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proven ways to increase sales productivity
Sales

How to Increase Sales Productivity in 2027: 5 Proven Ways

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How to Increase Sales Productivity in 2027: 5 Ways to Redesign Seller Work for AI Sales 9 min November 4, 2026 Executive Summary Sellers still spend most of their week on work that is not selling, and adding AI tools on top of old workflows has not fixed it. Gartner’s May 2026 research shows the teams pulling ahead are redesigning seller roles: AI handles research, messaging, signal monitoring, and next best actions, while sellers focus on the conversations where judgment and trust decide the deal. Organizations that give sellers AI-enabled next best actions are 2.6x more likely to achieve commercial growth. This guide covers five ways to make that shift, starting with the customer data every AI recommendation depends on. The average seller spends just 40% of the week selling, according to Salesforce’s State of Sales 2026. The other 60% goes to meetings, prospecting, quotes, planning, training, and manual data entry. Gen Z reps fare worse, selling 35% of the time and losing about two hours a week to data entry alone. AI is changing that equation fast. Gartner predicts that by 2027, 95% of sellers’ research workflows will begin with AI, up from less than 20% in 2024. However, more AI does not automatically mean more output. Gartner also expects AI agents to outnumber sellers 10 to 1 by 2028, while fewer than 40% of sellers will say agents improved their productivity. Get our latest insights into your inbox What is Sales Productivity? Sales productivity is the revenue a sales team produces relative to the time, people, and cost it takes. It combines efficiency (how well time and resources are used) and effectiveness (how well that effort turns into pipeline and closed deals). A team can be busy and efficient yet ineffective if the effort goes to the wrong accounts, and effective yet inefficient if every deal takes twice the work it should. In 2026 the definition includes AI. Gartner advises sales leaders to shift from traditional productivity measures to metrics that capture both human and AI contributions (Gartner). Productivity is now a property of the whole system of sellers and agents working together. The Reality of Sales Productivity Today Your bottom line will take a hit if reps don’t feel productive. Let’s first look at the problems plaguing sales teams today to find solutions. 1. Admin still crowds out selling Data entry, meeting prep, and CRM updates take the time that should go to buyers. Salesforce’s numbers above show the problem has barely moved in years. 2. AI is layered on top of old workflows According to Greg Hessong, Senior Director Analyst at Gartner, the most effective sales organizations are redesigning seller workflows so AI supports execution, recommendations, and orchestration, rather than bolting AI onto existing ways of working. When sellers are asked to do everything they did before plus manage new tools, productivity drops. 3. Customer context is scattered Interactions live across email, calendars, meeting recorders, engagement tools, and Slack. Salesforce found 51% of sales leaders using AI say disconnected systems are slowing their AI initiatives. AI recommendations built on partial context waste seller time instead of saving it. 4. Deals stay single-threaded Gartner’s 2025 buyer research found buying groups with low dysfunction were 13x more likely to report high-quality deals, and buyers who spent more time with supplier reps reported the least dysfunction. Sellers who cannot see the full buying group cannot manage it. Read Blog Related Read: Multithreading in Sales: The Modern Secret to Winning More Deals 5 Ways to Increase Sales Productivity in 2027 Here are 5 ways to increase sales productivity in 2027: 1. Redesign seller roles around what AI and humans each do best Start by deciding which work belongs to agents and which belongs to sellers. AI is well suited to account research, personalized messaging, signal monitoring, and next best actions, while sellers remain differentiated in empathy, judgment, contextual understanding, and value framing. Buyers agree. In a 2025 survey of 645 B2B buyers, buyers were 39 percentage points more likely to say a rep understood their needs than GenAI, and 32 points more likely to say a rep made them confident in the purchase. Map a typical rep week, tag each task as agent-led, human-led, or shared, and rebuild the role so the time agents free up goes to discovery, multi-threading, and helping buyers build the business case. 2. Remove admin at the source The fastest productivity gain is eliminating work entirely rather than speeding it up. CRM data entry is the clearest example. Automatic activity capture writes every email, meeting, calendar event, and call into Salesforce, matched to the right contact, account, and opportunity, so reps never log anything. It also gives every AI agent downstream a complete record to work from. This is the layer Nektar provides, with zero rep behavior change. It captures the complete customer interaction history and context your AI agents need to work. 3. Give sellers next best actions grounded in complete data Sales organizations that provide sellers with AI-enabled next best actions are 2.6x more likely to achieve commercial growth. The quality of those recommendations depends on what the AI can see. If half the buying group is missing from the CRM, or last week’s executive meeting was never logged, the next best action will be wrong. Build a centralized context layer connecting enterprise data, systems, and seller judgment before scaling agents. In practice that means complete activity history, every engaged stakeholder on the opportunity with a buyer role, and engagement trends the AI can read. 4. Coach on leading indicators and upskill on AI Coaching remains one of the highest-leverage manager activities, and AI raises its ceiling. Organizations that prioritize upskilling sellers on AI are 2.4x more likely to achieve strong revenue growth, and it is predicted that AI-driven enablement will deliver 40% faster sales stage velocity than traditional methods by 2029. Pair AI skills training with coaching on leading indicators that show where a deal or a rep is heading before

Nektar and momentum integration
Buyer's Guide

Nektar + Momentum: Complete Salesforce Context for AI

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Nektar + Momentum Integration: Connect Every Customer Interaction to Better Deal Intelligence Buyer’s Guide 9 min October 28, 2026 Momentum helps revenue teams turn customer conversations into structured insights and actions. Nektar complements that capability by ensuring customer interactions across email and calendars are captured, connected to the right Salesforce records, and available as structured CRM data. Together, they help close an important gap in revenue intelligence: the difference between understanding what happened on a call and understanding the full context of the customer relationship. Momentum records conversations and turns them into insights that can support deal reviews and AI-driven workflows. Nektar captures email and calendar activity, matches interactions to the right Contacts, Accounts and Opportunities, and keeps those associations correct as CRM records change. That distinction matters because not every important customer interaction happens on a recorded call. Emails, calendar meetings and the relationships surrounding an opportunity all contribute to understanding where a deal stands. With Nektar feeding connected customer activity into Salesforce, Momentum can draw on a richer CRM context for its AI-powered workflows. Get our latest insights into your inbox Why Integrate Nektar With Momentum? Sales conversations contain valuable signals: customer objections, buying intent, commitments, risks and next steps. Momentum helps teams extract those signals from conversations and turn them into actionable intelligence. But a call is one part of the customer journey. A prospect might raise a concern during a meeting, clarify it over email the following day, introduce another stakeholder in a calendar invitation, and involve an executive in a later conversation. Each interaction adds context to the deal. If that context is scattered across communication channels or associated with incomplete CRM records, revenue teams and AI systems may not have the full picture. This is where Nektar complements Momentum. Nektar captures customer emails and calendar interactions and connects them to the appropriate Salesforce Contacts, Accounts and Opportunities. It helps ensure that customer activity is not just collected, but associated with the CRM records where revenue teams need it. Momentum can then use the customer and deal context available in Salesforce alongside its conversation intelligence. The result is a more connected foundation for deal reviews, buying-group visibility, revenue workflows and AI. How Nektar and Momentum Complement Each Other The two platforms address different parts of the customer-context problem. Capability Nektar Momentum Customer interaction capture Captures email and calendar activity and connects it to Salesforce records. Records customer calls and captures conversational insights. Salesforce context Matches activity to the right Contacts, Accounts and Opportunities and keeps associations current as CRM records change. Uses Salesforce context and conversation insights to support deal intelligence and AI-driven workflows. Email activity Captures email interactions and writes structured activity into Salesforce. Can read email information once it is available in Salesforce. Buying-group context Connects customer interactions to the people and opportunities involved in the buying process. Adds insights from recorded conversations to the broader customer and deal context. AI readiness Provides structured, connected customer activity in Salesforce. Uses conversational insights and Salesforce context to support AI-powered workflows. The distinction: Momentum provides conversation intelligence. Nektar connects the email and calendar activity that helps complete the customer context in Salesforce. Neither needs to replace the other’s role. Together, they help revenue teams connect what customers say on calls with what happens across the rest of the buying journey. The Missing Piece: Email and Calendar Context in Salesforce Salesforce has increasingly emphasized that AI agents need trusted, relevant customer context to act effectively. Its Meet Momentum webinar makes the same point: when customer signals fail to reach Salesforce, agents operate without the full picture. But the solution isn’t simply to capture more conversations. It’s to connect the different interactions that make up a customer relationship. Consider a typical enterprise opportunity: A discovery call reveals the customer’s primary challenge. An email thread clarifies the requirements and brings a new stakeholder into the discussion. A calendar invitation identifies additional participants in an upcoming meeting. A follow-up conversation confirms the next steps and timeline. Momentum can provide insights from recorded conversations. Nektar helps ensure that email and calendar activity is captured and associated with the right Salesforce records, so those interactions contribute to the broader customer context. What Can You Build With Nektar + Momentum? Combining Nektar’s customer activity data with Momentum’s conversation intelligence creates opportunities to improve how revenue teams understand deals and act on customer signals. 1. More complete AI deal reviews Deal reviews are more useful when they account for the full customer relationship, not just the latest recorded conversation. Momentum can surface insights from customer calls, while Nektar brings email and calendar activity into the Salesforce context surrounding the deal. Together, these sources can help teams assess questions such as: What concerns has the customer raised across calls and email? Have agreed next steps been followed up? Which stakeholders are actively participating? Has a new stakeholder entered the buying process? Are there signs of engagement changing between meetings? The result is a richer evidence base for deal inspection. Revenue leaders can assess conversational signals alongside the activity surrounding them, rather than relying on call summaries alone. 2. Better buying-group visibility Enterprise deals rarely depend on one contact. Decision-makers, technical evaluators, champions, procurement teams and executives can all influence the outcome. Momentum can reveal who said what during recorded conversations. Nektar helps connect customer interactions to the relevant Salesforce people and opportunities, making it easier to understand engagement across the buying group. This can help RevOps teams identify: Which stakeholders are participating in conversations and email exchanges Which contacts are actively engaged with an opportunity Where stakeholder engagement is concentrated around a single person Where additional relationships may need to be developed How customer participation changes as an opportunity progresses The value comes from connecting these signals to the actual Salesforce records used for account and opportunity reporting. 3. More useful AI recommendations AI recommendations are only as relevant as the context behind them. An agent that sees a call summary but misses

How to Improve CRM Adoption in 2027_ 5 Strategies for the AI Era
CRM

Sales Data Quality: 6 Steps to AI-Ready CRM Data

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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

How to Improve CRM Adoption: 5 Strategies for 2027

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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

