Sales Data Quality in the AI Era: 6 Steps to AI-Ready CRM Data CRM 9 min October 27, 2026 Executive Summary Sales data quality used to be a reporting problem. Bad data meant a wrong dashboard, and someone usually caught it. In 2027, AI agents act on CRM data directly, routing leads, prioritizing pipeline, and drafting outreach with no human checking each decision.  Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. The biggest gap in most CRMs is missing data: emails, meetings, and contacts that never get entered. This guide covers six steps to fix sales data quality at the source. Most revenue teams already know their data is weak. 76% of CRM users said less than half of their organization’s CRM data is accurate and complete, and 37% said poor data quality had cost them revenue directly. A Gartner survey of 1,203 data management leaders found 63% either do not have, or are not sure they have, the right data management practices for AI. Get our latest insights into your inbox What is Sales Data Quality? Sales data quality is how well the information in your CRM reflects what actually happened with your buyers: who you talked to, when, about what, and where each deal stands. High-quality sales data is complete, accurate, and current enough that people and AI agents can make decisions on it without second-guessing. Gartner analysts recommend assessing data for AI readiness across five dimensions (Demand Gen Report). Applied to sales data, they look like this: Dimension What it means in a CRM Common failure Completeness Every customer interaction and stakeholder is captured Emails, meetings, and buying group contacts never logged Accuracy Records reflect reality Activity on the wrong opportunity; outdated titles and stages Consistency The same fact matches across systems Conversation, engagement, and scheduling tools each log the same meeting differently Timeliness Data is current when decisions are made Activity logged days later, or at the end of the quarter Tagging Records are classified so systems can use them Missing buyer roles, activity types, or opportunity associations Traditional data quality work focuses on accuracy: fixing typos, merging duplicates, standardizing fields. In sales, completeness is usually the bigger problem, because the data that matters most was never entered. Why Sales Data Quality Matters More With AI When people read CRM data, they compensate for gaps. A manager knows a rep was in three meetings last week even if none were logged. AI agents have no such context. They take the CRM at face value and act on it at scale. That changes the cost of bad data. A missing buying group contact used to mean a less accurate report. Now it can mean an agent recommending the wrong next step, routing a lead from an active account as net new, or flagging a healthy deal as at risk. Salesforce’s State of Sales 2026 found high-performing sales teams were far more likely to prioritize data hygiene (79%) than underperformers (54%), and 51% of sales leaders using AI said disconnected systems are slowing their AI initiatives. 6 Steps to Improve Sales Data Quality in 2027 Step 1: Define “AI-ready” for each use case and assign ownership Gartner’s first recommendation for AI-ready data is to align data sources with specific AI use cases (Gartner). A lead-routing agent needs accurate account matching and recent engagement. A forecasting model needs complete activity history and buyer roles on every open opportunity. Write down which fields and objects each agent or model depends on, set a quality threshold for each, and name an owner, usually RevOps, who is accountable for meeting it. Include access and privacy rules in the same governance document so security is designed in from the start. Step 2: Fix completeness at the source with automatic capture Most sales data quality problems start with data that was never entered. Reps do not log every email, meeting, and call, and no amount of training changes that for long. Automatic activity capture writes every customer interaction across email, calendar, meetings, and calls into the CRM, with no rep effort. This is the single largest quality improvement most teams can make, because it fixes completeness and timeliness at once. This is what Nektar does. It captures every interaction, structures it with machine learning, and writes it natively into Salesforce. Time Travel™ backfills historical activity on deployment, so AI models start with a full history rather than a blank slate. Step 3: Automate association and tagging Captured activity only helps if it lands on the right record. An email logged against the wrong opportunity is worse than no email, because it misleads every report and agent downstream. Automate the association of each interaction to the correct contact, account, and opportunity, create contacts for anyone engaged who is not in the CRM, and assign buyer roles on opportunity contact roles. This addresses accuracy and tagging, two