the same revenue engine. “Nicole, Carta’s CMO, doesn’t look at it as Jeff’s revenue number. She sees it as our revenue number.” — Jeff Perry Sales + Marketing: One Team, One Number The old model: Marketing owns lead generation Sales owns revenue Finger-pointing when numbers miss New model: Shared pipeline ownership Joint accountability for closed revenue Integrated planning across demand gen and sales capacity No classic sales-marketing friction Product isn’t building in a vacuum.Product is reacting to customers at lightning speed. He describes a moment at a Napa event: Prospects gave feedback at 11am.By evening the CPO confirmed engineers were already building on it. That’s alignment at operational speed. RevOps as the Connective Tissue Jeff’s perspective on where RevOps fits: It depends on company culture and structure, but: RevOps often has the clearest view of: The buyer journey Data flow across systems Change management needs Narrative resonance through metrics Many CEOs include RevOps in the small leadership group for exactly these reasons. The “What’s Your Gut?” Forecasting Method The problem with traditional forecasting is that most forecast calls

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