RevOps Automation_ 8 Workflows to Automate in 2027
RevOps

RevOps Automation: 8 Workflows to Automate in 2027

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RevOps Automation in the AI Era: 8 Workflows to Automate in 2027 RevOps 9 min October 27, 2026 Executive Summary RevOps has always been pulled between strategy and operations. Leaders want RevOps designing territories, pricing, and the GTM model. In practice, a large share of the week goes to cleaning data, fixing routing, reconciling reports, and chasing reps to update the CRM.  Validity’s State of CRM Data Management in 2025 found employees spend 13 hours a week hunting for basic CRM information, 46% of companies have no full-time owner for CRM data quality, and only 20% have formal data governance policies. AI agents promise to take that work off RevOps’ plate. They also raise the stakes. An agent that routes leads, updates opportunities, or forecasts pipeline acts on whatever is in the CRM, at machine speed, with no human reviewing each decision. Automating RevOps in 2027 hence starts with making that data trustworthy. Gartner predicts that by 2028, AI agents will execute 75% of RevOps tasks in workflow management, data stewardship, revenue analytics, and RevTech administration. The teams getting results are not automating the most workflows. They start with a few, ground them in complete customer data, and govern what agents are allowed to change.  This guide covers the 8 workflows worth automating first and how to keep them trustworthy. Get our latest insights into your inbox What is RevOps Automation? RevOps automation is the use of software to run the repeatable work of revenue operations across sales, marketing, and customer success: capturing and cleaning data, routing leads, maintaining the CRM, building reports, and keeping the revenue tech stack connected. It has moved through three stages. Rules-based automation handled triggers and workflows (if a lead scores above 50, assign it). AI-assisted automation added recommendations and summaries that a person still acted on. Agentic automation, the stage most teams are entering now, lets AI agents pursue a goal, decide the steps, and take action in the CRM and connected tools. Why RevOps automation matters in 2027 By 2028, 75% of RevOps tasks in workflow management, data stewardship, revenue analytics, and RevTech administration will be executed by AI agents. 68% of organizations are already scaling or optimizing AI across revenue functions, and 45% report less manual work as a result. The foundations have not kept up. In the April 2026 LXA and LeanData survey of 201 enterprise revenue leaders, 47% said manual processes that cannot scale are their top lead management failure, and only 50% were confident they could govern AI safely at scale. Gartner’s broader warning applies here too: through 2026, organizations will abandon 60% of AI projects that lack AI-ready data (Gartner). That puts RevOps in a new position. As agents take over execution, RevOps becomes the owner of the data and rules those agents run on. Gartner recommends RevOps leaders assess customer data across five areas before piloting agents: accuracy, consistency, completeness, timeliness, and tagging. 8 RevOps Workflows to Automate in 2027 1. Activity capture and CRM hygiene Reps do not log every email, meeting, and call, and manual logging mandates rarely last. Automating capture means every customer interaction across email, calendar, meetings, and calls is written into Salesforce and associated with the right contact, account, and opportunity, with no rep input.  The output RevOps should look for is a CRM that stays complete and correctly mapped on its own, including history from before an account or opportunity existed. This is the layer Nektar provides. A capture layer that keeps itself correct gives every downstream agent a trustworthy starting point. 2. Contact discovery and buying group mapping Most opportunities in Salesforce have one or two contacts attached, while real buying groups include finance, security, IT, and executive sponsors. Automating contact discovery means anyone a rep emails or meets with is created as a contact if missing, assigned a buyer role, and added to the opportunity’s contact roles. RevOps gets an accurate measure of multi-threading, such as the share of open deals with three or more engaged roles, without asking reps to maintain it. Learn More About Automation Buying Group Intelligence Here. 3. Activity deduplication and source reconciliation As stacks grow, the same meeting gets logged by the conversation intelligence tool, the sales engagement tool, and the scheduling tool, and once more for every attendee. Activity reports then overstate effort and skew attribution. Automate deduplication using shared identifiers such as email message IDs and calendar event IDs, and define one source of truth for each activity type. When you add a capture tool, confirm how it reconciles with what your other tools already write. 4. Lead routing and qualification Routing is one of the most mature agentic use cases. Agents such as Agentforce SDR and Microsoft’s Sales Qualification Agent research inbound leads, assess fit, and engage or hand off to a rep. Pair them with deterministic routing rules and SLA enforcement, so you can audit why each lead went where it did. Agents qualify better when account history is complete: a lead from an account with an open opportunity or a recent customer meeting should not be treated as net new. 5. Pipeline inspection and forecasting For every open deal, RevOps should be able to see the last two-way interaction, how many stakeholders are engaged, and whether activity is rising or falling, without asking the rep. Forecasting platforms such as Clari Forecast (now part of Salesloft) and Gong produce better calls when this engagement data is complete, and stale deals become easy to flag automatically. 6. Marketing attribution and ABM audiences Marketing attribution breaks when sales-engaged contacts never reach the CRM. Automate the link between campaigns and the people sales actually met on each opportunity, so marketing can see which touches influenced pipeline and build ABM audiences from first-party engagement rather than purchased lists alone. 7. Customer health, renewal, and expansion Health scores built only on product usage and support tickets miss the relationship signal: whether executives are still engaged, whether the champion has gone quiet, and whether new

10 Sales Productivity Tools for 2027
Sales

10 Best Sales Productivity Tools

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10 Sales Productivity Tools for the AI Era Sales 9 min October 26, 2026 Executive Summary Sales productivity tools used to mean faster data entry and better dashboards. In 2027 they mean AI agents that research accounts, draft outreach, qualify leads, and flag deal risk on their own.  Gartner expects AI agents to outnumber sellers 10 to 1 by 2028, yet predicts fewer than 40% of sellers will say those agents improved their productivity. The gap comes down to context: agents are only as useful as the customer data underneath them. This guide covers 10 tools worth evaluating this year, grouped by the job they do, and explains how to build a stack where the agents actually help. The average seller still spends only 40% of their time selling, according to Salesforce’s State of Sales 2026. GenZ reps fare worse at 35%, losing about two hours a week to manual data entry. Sales teams named AI and agents their top growth tactic for 2026, and sellers expect fully deployed agents to cut prospect research time by 34% and email drafting by 36%. That promise has a catch. Analysts warn that agents are no shortcut to productivity and recommend sales leaders build a centralized context layer connecting enterprise data, systems, and seller judgment. Most teams skip that step.  Data quality is another area of concern. 76% of organizations say less than half of their CRM data is accurate and complete. Agents built on that data produce confident, wrong answers at scale. Get our latest insights into your inbox What is Sales Productivity? Sales productivity measures how much revenue a sales team generates relative to the time, headcount, and cost it takes. It has two parts: efficiency (how well time and resources are used) and effectiveness (how well that effort converts into pipeline and closed revenue). In 2027, the definition stretches to include work done by AI agents. A team’s output now depends on how well reps and agents divide the work: agents take research, data entry, first-touch outreach, and follow-up, while reps spend their time on discovery, negotiation, and relationships. What are Sales Productivity Tools? Sales productivity tools are software that helps sellers spend more time selling and less time on administrative work. The category now spans five layers: Data foundation: captures customer interactions and keeps the CRM complete, so every other tool and agent has accurate context. CRM-native agents: AI agents built into Salesforce, Dynamics 365, or HubSpot that qualify leads, research accounts, and coach reps. Revenue intelligence and orchestration: conversation intelligence, forecasting, and sales engagement, increasingly sold as one platform. Prospecting and enrichment: external company and contact data plus AI research agents. Enablement: content, training, and coaching delivered in the flow of work. How to Choose Sales Productivity Tools in 2027 Start with the data foundation. Every agent in your stack reads from the CRM. If emails, meetings, and buying group contacts never reach Salesforce, each tool you add inherits the same blind spots. Check how a tool works with agents you already run. Most vendors on this list now offer a Model Context Protocol (MCP) server or native connectors to Agentforce, Copilot, or Claude. A tool that only works inside its own interface adds another tab to a rep’s day. Weigh adoption costs. Tools that require reps to log, tag, or update anything manually tend to decay. Favor tools that work in the background or inside surfaces reps already use, like Salesforce, Slack, and their inbox. Account for consolidation. Clari merged with Salesloft, Seismic merged with Highspot, and Salesforce has been acquiring AI companies across the Agentforce stack. Ask vendors for a 12-month roadmap and how merged products will integrate. The 10 best Sales Productivity Tools for 2027 Data foundation 1. Nektar Best for: Salesforce teams that want complete, AI-ready CRM data without asking reps to log anything. Nektar is the data layer underneath the rest of your stack. It automatically captures every customer interaction across email, calendar, meetings, call transcripts, and Slack, structures that data with machine learning, and writes it natively into Salesforce. Activities land on the right contact, account, and opportunity. Buying group members who never made it into the CRM are discovered and added. Time Travel™ backfills historical activity on day one, and the data keeps correcting itself as relationships change. The productivity gain comes from two directions: Reps stop doing data entry, because capture requires zero behavior change.  Every agent and tool on this list gets a complete picture of each deal to work from.  Mimecast used Nektar’s engagement data to power an internal Expansion AI tool that drove $2M in expansion revenue and $80M+ in pipeline in its first 80 days. Read the full story here. Where it fits: Nektar complements agents and revenue platforms. Agentforce, Gong, and forecasting tools all produce better output when the CRM reflects what actually happened with buyers. CRM-native AI agents 2. Salesforce Agentforce for Sales Best for: Sales Cloud customers who want AI agents working directly on Salesforce records. Agentforce replaced the Einstein branding as Salesforce’s AI layer. For sales teams, its two headline agents are an SDR agent that engages inbound leads, answers questions, and books meetings, and a Sales Coach agent that role-plays pitches and negotiations with reps (Salesforce). In April 2026 Salesforce launched Headless 360, making the platform’s capabilities available to agents through APIs, MCP tools, and CLI commands. Watch for: Agentforce acts on what is in Salesforce. Sparse activity history and missing contact roles limit what the SDR and coaching agents can do. 3. Microsoft Dynamics 365 Sales agents Best for: Dynamics 365 and Microsoft 365 shops. Microsoft made its Sales Qualification Agent generally available in October 2025, built on Copilot Studio to research leads, assess fit, and send personalized outreach (Microsoft). Sales Research and Sales Close agents extend the same approach to sales leaders and deal execution (Constellation Research). Watch for: Qualification quality depends on how complete lead and account records are inside Dynamics. 4. HubSpot Sales Hub with Breeze