of Gartner’s five dimensions, without manual work. Step 4: Deduplicate and reconcile sources Growing tech stacks create consistency problems. The conversation intelligence tool, the sales engagement platform, and the scheduling tool may each write the same meeting to Salesforce, and a meeting with three attendees can be logged three times. Decide which system is the source of truth for each activity type, deduplicate using shared identifiers such as email message IDs and calendar event IDs, and merge duplicate contacts and accounts on a schedule rather than in annual cleanups. Step 5: Enrich with external data, and know what it covers Third-party providers such as ZoomInfo and Clay fill in firmographics, titles, and contact details, and increasingly expose that data to AI agents directly. Enrichment improves accuracy on who a company and person are. It cannot tell you what your team discussed with them, who attended last week’s evaluation call, or which stakeholder went quiet. That first-party engagement data has to come from your own interactions, which is why enrichment works best on top of complete activity capture. Step 6: Monitor

How to Improve CRM Adoption in 2027: 5 Strategies for the AI Era CRM 9 min October 27, 2026 Executive Summary For two decades, companies have tried to fix CRM adoption by changing rep behavior: more training, stricter mandates, incentives for logging activity. The results have barely moved. In 2027 the problem matters more, because the CRM’s most demanding users are no longer managers reading dashboards. They are AI agents that act on CRM data directly. The fix is to stop treating reps as the CRM’s data entry layer. Capture activity automatically, give reps useful context back, and measure adoption by how complete the data is rather than how often reps log in. The numbers explain why reps resist. Salesforce’s State of Sales 2026, a survey of 4,050 sales professionals, found the average seller spends only 40% of the week selling. The rest goes to meetings, prospecting, planning, quotes, and manual data entry. Every minute spent updating the CRM is a minute not spent with a buyer, and reps know it. The result shows up in the data. In Validity’s State of CRM Data Management in 2025, 76% of CRM users said less than half of their organization’s CRM data is accurate and complete, and 37% said poor data quality had cost them revenue directly. Get our latest insights into your inbox What is CRM Adoption? CRM adoption is the degree to which a sales organization actually uses its CRM as the system of record: whether customer interactions, contacts, and deal updates make it into the system, and whether people and tools rely on that data to make decisions. Low adoption shows up as missing activity, sparse contact roles, stale opportunities, and forecasts nobody trusts. Why CRM Adoption Keeps Failing The root cause has not changed since the first sales CRM: the system depends on reps to type in what happened. Reps are paid to sell, so data entry loses every time it competes with a buyer conversation. Three patterns make it worse. First, the work is invisible to the rep. Logging an email helps the forecast, the manager, and marketing attribution, but rarely helps the rep close the deal in front of them. Second, reps often see the CRM as a surveillance tool, a record of their activity that will be used against them in a pipeline review. Third, stacks keep growing. Sales teams use an average of eight separate tools and each one creates another place where customer context lives outside the CRM. Training, mandates, and incentives address none of these. They ask reps to do more of the work they already avoid. What AI Agents Changed The CRM now has a new class of user. Agentforce, Microsoft Copilot, and custom agents read and write CRM records directly, and Salesforce’s April 2026 Headless 360 launch opened the full platform to agents through APIs and MCP tools. These users never skip a field. They also never question what they find. That makes incomplete data far more expensive. A manager reading a sparse opportunity can call the rep. An agent prioritizing pipeline, drafting outreach, or scoring risk acts on the gap with full confidence.  In Salesforce’s 2026 research, 51% of sales leaders using AI said disconnected systems are slowing down their AI initiatives, and high-performing teams were far more likely to prioritize data hygiene (79%) than underperformers (54%). Gartner now advises sales leaders to build a centralized context layer connecting enterprise data, systems, and seller judgment before scaling agents. So the goal of CRM adoption has shifted. It used to get reps to use the CRM. Now it is  to ensure the CRM reflects reality, whether or not reps touch it. 5 Strategies to Improve CRM Adoption in 2027 1. Take data entry off reps completely The most effective CRM adoption strategy is to stop depending on reps for data. Automatic activity capture writes every email, meeting, calendar event, and call into the CRM, associated with the right contact, account, and opportunity, with no clicks, plugins, or “add to CRM” buttons. Partial automation that still asks reps to approve or tag each activity tends to decay within a quarter, so look for capture that requires zero rep behavior change. This matters most when teams grow fast. Chainguard scaled its sales team from 50 to 430 reps in under six months with Nektar capturing activity in the background, so every new hire had complete CRM coverage from day one without any logging training. 