10 Sales Optimization Strategies for the AI Era
Sales

10 Sales Optimization Strategies for the AI Era

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Sales Optimization Strategies for the AI Era: 10 Ways to Grow Revenue on Data You Can Trust Sales, Data 9 min October 23, 2026 Executive Summary Sales optimization used to mean better coaching, cleaner pipeline reviews, and smarter discounting. All of that still matters. What changed is who acts on your CRM data.  AI agents now score leads, prioritize pipeline, and draft outreach inside Salesforce, often with no human reviewing each decision. When a person read a bad report, they caught the error. When an agent acts on bad data, it makes the wrong call at machine speed.  The 10 strategies below are the ones revenue leaders are running in 2027, and every one depends on CRM data that reflects what actually happened with your buyers. Most sales teams already know what they should be doing. They should multi-thread, inspect pipeline weekly, coach on leading indicators, and spend selling time on the right accounts. The gap is rarely the playbook. It is the data the playbook runs on. Consider the baseline. In Validity’s State of CRM Data Management in 2025, 76% of respondents said less than half of their organization’s CRM data is accurate and complete, and 37% said poor data quality costs them revenue.  AI readiness is no better: a March 2026 Cloudera and HBR Analytic Services study found only 7% of enterprises say their data is completely ready for AI. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. That gap between AI ambition and data readiness is where most sales optimization efforts stall. Get our latest insights into your inbox What Sales Optimization Means in 2027 Sales optimization is the practice of making steady, measurable improvements to how your team finds, advances, and closes revenue. The goal is higher win rates, shorter cycles, and better margins from the same headcount. What has shifted is the operating model. In current times, optimization means giving AI agents better inputs. Sales teams named AI and agents their top growth tactic for 2026 in Salesforce’s State of Sales report.  In April, Salesforce launched Headless 360, which makes every platform capability available to agents through APIs, MCP tools, and CLI commands. Agents can now read and write CRM data without anyone opening a browser. They do not question that data. They act on it. That raises the stakes on a problem most teams have tolerated for years. Reps do not log every email, meeting, and contact. Activity gets tied to the wrong opportunity. Buying committee members never make it into Salesforce. A human manager compensates for these gaps with judgment. An agent cannot. So every strategy below starts from the same principle: optimize the data layer first, then the motion that runs on it. Why it Matters Now Pressure to show AI returns is rising. 61% of senior business leaders said they feel more pressure to prove AI ROI than a year earlier. In a Dataiku and Harris Poll survey of 600 CIOs, 98% reported increased board demands for measurable AI ROI since 2024. Salesforce’s own sales leadership names unified data as the key ingredient for sales agents. The fastest path to proof is making sure the AI you already bought works from a complete and correct picture of your customers. Download Check If Your Data is AI Ready With Our Most Downloaded Data Readiness Checklist 10 Sales Optimization Strategies for 2027 1. Take data entry off the rep’s plate entirely Every optimization effort assumes the CRM reflects reality. In most companies it does not, because it depends on reps logging emails, meetings, and contacts by hand. Training and mandates have been tried for two decades with the same result. Reps sell; they do not do data entry. The durable fix is automatic capture: every email, calendar event, and call written into Salesforce without anyone opening a form. When capture requires zero rep behavior change, coverage is complete from a new hire’s first day. Chainguard used this approach while scaling its sales team from 50 to 430 reps in under six months, with full activity visibility and no manual logging. 2. Map the full buying group B2B purchases are decided by committees, and those committees often disagree. A May 2025 Gartner survey of 632 B2B buyers found 74% of buying teams show unhealthy conflict during the decision, while groups that reach consensus are 2.5 times more likely to report a high-quality deal. Sellers cannot build consensus among people they never see. Yet Opportunity Contact Roles in most Salesforce orgs are sparse, because reps add the people they remember and skip the rest. Start by asking a simple question: what percentage of your open deals have the economic buyer, champion, and technical evaluator all engaged? If you cannot answer from your CRM, your forecast is missing the most predictive signal it has. Automatically discovering contacts from email and calendar, and associating them to the right opportunity, turns Download The Buying Group Playbook Turn buying group visibility into more closed deals with a practical playbook for identifying and engaging every decision-maker. 3. Manage on leading indicators Pipeline amount and close rate tell you what already happened. Leading indicators tell you what is about to happen: how many first meetings each rep held this week, whether deals are advancing from discovery to technical evaluation, and whether senior buyers are in the room. Chainguard built a Monday morning ritual around exactly this. Leadership reviewed one dashboard showing meeting volume, meeting type, persona coverage, and product-line engagement for every rep, all built on automatically captured data. Managers could spot underperforming reps within weeks instead of waiting a quarter for pipeline results. 4. Inspect pipeline with engagement evidence Weekly pipeline reviews are still essential. The problem is that most reviews rely on what the rep says about the deal. A deal can sit in Stage 3 for 90 days, look healthy in the forecast, and have had no buyer meeting in six weeks. Add engagement evidence to every review: last

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Buyer's Guide

Build vs Buy: How GTM Teams Decide What to Build

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Build vs Buy: What 67 GTM Teams Told Us About Building It Themselves Buyer’s Guide 9 min October 19, 2026 Executive Summary The build vs buy question has changed for go-to-market teams. AI made building cheap, so almost every team can build its own CRM data tooling. We hear it on most of our calls. So we went back to them and reviewed 193 calls across 67 companies where building in-house came up. Most teams build something, and they should. The ones that got value from AI built the intelligence their business is judged on and bought the data layer underneath it. This post covers the five kinds of build we saw, where each one breaks, and five questions to answer before you decide. Why Build vs Buy is on Every GTM Team Agenda A tool that once took a quarter to build can now take an afternoon. Most revenue teams have at least one engineer or RevOps lead who has already proved it. So when a CRM data problem comes up, “We’ll build it” is a reasonable first answer. We noticed a consistent pattern. Teams that got value from AI drew the build or buy line in the same place. They built what makes their business different: ICP definitions, deal strategy, win-loss and churn models, agents, and the checks on AI output. They bought what has to be right every day: capturing every email and meeting, matching each one to the right contact, account and opportunity, and keeping the full history in their own CRM. Kyle Norton, former CRO at Owner.com, puts the same split bluntly: Kyle NortonFormer CRO, Owner.com Buy the systems where uptime, reliability, compliance, and scale matter more than uniqueness. Get our latest insights into your inbox The 5 Kinds of Build and Where Each One Breaks “We’ll build it” covers five different projects. Each starts for a different reason and fails in a different place, so it helps to know which one you are running before you weigh build or buy. Counts are companies in our sample, and some ran more than one. 1. The Stitched Stack (16 companies) Every team buys its own tool, and each tool writes to Salesforce. Sequencing, call recording and the CS platform each get an integration. Nobody approves a data pipeline, but three integrations signed months apart add up to one. It breaks because each tool writes its own version of the same event. One logs a meeting as a task, another logs it as an event, and the records never reconcile. Much of the history also sits in each vendor’s own objects, so it leaves when the contract does. As Abhijeet Vijayvergiya, Nektar’s co-founder and CEO, puts it:  Abhijeet VijayvergiyaCo-founder and CEO, Nektar.ai It’s on rent, it’s not permanent. 2. The Warehouse Cleanup (21 companies) This was the largest group. The thinking is simple: “We have a warehouse and data engineers. We’ll fix it downstream.” These teams usually do have the skills. It breaks because you cannot clean data that was never captured, or match a meeting to a deal after the context is gone. Teams end up parsing, deduping and pushing records back into the CRM. Some of the most expensive people in the company spend their time on cleanup. 3. The In-house AI Team (8 companies) The company has a real AI team, an MCP stack and a warehouse, so buying anything that touches AI looks redundant. It breaks because the AI team is rarely pointed at go-to-market. A GTM leader at a fintech infrastructure company told us their central AI team is “mostly there to make sure [we have] access to the technology and it’s governed.” GTM requests wait in line behind everyone else’s. 4. The “use what we already pay for” Build (10 companies) An executive who already pays for an LLM, a call recorder and a CRM asks why the company should pay again. The champion has no number ready. It breaks because the team spends a few cycles assembling the answer from existing tools. Win-loss analysis is the usual example: transcripts, CRM exports and raw email dropped into an LLM by hand, with a token bill nobody tracks. The gaps show up a quarter later. 5. The build on top (11 companies) Same engineers, same ambition, different line. These teams build the agents, models and dashboards, and buy the capture and matching underneath. Many teams from the first four groups end up here after a quarter or two. What they built on top was specific to each business. A fintech company built a signal that flags when nobody has spoken to a customer’s CFO, its earliest churn warning. A cybersecurity company built a white-space agent that maps which product to sell into which account. A contract software company built a model that tests whether sales engineer involvement in a trial predicts a close. None of these are products anyone sells. Each runs on matched activity data the company owns. Why An Internal Build Looks Free An internal build is paid for by people already on payroll. On a budget sheet it costs nothing, so anything you buy shows up as new spend against a zero. A GTM operations leader at a compliance software company described this exactly. His team had spent several quarters building its own activity reporting “in sweat equity,” with “no dollars tied to it outside of the timing and effort.” His biggest obstacle to buying was explaining new spend against a build that appeared to cost nothing. The build also got harder to leave every month. More reports, dashboards and habits depended on it, so switching meant restating history and re-enabling the field. Meanwhile the build had stopped where the work got hard. Partner activity, close to a third of that company’s revenue, had never been tracked. The Cost Nobody Adds Up In a build vs buy decision, the model is the inexpensive part. The expense is in making customer data trustworthy enough for a model to act

Build vs Buy resource
RevOps

Build vs Buy Guide

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Guide What 67 Teams Told Us About Building It Themselves A build vs buy guide for revenue, RevOps and AI transformation leaders deciding what to build in-house and what to buy. AI made building inexpensive. Most GTM teams now have someone who can stand up a CRM data tool in an afternoon, so “we’ll build it” is a reasonable first answer.We went back through 193 calls where building came up, across 67 companies, to see what happened next. Most teams build something, and they should. The ones that got value from AI drew the line in the same place. They built the intelligence their business is judged on and bought the data layer underneath it. This guide shows where that line sits, where in-house builds break, and what they cost once they ship.What’s inside: The five kinds of build we saw, and where each one breaks Why an internal build looks free on a budget sheet, and what it really costs to run A fill-in worksheet to add up your build before the conversation reaches finance How Mimecast built first, then bought, and what it built on top How AI transformation leaders at QuickBase, Asana and Mimecast decide what to build A one-page checklist of five questions to answer before you build Who it’s for: CROs, RevOps and GTM operations leaders, and AI transformation teams weighing build or buy for their CRM data. Download the Guide From The Revenue Lounge Podcast​ Other Downloadable Resources Ready to Turn your Data into Revenue Outcomes? Book a Demo

gong vs nektar
Buyer's Guide, Product

Gong vs. Nektar: How They Handle Salesforce Data

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Gong vs. Nektar: How They Handle Salesforce Data Buyer’s Guide, Comparison 18 min September 18, 2026 Sales teams today generate enormous amounts of customer activity across calls, meetings, email, and calendars. The challenge is no longer simply capturing that activity. It is making sure the resulting data is complete, structured, correctly associated, and available in Salesforce for the teams and systems that depend on it. This is where Gong and Nektar approach the problem differently. Gong is primarily a conversation intelligence platform. Its core capabilities center on recording and analyzing calls and meetings, generating summaries and insights, coaching reps, and, with Gong Engage, supporting outbound workflows. Nektar is focused on revenue activity capture and CRM data completeness. It captures customer interactions across email, meetings, and calls and structures that activity in Salesforce as native CRM records. That distinction is important because the two platforms do not necessarily need to compete for the same job. In fact, they can work together. The key question is not simply whether Gong or Nektar can “log activity.” It is which system should own the activity data in Salesforce, and what level of structure and context does the revenue organization need from that data? Gong and Nektar Solve Different Problems The simplest way to understand the difference is to look at where each platform starts. Gong starts with the conversation. It records customer conversations, analyzes them, generates summaries and insights, and connects that information to CRM records. For teams using Gong Engage, it can also support sales engagement and outbound sequencing. Nektar starts with the customer relationship. Its role is to capture customer activity across the team’s inboxes and calendars, structure that information, and make it available as Salesforce-native data. That means Nektar can capture: Emails Meetings Calls Meeting participants Customer contacts Opportunity relationships Opportunity Contact Roles Activity metadata The objective is to create a more complete representation of the customer relationship inside Salesforce without requiring reps to manually log every interaction. A useful way to think about the distinction is: Gong helps revenue teams understand what happened in conversations. Nektar helps ensure the activity surrounding those conversations becomes complete, structured CRM data. That makes Nektar less of a replacement for Gong and more of a revenue telemetry layer underneath the revenue stack. Get our latest insights into your inbox What Happens to Gong Data in Salesforce? Gong’s Salesforce integration connects Salesforce records with Gong’s conversation intelligence platform. Salesforce Accounts, Contacts, Leads, and Opportunities can provide context for Gong conversations, while Gong can send conversation-related activity and information back into Salesforce. Depending on the Gong products and configuration being used, this can include activities such as calls, meetings, and emails. For example, Gong can help a sales organization understand: What was discussed on a customer call What objections were raised What commitments were made What topics appeared repeatedly across conversations How a deal is progressing Which conversations may require management attention This is valuable conversation intelligence. But there is an important distinction between conversation intelligence and CRM data infrastructure. A conversation can contain useful information without that information automatically becoming a governed Salesforce field, Contact, Opportunity Contact Role, or other structured CRM record. That difference becomes increasingly important as enterprise sales organizations become more complex. The Customer Relationship is Bigger Than the Recorded Conversation Consider a typical enterprise opportunity. A prospect may interact with a sales organization through discovery calls, executive meetings, email threads, calendar invitations, product demonstrations, technical evaluations, security reviews, procurement conversations, follow-up emails or internal and external meetings. There may be 8, 10, or 20 people involved across those interactions. Not all of them will necessarily appear in a recorded Gong conversation. Some may communicate exclusively through email. Others may join a calendar invitation but never speak during the meeting. Some may not exist as Salesforce Contacts yet. And an account may have multiple active opportunities at the same time. This creates a larger CRM question: Who is involved in the customer relationship, what activity has taken place, and how should that activity be represented in Salesforce? Conversation intelligence is one part of the answer. Activity capture and relationship intelligence are another. Gong & Nektar Salesforce Data: 6 Core Differences 1. Conversation Intelligence vs. Revenue Activity Capture Gong’s primary job is conversation intelligence. Its activity data in Salesforce is largely connected to those conversation intelligence and engagement workflows. Nektar’s primary job is broader activity capture. It automatically captures customer interactions across email, meetings, and calls across connected inboxes and calendars, regardless of whether a rep manually logs the activity. It then structures that information and writes it into Salesforce as native CRM records, including tasks, events, contacts and opportunity contact roles (OCR). This distinction matters because Salesforce can be used as more than a place to store a record saying that a meeting happened. It can become the underlying data layer for forecasting, pipeline inspection, account planning, buying-group analysis, reporting or AI applications.  For those use cases, activity needs to be not only captured but structured and associated with the right CRM records. 2. Contact Creation: Capturing a Person vs. Creating a CRM Record One of the biggest differences between conversation intelligence and a CRM data layer appears when a new stakeholder enters the buying process through email. Consider a simple example. A prospect who isn’t yet in Salesforce starts emailing an account executive. That person may eventually become an important member of the buying group. But if the CRM doesn’t know the person exists, subsequent activity can remain disconnected from the opportunity. This is where Gong’s Salesforce email export has an important limitation. According to Gong’s documentation, “Gong exports emails associated with Salesforce leads or contacts from the date you enable export.” Gong also states that it does not export emails retroactively. That creates two important constraints.  The person needs to already exist in Salesforce Gong’s email export is based on emails associated with existing Salesforce Leads or Contacts. In other words, an email conversation with someone who isn’t already

Gong salesforce integration
Buyer's Guide, Salesforce

A Guide to Gong Salesforce Integration

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A Guide to Gong Salesforce Integration Buyer’s Guide, Salesforce 7 min September 17, 2026 Gong’s Salesforce integration connects conversation intelligence with CRM data. The model is straightforward: Salesforce provides Gong with CRM context, Gong analyzes conversations and other captured interactions, and selected Gong data can be exported back into Salesforce. For revenue teams evaluating Gong, however, there is an important distinction between conversation intelligence and CRM data capture. Gong is built to understand conversations. Salesforce remains the system where customer and opportunity records live. The question is therefore not whether Gong captures valuable conversation data. It does. The question is: how much of that context actually becomes Salesforce data? Get our latest insights into your inbox What does Gong capture? Gong captures and analyzes customer conversations across supported channels, including calls and meetings, and can use Salesforce data to provide additional context around those conversations. Once Salesforce is connected, teams can search and analyze conversations based on Salesforce fields such as opportunity stage or amount. Gong can also export conversation data back into Salesforce.  The Salesforce integration therefore works in both directions conceptually: Salesforce → Gong Salesforce provides CRM context that Gong can use for analysis. Gong → Salesforce Selected conversation information can be exported back into Salesforce. That second part is where the data model becomes important. What does Gong write to Salesforce? Gong can export captured conversation information to Salesforce. Their FAQ says “No, Gong doesn’t create contacts in Salesforce, but Gong does import the email of new contacts and log it in Gong.” Teams can also control which Gong calls are exported using filters. So the Salesforce record can include information derived from Gong conversations.  But there is a critical limitation in Gong’s own documentation: Gong doesn’t create contacts in Salesforce. Gong explicitly says that when a new contact is identified, it does not automatically create that contact in Salesforce. Gong can import the new contact’s email into Gong and log it there, but the Salesforce contact does not get created automatically.  That creates a particularly important boundary for teams thinking about CRM completeness. Where Gong Stops The contact limitation matters because the Salesforce activity record and the Salesforce person record are connected. If a person does not already exist in Salesforce, Gong says it will not create that Contact. Its documentation also states that Gong exports emails associated with Salesforce leads or contacts from the date you enable export. A meeting is exported only when at least one external participant matches a Salesforce contact or lead. If no contact, lead, or account is found, the task/event isn’t created. That means the two limitations compound: No Salesforce Contact → no corresponding Salesforce email activity. The conversation can exist in Gong. The person can be identified by Gong. But that does not necessarily mean the person becomes a Salesforce Contact or that the associated activity becomes part of the Salesforce record. This is not a criticism of Gong’s conversation intelligence. It is simply the boundary between Gong’s conversation system and Salesforce’s CRM data model. No Contact means no Opportunity Contact Role In Salesforce, an Opportunity Contact Role connects a person to a specific opportunity and identifies their role in the deal. That relationship is important for reporting on the people involved in an opportunity. If Gong identifies a new person in a conversation but does not create a Salesforce Contact for that person, there is no Salesforce Contact record to associate with the opportunity. As a result, that person cannot have an Opportunity Contact Role created through that Gong-to-Salesforce workflow. The chain looks like this: Person participates in Gong conversation → Gong identifies the person → No Salesforce Contact → No Opportunity Contact Role → Person is missing from opportunity-level buying-group reporting. The conversation can exist in Gong. The person can be known to Gong. But without the corresponding Salesforce Contact and opportunity relationship, that stakeholder does not become part of the structured buying-group data in Salesforce. This is particularly important in complex B2B sales cycles, where the people participating in conversations are not always the same people who were originally entered into Salesforce. Why Opportunity Contact Roles matter A Salesforce Contact by itself tells you that a person exists in the CRM. An Opportunity Contact Role tells you that the person is connected to a specific deal. That distinction matters for buying-group visibility. A CRM may contain several Contacts associated with an Account while still failing to show which of those people are actually involved in a particular opportunity. When a stakeholder participates in a Gong-recorded conversation but does not already exist as a Salesforce Contact, the Gong integration cannot establish that Salesforce relationship through Contact creation. That creates a gap between conversation intelligence and structured opportunity intelligence. What does this mean for Salesforce reporting? Salesforce reporting depends on what exists in Salesforce. If a buyer participates in a Gong-recorded conversation but doesn’t have a Salesforce Contact, that person may not become part of the CRM’s contact-level reporting through Gong. That matters particularly in complex B2B sales cycles where one opportunity can involve many stakeholders. A CRM report may show the opportunity, existing contacts and Gong activities associated with known CRM records. But it can still lack people who participated in conversations but weren’t already represented as Salesforce Contacts. For teams measuring buying-group coverage, that creates a data question: Are we measuring the actual buying group, or only the portion of the buying group that already exists in Salesforce? What does this mean for AI in Salesforce? The same distinction becomes even more important when AI is added. AI systems can only reason over the context available to them. Gong can have rich conversational context about a buyer while Salesforce may not contain a corresponding Contact record for that person. That can create a split between conversational intelligence and CRM intelligence. The conversation exists. The person may be known. But the CRM record may not. For AI operating on top of Salesforce, that can mean an