2. Capture contacts and buying groups automatically Opportunity contact roles are where manual adoption breaks down most visibly. Reps add the champion and maybe one other person, while finance, security, IT, and executive sponsors never make it into the CRM. Automate contact creation for anyone a rep emails or meets with, assign buyer roles, and associate them with the right opportunity. Managers get an honest view of multi-threading, and marketing gets the full list of people sales actually engaged. 3. Give reps something back in the flow of work Reps engage with the CRM when it saves them time. Once activity and contacts are captured automatically, use that data to hand reps useful context where they already work: an account summary before a call, recent engagement across the account team, the stakeholders who have gone quiet, and AI-drafted follow-ups grounded in what actually happened. The CRM stops being a reporting chore and becomes a source of prep. This also helps with the surveillance problem. When reps see the data working for them, it reads less like monitoring. Be transparent about what is captured and how it is used, and use privacy controls to exclude personal or sensitive communications. 4. Consolidate where customer data lives Every tool that holds its own copy of customer interactions makes the CRM less complete. Map which tools capture email, meetings, and calls today, decide which one writes the system-of-record activity to the CRM, and deduplicate the rest. Salesforce’s 2026 research found more than 80% of sales teams without a unified platform plan

CRM Data Capture: How to Deal with Missing Data from CRM RevOps 10 min Updated: September 8, 2026 Data is often called the new oil of the modern business world, and companies spend real money trying to extract the right data from the right sources. The same is true for a sales team specifically. Reps spend an average of 7 hours per week on CRM data entry. And even after all that effort, so many contacts they actually deal with never make it into the CRM at all. Most organizations can extract data. Fewer can actually use it well. That gap is what happens when CRM data capture isn’t genuinely high quality. This guide covers why that matters, which solutions actually close the gap and where each one stops, and what to do about selling effectively even while the gap still exists. Get our latest insights into your inbox Why High-Quality Data Capture Matters CRM data capture is how businesses gather and consolidate information about potential and existing customers. CRM systems accumulate a genuinely large amount of valuable data, which sales teams and relationship-focused dealmakers use to move prospects toward becoming customers, and eventually toward becoming referral sources. 70% of organizations report bad data has cost them at least $500,000, with 37% reporting losses exceeding $1 million. Most data problems trace back to the initial data capture step itself. Since a CRM platform is usually a significant financial commitment, getting an actual return on it depends directly on capturing and maintaining high-quality, accurate customer data from the start, not fixing it after the fact. Here’s what high-quality CRM data capture actually improves: 1. Bad Data and Lack of Trust When reps lack relevant information about a prospect, their interactions become less meaningful, resulting in overlooked opportunities and deals that quietly fail. 2. Inaccurate Forecasts Inaccurate forecasting and reporting creates real strategic problems, making it hard for management to make timely, data-driven decisions. 3. Automation Errors Costly automation mistakes, a segmentation error that sends the wrong message to the wrong prospect, for instance, can damage brand reputation. 4. Bad Customer Experience Wrong contact information hurts the customer experience directly and erodes trust, potentially leading to dissatisfaction and lost credibility. 5. Financial Pain Poor or unreliable data creates real financial waste: sending materials to the same customer multiple times because of duplicate records, or losing time and effort to integrations that break because the underlying data doesn’t match. 