salesloft salesforce integration
Buyer's Guide, Salesforce

A Guide to Salesloft Salesforce Integration

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A Guide to Salesloft Salesforce Integration Buyer’s Guide, Salesforce 10 min September 17, 2026 Salesloft and Salesforce are designed to work together. Salesloft’s CRM Sync connects sales engagement activity with Salesforce, allowing teams to synchronize records, log activities, map fields, and automate data movement between the two systems. But there is an important distinction between integrating a sales engagement platform with Salesforce and building a complete record of customer activity and relationships inside Salesforce. Salesloft’s documentation focuses primarily on what the integration can synchronize. For revenue teams evaluating Salesforce data quality, the more useful question is what happens at the edges of that integration. What gets captured? What gets written to Salesforce? What object does it become? And what information can still remain outside the CRM? Get our latest insights into your inbox What Does Salesloft Capture? Salesloft captures engagement activity generated through its sales engagement platform, including emails, calls, cadence activity and other seller interactions. Its Salesforce integration is designed to synchronize both CRM records and activity data. Salesloft says its CRM Sync can automatically record calls and emails as activities in Salesforce, while pushing more than 30 activity properties, including call duration, call disposition, sentiment, email activity counts and cadence identifiers. The integration also works with Salesforce Leads, Contacts, Accounts and Opportunities. Salesloft describes the connection as bidirectional, allowing records and field updates to move between Salesforce and Salesloft. Administrators can configure field mappings, determine which system has priority for particular fields, and define synchronization rules. Salesloft also provides a sync log showing transferred records, fields, timing, direction and outcomes, allowing administrators to troubleshoot failed syncs and mapping errors. That makes Salesloft more than a simple activity logger. It is designed to keep the sales engagement system and Salesforce synchronized. But synchronization is not the same thing as comprehensive relationship intelligence. What Does Salesloft Write to Salesforce? Salesloft primarily writes two categories of information back into Salesforce. 1. CRM records and field updates Salesloft can synchronize Salesforce Leads, Contacts, Accounts and Opportunities. Administrators can configure field mappings and determine the direction and priority of individual fields. Salesloft’s CRM Sync also supports automation rules that can execute actions based on defined criteria. This means Salesforce can remain the source of important CRM information while Salesloft uses that information to drive sales engagement workflows. 2. Sales activities Salesloft can write calls and emails into Salesforce as activities. Those activities can carry additional information such as: Call duration Call disposition Sentiment Email activity counts Cadence identifiers Other activity properties Salesloft has also expanded its activity logging to account for multiple people involved in an email or meeting. Shared Activity Syncing ensures that emails and meetings involving multiple People are accurately recorded in Salesloft and Salesforce, providing a complete view of interactions across your team and contacts. This feature is crucial for maintaining data integrity and gaining accurate insights. That is significant for organizations trying to capture more than one stakeholder in a deal. What about Clari Capture and Salesforce? The Salesforce integration story becomes more interesting following the Clari-Salesloft merger. Clari and Salesloft completed their merger on December 3, 2025. The combined company now operates as Salesloft, with Clari Forecast retaining the Clari name while Clari’s broader revenue intelligence, deal management and conversation intelligence capabilities continue under the Salesloft brand. For existing Clari customers, that creates a practical Salesforce question: What happens to Clari Capture, and how does it fit into the Salesforce activity-capture architecture going forward? The distinction matters because Clari and Salesloft have historically addressed different parts of the revenue workflow. Salesloft’s CRM Sync is designed to move sales engagement activity between Salesloft and Salesforce. Clari’s platform has historically used activity and revenue signals to support forecasting, deal inspection and revenue intelligence. Salesloft’s current platform brings these capabilities together under one company and one broader Predictive Revenue System. For an existing Clari customer, however, the immediate concern is not the corporate structure. It is the data flow into Salesforce. What happens to Clari Capture? The Clari-Salesloft merger raises a very specific Salesforce question for existing Clari customers: What happens to Clari Capture? Clari Capture was Clari’s activity-capture product. It automatically captured customer interactions, synced activity to the CRM, and could create and enrich contacts based on email and calendar activity. Clari’s own documentation describes Capture as a system that captures contacts and activities, enriches them, and synchronizes them back to the CRM. That matters because Capture was not simply a reporting feature. It sat in the data path between customer activity and Salesforce. There is no clear public replacement path yet.Clari and Salesloft now operate as one company under the Salesloft brand. Salesloft says that “the capabilities Clari users rely on today will continue under the Salesloft name” and that Clari Forecast retains the Clari name. It also says that product integration is ongoing and that customers should expect capabilities to deepen over time. The current Salesloft platform and product pages describe the combined Predictive Revenue System, Salesloft CRM Sync, Clari Forecast and other capabilities. Salesloft has also announced that Clari forecasting, deal intelligence, conversation intelligence and Salesloft engagement data are being brought together in the combined platform. There is currently no publicly documented migration path that tells an existing Clari Capture customer to move to a specific replacement product, what happens to their existing Capture configuration, or how historical Capture data and Salesforce workflows should be handled. What Clari Capture used to do matters Clari’s documentation describes Capture as automatically capturing contacts from calendar invites and email threads, enriching those contacts, creating new contacts in the CRM, and attaching newly identified members of the buying team to the appropriate opportunity. Clari’s community documentation also shows that Capture had its own logic for deciding when and how activity was written back to Salesforce. For example, Clari documented that Capture could skip writing an activity when it could not determine an exact account match. So replacing Capture is not simply a matter of changing the name of a product.

A Guide to Groove Salesforce Integration
Buyer's Guide, Salesforce

A Guide to Groove Salesforce Integration

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A Guide to Groove Salesforce Integration Buyer’s Guide, Salesforce 6 min September 17, 2026 Groove is different from many sales engagement platforms in this comparison because its architecture is deeply tied to Salesforce. Groove describes itself as a Salesforce-native sales engagement platform, and its AppExchange listing highlights real-time email and calendar synchronization, automatic activity logging, contact capture and the ability to create Salesforce Leads, Contacts and Accounts.  That makes Groove’s Salesforce integration fundamentally different from a platform that primarily maintains its own database and periodically syncs information into Salesforce. The more useful question is therefore not whether Groove can write data into Salesforce. It can. The question is what that data looks like at scale and where the integration still has boundaries. Get our latest insights into your inbox What does Groove capture? Groove captures sales engagement activity across email, calendar and other engagement workflows. Its Salesforce AppExchange listing describes capabilities including: Email synchronization Calendar synchronization Activity logging Contact capture Call logging Opportunity management Multi-channel campaign automation Groove can also create Salesforce Leads, Contacts and Accounts from the engagement workflow. That last capability is important. Unlike Gong, Groove can create Salesforce Contacts. So contact creation is not the gap to focus on when evaluating Groove. The more relevant questions are around the operational model, support and the depth of information written into Salesforce. What does Groove write to Salesforce? Groove writes engagement data into Salesforce’s native CRM objects. Groove writes key engagement data to contact, lead and task objects.  Groove also supports additional fields that can be surfaced in Salesforce for reporting. These can include information associated with: Flows Templates Step type Step number Response time Email send time Flow completion Flow removal Next-step dates Active Flow counts Groove’s documentation explains that some of these fields need to be created as custom fields in Salesforce before they can be populated. That creates a fairly detailed activity dataset inside Salesforce. Groove can create contacts Groove’s AppExchange listing explicitly says users can create new Leads, Contacts and Accounts from the Groove workflow. Groove can also automatically import new Salesforce Leads and Contacts into Flows. Its current documentation says Groove scans Salesforce for new Leads or Contacts every three hours by default, while Salesforce Flow can be configured for real-time imports when the required setup is in place. So contact creation and synchronization are core parts of the Groove architecture. Where Groove stops The limitation to examine with Groove is therefore not contact creation. It is what happens when an organization moves from capturing activity to managing customer context at enterprise scale. Groove’s data model is strongly optimized around Salesforce records and engagement activities. That makes it useful for organizations that want sales engagement to operate close to the CRM. But the more complex the revenue organization becomes, the more the questions move beyond activity capture: Who are all the people involved in an account? Which relationships exist between those people? Which stakeholders are active across different teams? What happened outside the engagement workflow? How does activity from Sales, CS, Marketing and executives connect? Can that context be used consistently across the opportunity? Those questions require a broader customer-context layer than activity logging alone. Support and Scale are Part of the Evaluation Groove’s own customer reviews provide another dimension that buyers should consider: support. One AppExchange reviewer wrote: “We don’t have a headcount large enough to warrant them giving us support.” That is a customer-reported experience, not a general statement about Groove’s support model, but it is relevant to buyers evaluating how an implementation will be supported as usage expands. At the same time, other Groove customer stories highlight the platform’s Salesforce-native architecture and support for complex Salesforce workflows. So the support question is best treated as a due-diligence issue, not as a blanket product criticism. For enterprise buyers, the relevant questions are: What support tier is included? Who owns Salesforce integration issues? What happens when custom Salesforce configurations change? How are sync failures handled? How quickly are managed-package changes released? What support is available during implementation and migration? What does this mean for Salesforce reporting? Groove can put a substantial amount of engagement information into Salesforce. Salesloft recommends the Groove Insights package for more granular reporting and provides prebuilt dashboards covering flows, calls, meetings booked, opportunities created and bounce codes.  That can make Salesforce a useful reporting environment for Groove-generated engagement. But there is an important technical distinction in Groove’s documentation: Lead and Contact data can be backfilled. Task data cannot. Groove says Lead/Contact fields are updated hourly and can be backfilled, while Task data is populated after the activity takes place and cannot be backfilled. For organizations doing historical CRM cleanup or migration, this difference matters. What does this mean for AI? Groove’s Salesforce-native architecture can put more engagement data directly into the CRM. But AI needs more than engagement volume. It needs relationships and context. A Salesforce record containing: Email → Task → Contact → Account is useful. A connected model containing: Person → Role → Relationship → Buying Group → Activity → Opportunity → Account → Outcome is a different level of context. Groove’s Salesforce integration addresses a significant part of the first problem. The second requires additional data modeling and enrichment beyond activity capture. Groove + Salesforce: The Practical Takeaway Groove has a particularly deep Salesforce integration. It can capture email and calendar activity, log activities into Salesforce, create Leads, Contacts and Accounts, write engagement fields into Salesforce, support custom objects and Salesforce workflows, and provide reporting through Salesforce and Groove Insights Its current documentation and AppExchange listing make that architecture clear.  For buyers, the remaining questions are therefore less about basic CRM synchronization and more about scale, support, historical data, and whether activity capture is enough to provide the customer context required by modern RevOps and AI workflows. Read our other Integration Guides Outreach Salesforce Integration Gong Salesforce Integration Salesloft Salesforce Integration About Nektar Nektar helps revenue teams turn Salesforce into a more complete source of

Salesforce dreamforce
Salesforce

Salesforce Dreamforce ’26 Keynote

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Salesforce Dreamforce ’26 Keynote: 7 Big Ideas Shaping the Next Era of Enterprise AI Event, Salesforce 10 min September 16, 2026 From Data 360 and headless applications to Agent Fabric and Salesforce Guardian, Salesforce used its Dreamforce ’26 keynote to lay out a vision for an enterprise where AI is deeply embedded in the systems, data and processes that run the business. Salesforce’s Dreamforce ’26 keynote was less about unveiling a single headline product and more about presenting a picture of how the company believes enterprise technology is changing. The central idea was clear: AI is moving from an application feature to an enterprise operating layer. That means connecting AI models to the data businesses already rely on, embedding business logic and semantics into those systems, deploying agents to perform work, and creating new interfaces through which people and AI can interact with the enterprise. Get our latest insights into your inbox The keynote brought together announcements across Salesforce’s data, application, AI, security and integration portfolios. It also repeatedly returned to a fundamental challenge facing enterprise AI: powerful models may be probabilistic, but businesses still operate on systems, rules, permissions and data that need to be deterministic. Salesforce’s answer is an architecture designed to bring those worlds together. Here are seven of the biggest ideas from the keynote. 1. Salesforce wants enterprise AI to operate beyond the traditional CRM interface One of the keynote’s most significant themes was that the Salesforce application itself is no longer necessarily the destination for work. Salesforce described a new architecture built around four layers: Data → Apps & Semantics → Agents → Interface The interface is the layer users ultimately interact with, but Salesforce’s vision is that the underlying intelligence can increasingly be delivered through different interfaces and environments. That includes Salesforce’s own products as well as experiences such as Slack and other external interfaces. The company tied this shift to its metadata architecture and its move toward headless applications. Instead of applications being consumed only through their traditional user interface, their underlying capabilities and business intelligence can be exposed and recomposed elsewhere. The implication is significant. Enterprise software has traditionally been organized around applications: employees open Salesforce, Workday, ServiceNow or another system and perform their work there. In an agentic enterprise, that model starts to change. Agents may access the capabilities of those systems directly. Employees may interact with enterprise intelligence through conversational interfaces. And applications increasingly become the infrastructure underneath those experiences. Salesforce called this a transformation of the interface layer. One that could fundamentally change how people interact with enterprise software. 2. The AI model is only one part of the equation The keynote repeatedly moved the conversation away from AI models themselves and toward what surrounds them. Salesforce described AI as operating in a probabilistic world, while enterprise systems operate in a deterministic world. An AI model can reason and generate answers, but it doesn’t inherently know the specific data, rules, processes and business logic of an organization. That’s where enterprise systems come in. Salesforce’s architecture is designed to ground AI in the company’s underlying source of truth: its data, applications, business semantics, workflows, permissions and rules. The keynote’s argument was essentially that the two worlds need to work together. AI can remain probabilistic. But the environment in which it operates needs reliable foundations. That distinction becomes particularly important when AI moves from generating information to making decisions or taking action. An incorrect answer from a chatbot is one problem. An autonomous agent acting on an incorrect piece of enterprise information is another. 3. Data 360 is positioned as the foundation for AI If there was one prerequisite Salesforce returned to repeatedly, it was data. The keynote positioned Data 360 as the foundation layer for bringing enterprise information together. Salesforce described it as a way to integrate, federate and harmonize data across the enterprise and make that information available to AI. The goal isn’t simply to put more information in one database. It is to allow AI to work across information that may otherwise remain distributed across different systems and data environments. The keynote also emphasized that getting data ready for AI involves more than simply connecting sources. Data needs to be discovered, cleaned and curated. And once it is available, it needs to be understood in the context of the business. This is why Salesforce placed Data 360 underneath the applications, agents and interface layers in its architecture. The message was straightforward: The quality of enterprise AI ultimately depends on the quality and accessibility of the enterprise data underneath it. 4. Business semantics are becoming as important as the data itself One of the more interesting parts of the keynote was the emphasis on semantics. Enterprise data isn’t meaningful in isolation. Different companies have different definitions for concepts such as revenue, ARR, churn, customer health and other business metrics. Those definitions, relationships and rules represent the way a company actually understands its business. Salesforce argued that this business intelligence is embedded in applications and their metadata and semantic layers, and that AI needs access to it. This is an important distinction. Consider a simple question: “How much revenue did this customer generate?” The underlying number might be easy to retrieve. But determining which revenue definition applies, which period matters, which products are included and how the company defines the metric is a business-semantic question. For an AI system to operate effectively inside an enterprise, it needs more than raw information. It needs to understand what that information means in the context of that business. That is why Salesforce’s architecture puts apps and semantics between the data layer and the agent layer. 5. Salesforce is turning agents into a digital workforce The keynote showcased agents across a wide range of business functions. Sales agents can work on pipeline. Service agents can resolve customer issues. Recruiting agents can engage candidates. IT agents can support employees. Other examples extended into areas such as supply chain operations. Salesforce’s broader framing was that

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

salesforce email integration
Buyer's Guide, CRM

Salesforce Email Integration & Email Sync: Complete 2026 Guide

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Salesforce Email Integration: Every Way to Sync Email to Salesforce Buyer’s Guide 29 min September 8, 2026 If your sales team lives in Gmail or Outlook while your customer data lives in Salesforce, there is a basic problem: Your customer conversations are happening somewhere your CRM cannot automatically see them. Emails are sent. Replies come in. New stakeholders get added to threads. Opportunities move forward through conversations that may never make it into Salesforce. And when those interactions aren’t captured, Salesforce doesn’t just have less activity data.  This displays an incomplete picture of the customer. That affects sales managers trying to inspect deals, RevOps teams trying to measure engagement, marketers trying to understand account activity, customer success teams trying to understand relationships, and increasingly, AI systems trying to reason over Salesforce data. The good news is that there are several ways to get email into Salesforce in 2026. The bad news is that salesforce email integration can mean very different things depending on the approach you choose. You can: Log emails manually Use Salesforce’s Outlook or Gmail integration Use Einstein Activity Capture (EAC) Use a sales engagement platform Use a dedicated email/activity capture platform Each solves a different problem. Some simply help a rep log an email. Others automatically capture email activity. And the most sophisticated options go further, turning that activity into structured CRM data about contacts, opportunities, buying groups and relationships. This guide explains the differences. Get our latest insights into your inbox What is Salesforce Email Integration? A Salesforce email integration connects your email system (typically Microsoft Outlook or Google Gmail) with Salesforce so that email activity can be associated with CRM records. Depending on the solution, that can mean very different things. A basic integration might let a salesperson: View Salesforce records from Gmail or Outlook Create Salesforce records from their inbox Manually relate an email to an opportunity Log an email to a contact View Salesforce information while composing an email A more advanced email sync solution can automatically capture incoming and outgoing messages and associate them with Salesforce records without requiring the salesperson to manually log anything. Salesforce’s own Outlook and Gmail integrations provide the in-email experience. Einstein Activity Capture adds automatic activity capture and synchronization.  And dedicated activity-capture platforms can go further by turning email and meeting activity into structured revenue intelligence. So before choosing a tool, ask this: Do we want Salesforce to help reps log email, or do we want Salesforce to automatically know what is happening in customer relationships? Those are not the same requirement. 5 Ways to Get Email Into Salesforce Here’s the simplest way to understand the landscape: The right answer depends less on “Can this tool sync email?” and more on “What do we need Salesforce to know about our customer relationships?” Let’s look at each option. 1. Manual Email Logging in Salesforce The simplest Salesforce email integration is no integration at all. A salesperson sends an email from Gmail or Outlook and manually logs it to Salesforce. Salesforce supports manual email logging through its email tools, including the ability to relate emails to Salesforce records. Enhanced Email also allows emails to be stored as EmailMessage records rather than simply appearing as generic tasks.  How it works A typical workflow looks like this: Send email → choose Salesforce record → click Log Email → Salesforce records the activity The rep decides where the email belongs. That might be a contact, lead, account, opportunity, case or another Salesforce record that supports activities. What manual logging does well Manual logging has one major advantage: Control. The rep decides exactly what belongs in Salesforce. It can also be inexpensive because you’re using functionality already available within Salesforce rather than introducing another platform. Manual logging may be enough for organizations that have small sales teams, low email volume, simple sales processes, strong CRM discipline or limited need for historical activity. Where manual email logging breaks The problem is not whether Salesforce can log the email. The problem is whether people will consistently do it. Consider a salesperson handling 80 customer emails a day. If logging each message takes even a few seconds, the process becomes tedious. More importantly, it introduces human judgment into the CRM data: What did the rep remember to log? What did they forget? Which record did they choose? Did they log the reply? Did they add the new stakeholder they were emailing? There are also hard limits to some manual methods. For example, Salesforce’s Email to Salesforce functionality can match up to 50 email addresses and can create up to 50 email activities for a received email; email text and HTML are truncated at 131 KB.  Best use case: Small teams that need occasional email logging and don’t need automatic capture. Verdict: Inexpensive, simple and controllable, but fundamentally dependent on rep behavior. Explore Salesforce Inbox Looking for more than basic email sync? See how Salesforce Inbox brings email, calendar, and Salesforce workflows together for sales reps. 2. Salesforce Outlook and Gmail Integrations The next level is Salesforce’s native integrations with Microsoft Outlook and Gmail. This is where the phrase Salesforce email integration can become confusing. The Outlook/Gmail integration is primarily about bringing Salesforce into the rep’s inbox. Salesforce describes the integration as a way for reps to view, edit and create Salesforce records from their email environment and relate emails and events to Salesforce records.  The Outlook integration is available as a Salesforce add-in through Microsoft AppSource, while the Gmail integration is available through Salesforce’s Chrome-based experience.  What the Outlook/Gmail integration can do   Depending on configuration and licensing, reps can: See Salesforce records while working in email Search Salesforce records Relate emails to Salesforce records Log emails and events Create contacts and leads Create other Salesforce records View Salesforce information alongside emails Use email templates Access productivity functionality Salesforce’s current documentation also confirms that the integrations can log emails and events to records created from custom objects when those objects support activities.  And Salesforce continues

salesforce inbox: The Complete Guide to Features, Pricing, Setup & Limitations
Buyer's Guide

Salesforce Inbox: What It Is, Features, Pricing & Limitations (2026)