6. CRM Issues Data problems affecting tools like Salesforce and HubSpot slow a team’s progress and can disrupt marketing and relationship-building work until they’re actually resolved. Clean data matters, but achieving it is genuinely demanding. Manual entry into spreadsheets is prone to errors: omissions, duplicates, inaccuracies. It’s also incredibly time-intensive, and every minute a rep spends on manual CRM entry is a minute not spent building a relationship. This is exactly why automating CRM data capture matters. Read Salesforce Email Integration Guide Want to get email activity into Salesforce automatically? See how Salesforce email integration works and the different ways to keep your CRM activity up to date.  What Actually Fills the Gap in Your CRM Most guides on this topic describe the problem in detail and then jump straight to “automate it.” That skips the actual decision: automate it how. There are four real categories of solution here, and each one closes a different part of the gap while leaving another part untouched. 1. Manual Entry The default in most CRMs, and the weakest option by a wide margin. A rep types in what they remember from a call or copies a name and email from a signature. Where it stops: It doesn’t scale, doesn’t capture what a rep forgets or never noticed, and introduces a real error rate on every entry. It also captures nothing retroactively, if a contact wasn’t logged three months ago, that history is simply gone. 2. Enrichment Vendors Tools like ZoomInfo, Cognism, or similar data providers append third-party data to an existing record: a verified email, a phone number, a job title, company firmographics. Where it stops: Enrichment tells you about a company and a person in general. It doesn’t tell you what’s actually happening in your specific relationship with them, whether they’ve responded to your last email, attended your last meeting, or gone quiet for three weeks. It’s excellent at filling in a blank field on a contact you already have. It can’t create a contact your team never knew existed, and it can’t capture the activity history that shows whether that contact is actually engaged. 3. CRM-Native Capture Salesforce’s Einstein Activity Capture and similar built-in tools automatically log emails and calendar events without a separate integration. Where it stops: Native capture typically works at the Account level, not the Opportunity level, so activity gets attached to the company broadly rather than the specific deal it actually relates to. It also usually caps how much history it retains (EAC, for instance, keeps roughly six months), stores captured data outside Salesforce’s own database rather than as native, reportable objects, and loses everything it captured if you ever switch away from it. It solves “did we get an email logged at all” without solving “is this logged against the right deal, with the right contact roles, in a format I can actually build a report on.” 4. Dedicated Activity Capture Purpose-built tools like Nektar capture contact and activity data specifically, matching it to the correct Account, Opportunity, and Contact automatically, including contacts a rep never manually added, and retroactively backfilling historical activity the moment a new Opportunity is created. Where it stops: This category solves the completeness and matching problem, whether the right data is captured and attached to the right record, but it’s not a replacement for enrichment. It doesn’t append third-party firmographic or technographic data the way an enrichment vendor does. The two are complementary: dedicated activity capture tells you what’s actually happening with a contact; enrichment tells you more about who that contact is. The

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

5 Strategic Benefits of Improving CRM Data Quality CRM, RevOps 11 min Updated: August 13, 2026 The global CRM market continues to grow briskly, but market size says nothing about how much value businesses are actually extracting from what they’ve bought. Realizing a CRM’s true value has never really been about the CRM itself, it’s about the quality of the data living inside it, and that continues to be a major, unresolved challenge for most businesses. Estimates on the scale of the problem vary, but they’re consistently alarming: a large share of CRM data is incomplete, stale, or duplicated in any given year, and typical annual decay runs in the range of 22.5% to 70% depending on industry and record type.  