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Salesforce Inbox: The Complete Guide to Features, Pricing, Setup & Limitations Buyer’s Guide 27 min September 7, 2026 Sales reps read and reply to emails, schedule meetings, send follow-ups and exchange important deal information in their inbox. Salesforce Inbox is designed to bring Salesforce context and sales productivity tools into that workflow. But there is an important distinction that is often missed in articles about Salesforce Inbox: Salesforce Inbox is not the same thing as Einstein Activity Capture. Inbox is primarily the productivity layer that brings Salesforce records and tools into Outlook, Gmail and Salesforce’s email experience. Einstein Activity Capture (EAC), when enabled, provides the automated background capture of email and calendar activity. Salesforce also supports Inbox without EAC, in which case reps can manually log emails rather than having them automatically captured. That distinction matters because many teams evaluating Salesforce Inbox are actually trying to solve a different problem: How do we make sure every meaningful customer interaction becomes reliable, structured Salesforce data without asking reps to manually log everything? This guide explains what Salesforce Inbox is, what it does, how Salesforce Inbox for Outlook works, what it costs, what has changed through 2026, how it compares with Einstein Activity Capture, and where its limitations become important for growing revenue teams. Get our latest insights into your inbox Salesforce Inbox at a Glance Question Salesforce Inbox What is it? A Salesforce email productivity layer that works with Outlook, Gmail and Salesforce Primary purpose Give reps Salesforce context and productivity tools while they work in email Works with Outlook? Yes Works with Gmail? Yes Email tracking? Yes, with Inbox productivity features Meeting scheduling? Yes Send Later? Yes Automatic email capture? Not by Inbox alone; typically provided through Einstein Activity Capture Manual email logging? Yes Mobile Inbox app? No. Salesforce retired Inbox Mobile on February 1, 2024 Current licensing Included in some Salesforce editions/bundles; extra cost in others Current standalone list price? Salesforce’s current public documentation does not present one universal standalone Inbox price Best suited for Reps who want Salesforce context and email productivity tools inside Outlook/Gmail Biggest misconception Treating Salesforce Inbox and Einstein Activity Capture as the same product Salesforce currently states that Inbox is available in Lightning Experience and is included in Unlimited, Einstein 1 Sales and Agentforce 1 Sales editions, while Professional and Enterprise customers can purchase it at additional cost. An Inbox license unlocks Inbox features in the Outlook integration, Gmail integration and Lightning Experience. What is Salesforce Inbox? Salesforce Inbox is a set of email productivity features that brings Salesforce information and sales tools into the places where reps send and receive email. In practice, that means a salesperson can work in Outlook or Gmail and use a Salesforce side panel to see relevant Salesforce records, relate emails to Salesforce records, insert availability into emails, track email engagement, schedule emails for later and use productivity shortcuts. The product is therefore less about replacing your inbox and more about connecting the inbox to Salesforce. For example, a rep working on an opportunity could: Open an email from a prospect in Outlook. See the relevant Salesforce record in the side panel. Review Salesforce context without opening another browser tab. Insert available meeting times into the email. Track whether the email is opened or links are clicked. Schedule the email to be sent later. Manually relate or log the email to Salesforce. If Einstein Activity Capture is also configured, emails and calendar events can be automatically captured and associated with Salesforce records in the background. The simplest way to think about it is that Salesforce Inbox is productivity inside your inbox. Einstein Activity Capture is automated activity capture behind the scenes. They can work together, but they are not interchangeable. What does Salesforce Inbox do? Salesforce Inbox brings several Salesforce capabilities into Outlook and Gmail. The exact experience depends on your Salesforce edition, license and configuration, but the core Inbox productivity capabilities include: 1. Salesforce records inside Outlook and Gmail The Salesforce panel lets reps see relevant CRM information while working with email. Instead of switching repeatedly between Salesforce and Outlook or Gmail, reps can access Salesforce records from the email experience. Salesforce describes the experience as surfacing relevant Salesforce records alongside email and events and allowing users to relate email messages to Salesforce. This is particularly useful for sales reps who spend most of their day in email. 2. Email tracking Inbox can track email engagement, including when recipients open emails and click links. Salesforce’s documentation specifically notes that when Inbox productivity features are added to the Outlook or Gmail integration, reporting can include recipient opens and link clicks. This gives reps another signal about whether an email has been engaged with. It is important, however, not to confuse email engagement tracking with automatic CRM activity capture. They solve different problems. 3. Insert Availability Salesforce Inbox lets reps insert their availability directly into an email. Rather than exchanging multiple emails to find a suitable meeting time, the rep can send available slots or a scheduling link. Salesforce has continued investing in this experience. In the Winter ’26 release, Salesforce introduced a redesigned meeting-scheduling interface designed to make it simpler for prospects to select a meeting time. 4. Scheduling links Inbox can generate scheduling links that allow recipients to select an available time. This builds on Salesforce’s earlier scheduling capabilities, including the ability to share preferred meeting times and scheduling links. The important benefit is straightforward: The prospect chooses the time instead of starting another scheduling thread. 5. Send Later Salesforce Inbox allows reps to schedule emails for a later time. This can be useful when a rep wants to write an email immediately but send it at a more appropriate time. Salesforce has also expanded Send Later beyond new messages. Salesforce release notes document support for using Send Later when replying to threaded emails, rather than restricting scheduling to new, unthreaded emails. 6. Text shortcuts and email productivity Inbox includes productivity features such as text shortcuts.

salesforce gmail integration
Buyer's Guide, CRM

Salesforce Gmail Integration: The Complete Guide for 2026

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Salesforce Gmail Integration: The Complete Guide for 2026 Buyer’s Guide, CRM 20 min September 1, 2026 Salesforce and Gmail are two of the most important systems in a modern sales stack. But connecting them isn’t as straightforward as it sounds. Customer conversations happen in Gmail. Meetings happen in Google Calendar. New stakeholders enter email threads. Follow-ups get sent. Important buying signals appear in conversations. But your revenue team still needs that activity in Salesforce. That is what a Salesforce Gmail integration is designed to solve. In 2026, however, “Salesforce Gmail integration” can mean several different things. Salesforce itself offers Gmail integration, Einstein Activity Capture, Salesforce Inbox, and related synchronization capabilities. Third-party platforms can add another layer of activity capture, automation, matching, and intelligence. So the question is no longer simply: How do I connect Gmail to Salesforce? It’s: How much of the customer activity happening in Gmail do you need Salesforce to understand, and what do you need Salesforce to do with it? This guide explains what the Salesforce Gmail integration does today, how to set it up, where the native option works well, where teams can run into limitations at scale, and when a dedicated Salesforce activity-capture or revenue-data platform makes sense. Get our latest insights into your inbox What is Salesforce Gmail integration? A Salesforce Gmail integration, sometimes referred to as a Salesforce connector for Gmail, connects a user’s Gmail account with Salesforce so they can access Salesforce information from Gmail and associate email activity with Salesforce records. Salesforce’s native Gmail Integration allows users to see, create, and modify Salesforce records from Gmail. With Enhanced Email enabled, users can relate emails to Salesforce records while composing messages and associate emails with contacts, leads, accounts, opportunities, cases, and supported records.  The experience has also evolved. Salesforce made Gmail Integration available inside Sales Cloud Everywhere, bringing the Gmail functionality into its Chrome side panel alongside other Salesforce capabilities.  Separately, Einstein Activity Capture (EAC) can automatically capture email and calendar activity from connected Google accounts and associate that activity with Salesforce records. Since Summer ’25, Salesforce has introduced Sync Email as Salesforce Activity, which allows captured emails to be stored as Salesforce activity data, including Task and EmailMessage records. This makes the email data available to standard Salesforce reports, workflows, triggers, APIs, and other platform capabilities.  So there are really two related layers: Gmail integration: Work with Salesforce from inside Gmail. Activity capture: Automatically bring customer email and calendar activity into Salesforce. For many teams, you will want both. Salesforce Gmail Integration is also part of a broader ecosystem of Chrome extensions built to make Salesforce easier to use from the browser. If you’re evaluating those tools alongside Gmail integration, What can you do with Salesforce Gmail integration? The native integration covers the core workflows most sales teams expect. 1. View Salesforce records from Gmail Sales reps can open an email in Gmail and access relevant Salesforce information without constantly switching between applications. Depending on configuration, they can view and work with Salesforce records associated with the people they’re communicating with. This is particularly useful for basic CRM lookups: Is this person already a contact? Which account are they associated with? Is there an open opportunity? What activity has already been recorded? Do I need to create a Salesforce record? Salesforce describes the Gmail integration as a way for users to see, create, and modify Salesforce records directly from Gmail. 2. Log emails to Salesforce Users can associate emails with relevant Salesforce records from Gmail. Enhanced Email is important here. Salesforce requires Enhanced Email for relating Gmail emails to Salesforce and stores email messages as richer email records rather than treating everything as a simple task.  This gives sales teams a much better record of customer conversations. 3. Automatically capture emails Manual logging is not the only option. With Einstein Activity Capture and Sync Email as Salesforce Activity enabled, Salesforce can automatically sync emails sent and received through connected Google accounts into Salesforce.  That changes the workflow from: Rep sends email → remembers to log it to: Rep sends email → Salesforce captures the activity For organizations trying to improve CRM completeness, this is one of the biggest benefits of connecting Gmail and Salesforce. 4. Sync Google Calendar activity Salesforce can also capture and synchronize calendar activity through Einstein Activity Capture. That gives Salesforce visibility into meetings alongside email activity. Salesforce can match Google Calendar events with relevant Salesforce users, contacts, and leads. It can also sync attendee information, subject to Salesforce’s matching and attendee limits.  5. Create Salesforce records from Gmail The Gmail integration can also reduce the friction involved in creating CRM records. If someone new enters a sales conversation, the rep can create or update the relevant Salesforce record without leaving Gmail. This is important because the best CRM workflow is often the one that requires the fewest context switches. How to set up Salesforce Gmail integration The exact setup depends on whether you’re using the basic Gmail integration, Einstein Activity Capture, Salesforce Inbox, or a combination. For the native Gmail integration, Salesforce’s current setup process starts in Salesforce Setup. Step 1: Enable Enhanced Email Before enabling Gmail Integration, Salesforce recommends verifying that Enhanced Email is enabled. Enhanced Email is required to relate Gmail emails to Salesforce records.  Step 2: Enable Gmail Integration In Salesforce: Setup → Quick Find → Gmail Integration and Sync Enable the setting that allows users to access Salesforce records from Gmail. Salesforce’s current documentation specifically instructs administrators to enable Gmail Integration and Sync and then enable Enhanced Email with Gmail.  Step 3: Configure Einstein Activity Capture If your objective is automatic activity capture rather than simply accessing Salesforce from Gmail, configure Einstein Activity Capture. This is where administrators define how email, events, and other activity should be captured and synchronized. With the newer Sync Email as Salesforce Activity architecture, captured email can become standard Salesforce activity data.  Step 4: Configure users and permissions Users need the appropriate Salesforce permissions and must connect

Salesforce Outlook Plugin
Buyer's Guide, CRM

Salesforce Outlook Plugin: Setup, Features & Alternatives

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Salesforce Outlook Plugin: What It Is, How It Works, Setup, Limitations & Alternatives Buyer’s Guide, CRM 11 min September 1, 2026 If your sales team works in Microsoft Outlook while Salesforce is your CRM, a Salesforce Outlook plugin can bridge the gap between the two. But in 2026, “Salesforce Outlook plugin” can mean several different things. Salesforce has a native Outlook integration, Einstein Activity Capture, and Salesforce Inbox. There are also third-party activity capture and revenue intelligence platforms that extend what the native tools can do. The right choice depends on a simple question: Do you just need Salesforce inside Outlook, or do you need Outlook activity to become reliable, structured Salesforce data? This guide explains what Salesforce Outlook integration looks like today, what the native option does well, where it can become difficult at scale, and when it makes sense to consider alternatives. Get our latest insights into your inbox What is a Salesforce Outlook plugin? The Salesforce Outlook integration is a Microsoft Outlook add-in that brings Salesforce functionality into Outlook. Instead of switching between Outlook and Salesforce, users can work with Salesforce records while reading and sending email. Depending on configuration, users can: View Salesforce information from Outlook Match emails to Salesforce records Log emails and calendar events Create Salesforce records from Outlook Relate emails and events to accounts, contacts, leads, opportunities and other supported records For automatic activity capture, Salesforce also provides Einstein Activity Capture (EAC). EAC can automatically capture email and calendar activity from connected Microsoft accounts and associate it with Salesforce records. So there are really two parts to the modern Salesforce Outlook experience: Outlook Integration = Salesforce functionality inside Outlook Einstein Activity Capture = automatic email and calendar activity capture Salesforce Inbox can add further email productivity capabilities for organizations with the appropriate licensing. Salesforce for Outlook vs. Outlook Integration If you’re reading older articles about Salesforce Outlook integration, you’ll encounter Salesforce for Outlook. That is the legacy product. Salesforce for Outlook is no longer supported, although Salesforce says it will continue functioning until its planned retirement in December 2027. Salesforce recommends migrating to Outlook Integration and Einstein Activity Capture instead. So if you’re implementing Salesforce and Outlook today, don’t build a new deployment around Salesforce for Outlook. The current architecture is the Salesforce Outlook add-in plus the appropriate activity-capture capabilities. What does Salesforce Outlook integration do well? For straightforward Salesforce environments, the native integration is a strong starting point. Salesforce inside Outlook: Reps can access Salesforce records without constantly switching applications. Email and event logging: Users can relate emails and meetings to Salesforce records directly from Outlook. Record creation: A rep can create supported Salesforce records from an email rather than manually opening Salesforce. Automatic activity capture: Einstein Activity Capture can remove the need for reps to manually log every email and calendar event. Native Salesforce architecture: Because the solution comes from Salesforce, it works within the Salesforce ecosystem, permissions model and automation framework. For a team that primarily wants to simply put Salesforce in Outlook and capture their reps’ activity, the native solution may be all they need. The complexity starts when “capture activity” becomes “capture all of our revenue activity accurately.” Where Salesforce Outlook integration gets complicated at scale The native integration works well for simple use cases. Enterprise revenue organizations, however, often have requirements that are considerably more complex. 1. Shared mailboxes Sales teams often work from addresses such as: sales@ support@ partnerships@ renewals@ customersuccess@ These aren’t individual user accounts. Salesforce documents limitations around Outlook shared mailboxes: true Outlook shared mailboxes aren’t supported in Microsoft add-ins, while certain shared-folder configurations with delegated access can be supported. This matters because the email address, Salesforce user, account and ownership of the customer relationship may all be different things. If shared mailboxes are central to your workflow, test this before rolling out the integration. 2. Email aliases Large organizations may have people using multiple addresses: alex@company.com alex@brand.com alex@acquiredcompany.com The challenge isn’t sending the email. It’s recognizing that these addresses may belong to the same person. Alias-based matching can create problems with contact identification and calendar attendees, particularly in complex organizations with multiple domains, subsidiaries or acquisitions. If your business uses aliases heavily, validate how the integration handles them before deployment. 3. Custom Salesforce objects Salesforce’s Outlook Integration can allow users to relate activity to supported custom-object records. But there’s an important distinction between a rep can manually associate this email with a custom record, and a system that can automatically determine which custom record this activity belongs to. The latter is much harder. If your Salesforce architecture revolves around custom objects such as subscriptions, projects, implementations, engagements or partner relationships, you’ll want to understand exactly how automatic activity matching works. Salesforce provides configurable matching logic through its Activities: Match Email to Records Flow, but sophisticated organizations may need additional configuration to make their matching rules work reliably. 4. Multi-attendee meetings A meeting isn’t just a calendar event. It can tell you who is involved in a deal. Consider a meeting with: An economic buyer A champion A technical evaluator Procurement An executive sponsor Your AE and SE Capturing the meeting is useful. Knowing who attended and what role each person plays is considerably more valuable. Salesforce states that Einstein Activity Capture can sync combinations of Salesforce users, contacts and leads for up to 50 attendees per event. But calendar synchronization shouldn’t be confused with buying-group intelligence. Knowing that six people attended a meeting doesn’t automatically tell Salesforce that one is the champion and another is procurement. 5. Historical activity Connecting Outlook today doesn’t necessarily answer the question: “How do we get our historical customer interactions into Salesforce?” This matters for organizations with: Long sales cycles Years of customer relationships Existing open opportunities Acquired companies New CRM implementations Former employees Salesforce provides historical email migration and retention capabilities through its activity-capture architecture, but the available history depends on configuration and migration options. If historical activity matters, evaluate the actual lookback period, migration process

einstein activity capture thumbnail
Buyer's Guide

Einstein Activity Capture: What It Is, How It Works, Limitations & Alternatives

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Einstein Activity Capture: What It Is, How It Works, Limitations & Alternatives Buyer’s Guide 15 min August 27, 2026 If your sales team works in Gmail or Outlook while your revenue teams work in Salesforce, there is an obvious gap: customer activity happens outside the CRM. Emails are sent. Meetings happen. New stakeholders enter conversations. But unless that activity makes its way into Salesforce, your CRM can never provide a complete picture of the customer relationship. That’s the problem Einstein Activity Capture (EAC) is designed to solve. Einstein Activity Capture automatically captures email and calendar activity from connected Microsoft or Google accounts and associates it with relevant Salesforce records. It reduces manual data entry and gives sales teams greater visibility into customer interactions. (Salesforce) Before Summer ’25, captured EAC email data lived outside standard Salesforce records. It wasn’t available to many Salesforce Platform features, couldn’t be exported in the same way as standard records, and was subject to EAC-specific retention limitations. Starting in Summer ’25, Salesforce introduced Sync Email as Salesforce Activity. With it enabled, EAC stores captured email as standard Salesforce ‘Task’ and ‘EmailMessage’ records. The data can be used in standard reports, workflows, triggers, SOQL and APIs. (Salesforce) Salesforce is now migrating older EAC implementations to the new architecture as well, with automatic migration dates rolling out during 2026. (Salesforce). This guide looks at how Einstein Activity Capture works in 2026, what it does today, what it costs, where it stops, and what to do about it. Get our latest insights into your inbox What Is Einstein Activity Capture? Einstein Activity Capture is Salesforce’s activity-capture capability for automatically bringing email and calendar activity into Salesforce. It connects with Microsoft and Google accounts and can capture emails, calendar events, contact activity and meeting activity. Captured emails can now be stored as Salesforce EmailMessage and Task records and associated with relevant users, contacts, leads, accounts and opportunities. Salesforce’s matching logic can also be customized using Flow. (Salesforce) The basic workflow is: Gmail / Microsoft 365 → Einstein Activity Capture → Email + calendar activity → Salesforce records → Reports + automation + APIs + AI For many organizations, that’s exactly what they need. The question is what happens after the activity reaches Salesforce. How to Set Up Einstein Activity Capture Setting up Einstein Activity Capture involves three main steps: connect your email account, configure capture settings, and configure how Salesforce matches activity to CRM records. 1. Check your prerequisites Before you begin, make sure: You’re using Salesforce Lightning Experience with the required EAC license. Your organization uses a supported Microsoft 365/Exchange or Google Workspace environment. Your Salesforce admin has the required permissions. Your Microsoft or Google administrator is available to approve the required OAuth/connection permissions. For Microsoft 365, Salesforce now uses Microsoft Graph for EAC connections. 2. Connect your email and calendar In Salesforce: Setup → Quick Find → Einstein Activity Capture → Settings From here, an admin can configure the EAC connection and capture settings, then assign users to the configuration. Depending on your setup, you can configure: Email and calendar capture Privacy settings Header-only email capture Sync Email as Salesforce Activity The last option is particularly important in current EAC implementations because it stores captured email as standard Salesforce activity records such as EmailMessage and Task. 3. Configure email-to-record matching Salesforce uses the Activities: Match Email to Records Flow to determine which Salesforce records captured emails should be associated with. Find it under: Setup → Flows → Activities: Match Email to Records The default Flow matches email addresses against Users, Contacts, Leads, Accounts and Opportunities. Admins can use Save As to create a customized version if they need different matching logic. 4. Test before rolling it out Send a few test emails and verify that they’re being associated with the right Contact, Lead, Account or Opportunity. This step matters because capturing an email and correctly associating it with the right CRM record are two different things. In short: Connect → Configure capture → Configure matching → Test. That is the setup flow a Salesforce admin actually needs to follow. What Einstein Activity Capture Does Well If you’re evaluating Salesforce activity capture options, EAC is generally a good fit for organizations that want core activity capture up and running quickly, without investing heavily in custom configuration or development. It tends to work best when you’re looking for: 1. It removes manual activity logging Sales reps don’t have to remember to manually log every email or meeting. That’s still EAC’s fundamental value proposition. 2. It is deeply integrated with Salesforce For organizations already running Salesforce, EAC provides a native way to connect email and calendar activity with CRM data. 3. Captured email is now Salesforce data With Sync Email as Salesforce Activity, captured emails can be used by standard Salesforce reports, workflows, triggers and APIs. (Salesforce) 4. Matching can be customized Salesforce provides the Activities: Match Email to Records Flow, which administrators can customize to change how captured emails are associated with CRM records. (Salesforce) 5. Salesforce is continuing to invest in the architecture Salesforce is retiring its older Activity 360 Reporting, Activity Metrics and Activities Dashboard capabilities in favor of standard Salesforce activity data. (Salesforce) So the fair assessment isn’t: “EAC is an old, limited activity-capture tool.” It’s: EAC has become a more capable Salesforce-native activity data layer. Einstein Activity Capture Limitations in 2026 The important question in 2026 isn’t whether EAC can capture activity. It can. The question is whether capturing activity automatically gives revenue teams the intelligence they need. Often, it doesn’t. 1. Email aliases aren’t supported Salesforce’s own documentation states that EAC sends email only from the connected account and that alias addresses aren’t supported. Email aliases also can’t be authenticated as separate connected accounts. (Salesforce) For organizations where reps operate with multiple sending identities, this can create gaps between the way people communicate and the way Salesforce captures that communication. 2. EAC doesn’t automatically create an Opportunity Contact Role (OCR) from every interaction EAC

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

Product

How Nektar Puts CRM Data Hygiene on Auto Pilot

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

RevOps

A RevOps Guide to Conquer Bad Data

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

CRM

8 Steps to Maintain CRM Data Hygiene

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

Marketing, Sales

How to Drive Sales and Marketing Alignment With Unified Data

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

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