These inefficiencies cost real money, Gartner’s widely cited estimate puts the average cost of poor data quality at $12.9 million per organization annually. The problem compounds over time too: the more data that accumulates in a CRM without active management, the messier it gets, until the system that was supposed to drive growth quietly turns into dead weight. Improving and enriching CRM data is the first real step toward realizing what a CRM was actually bought to do. High-quality data gives revenue leaders a genuinely solid foundation, one that holds up even through downturns and market uncertainty. This guide covers the real cost of poor CRM data, and five strategic benefits businesses see once they fix it. Get our latest insights into your inbox Low-Quality CRM Data Leads to High Costs Beyond the obvious cost of storing stale, incorrect, or missing data, several hidden costs quietly drain revenue: higher cost per customer, lower conversion rates, reduced revenue, and thinner margins. Forrester’s research has found that persistently low-quality data across enterprise systems robs leaders of real productivity, since they end up continuously re-verifying data just to trust it enough to act on. Any decision made on poor-quality data is inherently risky, whether it’s a marketing campaign, sales-marketing alignment, pipeline forecasting, or buying-committee strategy. A few specific ways this shows up: 1. Frustrated Sales Reps Reps have a genuinely conflicted relationship with their CRM: valuable when it works, resented for the manual entry it demands. A majority say they’d consider leaving their role if their organization doesn’t invest in fixing CRM data quality. Every hour spent on manual entry is an hour not spent building the relationships that actually drive quota. 2. Incorrect Sales Forecasting Accurate forecasts let leaders allocate resources efficiently and maximize returns. Poor-quality data produces forecasts that are wrong in ways that compound, and the result is resources spent chasing outcomes that were never realistic in the first place. 3. Poor ROI From the CRM The CRM remains one of the largest tech investments most businesses make, and most fail to extract full value from it because the underlying data is riddled with inefficiencies. Left unaddressed, a CRM stops being an asset and starts becoming a quiet source of revenue drain. 4. Failed Marketing Campaigns Customers expect real-time, personalized messaging, and poor-quality data turns that expectation into a liability. A campaign built on a list of stale contacts spends real budget on an idea that was never going to convert, and repeated failures like this damage brand reputation over time. How to Improve CRM Data Quality Fixing data quality at the root is the first step toward the kind of GTM alignment revenue leaders actually want. That means investing in technology that doesn’t add pressure to sales, marketing, or customer success teams, but instead works quietly in the background while those teams focus on their actual jobs. CRM data entry is the clearest example of where this matters. It’s still largely manual, which eats rep time and introduces exactly the kind of error, or missing information, that costs deals. The fix isn’t just automating entry, it’s automating entry and enrichment together, so GTM teams always work from data that’s both current and complete, with a layer of intelligence on top that actually helps teams scale. 5 Strategic Benefits of Improving CRM Data Quality 1. Accurate Visibility of the GTM Funnel Accelerating pipeline development requires sales, marketing, and customer success working from the same high-quality data. With accurate, reliable CRM data, the entire organization references one shared, data-driven picture instead of three partial ones, closing a lot of leakage at the root. Everyone gets clear answers to the questions that actually matter: how many qualified leads are genuinely in the pipeline, which contacts are most likely to engage, and which leads should actually be disqualified. If a deal is stuck at a specific stage and the data shows that sharing a case study at that exact point tends to accelerate similar deals, sales and marketing can act on that together, and both sides can see the impact of the collaboration directly in the pipeline. Marketing also gains confidence in the leads it hands to sales, and capturing previously missing contacts gives inside sales a whole set of people they didn’t even know existed to reach out to. 2. Increased Focus on Deals That Actually Convert Pipelines bloat over time with opportunities that add little real value, often because reps resist dropping a deal even after activity has gone quiet, assuming a bigger pipeline always looks better. The truth is the opposite: every minute spent on a deal that’s not really live is a minute not spent on one that could actually close. Complete, accurate CRM data tells you specifically which deals need to come out of the pipeline, freeing the team to focus resources on the accounts that are genuinely live. Sales managers get a clear picture of exactly where the pipeline is bloated, can act quickly on stalled deals, and can build a more predictable quarter as a result. Tracking rep activity data directly is a strong leading indicator here, showing which deals are real and which just look real on paper. 3. High Engagement With the Buying Committee Selling is fundamentally about relationships, and the B2B

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

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

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