{"id":2002,"date":"2026-03-11T18:32:08","date_gmt":"2026-03-11T18:32:08","guid":{"rendered":"https:\/\/blog.coffee.ai\/how-conversation-intelligence-improves-sales\/"},"modified":"2026-07-11T05:07:07","modified_gmt":"2026-07-11T05:07:07","slug":"how-conversation-intelligence-improves-sales","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/how-conversation-intelligence-improves-sales","title":{"rendered":"How Conversation Intelligence Improves Sales"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: July 10, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Why Conversation Intelligence Works for Modern Sales Teams<\/h2>\n<ul>\n<li>Conversation intelligence uses AI to record, transcribe, and analyze every sales call, then turns those conversations into structured CRM data.<\/li>\n<li>Teams recover 5\u20137 hours per rep each week and see higher win rates, faster ramp times, and 5\u201315% larger deal sizes through data-driven coaching.<\/li>\n<li>AI forecasting improves accuracy from the 60\u201370% range to above 85% by filling qualification fields directly from real buyer conversations.<\/li>\n<li>Real-time signals for buyer intent, objections, and deal risks give managers time to intervene early and coach more than 20 reps instead of the traditional 8\u201310.<\/li>\n<li>Eliminate manual CRM entry and turn every call into usable data with <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Coffee<\/a>.<\/li>\n<\/ul>\n<h2>Why Data Quality Powers Every Conversation Intelligence Outcome<\/h2>\n<p>Accurate CRM data sits at the center of every benefit conversation intelligence delivers. When data comes straight from calls instead of rushed notes, forecasts improve, coaching becomes specific, and deal risks surface earlier. Conversation intelligence fixes the data problem by capturing what buyers say and writing it into the CRM without relying on rep memory or manual updates.<\/p>\n<p><a href=\"https:\/\/askelephant.ai\/blog\/improve-crm-data-quality-conversation-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">HubSpot&#8217;s research on database decay puts B2B contact data decay at approximately 22.5% per year<\/a>, so any CRM that depends on manual entry degrades quickly. Coffee&#8217;s agent addresses this decay by automating three streams at once: contact and company creation from emails and calendars, activity logging for every interaction, and structured qualification data pulled from call transcripts.<\/p>\n<h2>7 Measurable Outcomes Conversation Intelligence Delivers<\/h2>\n<ol>\n<li><strong>Admin time recovered:<\/strong> Sales reps spend an average of 28% of their week actually selling, and conversation intelligence recovers 5\u20137 hours per rep per week by automating notes, CRM entry, and follow-up drafting.<\/li>\n<li><strong>Win-rate lift:<\/strong> <a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-roi\" target=\"_blank\" rel=\"noindex nofollow\">Teams see close-rate improvement as coaching surfaces top-performer behaviors across every call<\/a>.<\/li>\n<li><strong>Forecast accuracy improvement:<\/strong> <a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-roi\" target=\"_blank\" rel=\"noindex nofollow\">Forecast accuracy climbs from the 60\u201370% range to above 85%<\/a> when qualification fields are filled from call data instead of rep self-reports.<\/li>\n<li><strong>Faster rep ramp:<\/strong> <a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-software\" target=\"_blank\" rel=\"noindex nofollow\">New-rep ramp compresses when AI coaching insights are available on every recorded call from day one<\/a>.<\/li>\n<li><strong>Larger deal sizes:<\/strong> <a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-roi\" target=\"_blank\" rel=\"noindex nofollow\">Deal sizes expand 5\u201315%<\/a> through deeper discovery and stronger objection handling informed by conversation data.<\/li>\n<li><strong>CRM data quality:<\/strong> <a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-software\" target=\"_blank\" rel=\"noindex nofollow\">CRM field completeness improves when conversation intelligence writes structured data directly to records after every call<\/a>.<\/li>\n<li><strong>Coaching scale:<\/strong> <a href=\"https:\/\/traq.ai\/conversation-intelligence-and-sales-coaching\" target=\"_blank\" rel=\"noindex nofollow\">A single manager can deliver meaningful, data-backed coaching to 20 or more reps<\/a> instead of 8\u201310, because AI summaries replace hours of call listening.<\/li>\n<\/ol>\n<h2>Coaching Every Rep with Conversation Intelligence<\/h2>\n<p>Managers currently review only 2\u20135% of sales calls, so most rep activity stays invisible and coaching relies on guesswork. Conversation intelligence analyzes every conversation and highlights the objection that went unaddressed, the discovery question that never came, and the buying signal a rep missed. These patterns stay hidden when managers can only sample a few calls each week. Coaching grounded in this data shifts focus toward better discovery and consistent objection handling, which improves close rates over time.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee&#8217;s AI meeting bot joins every Zoom, Teams, or Meet call, transcribes with speaker attribution, and structures notes against BANT, MEDDIC, or SPICED. Each coaching session then relies on what buyers and reps actually said, not on partial notes or memory.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678412915-a11943d2b0b8.gif\" alt=\"Join a meeting from the Coffee AI platform\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Join a meeting from the Coffee AI platform<\/em><\/figcaption><\/figure>\n<p><strong>Implementation note:<\/strong> SMB teams with 5\u201320 seats gain full coverage of calls without hiring an enablement manager. Mid-market teams with 20\u201350 seats use the same data to run consistent coaching programs across several managers without duplicating effort.<\/p>\n<h2>Using Conversation Intelligence to Improve Pipeline Forecasting<\/h2>\n<p>The same conversation data that powers coaching also strengthens pipeline forecasting because both depend on accurate deal information. AI-blended forecasting models improve accuracy because they rely on buyer statements instead of rep-assigned stages. When qualification criteria such as MEDDPICC come straight from transcripts, the pipeline reflects reality instead of optimism. <a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-roi\" target=\"_blank\" rel=\"noindex nofollow\">Forecast accuracy moves from the 60\u201370% range to above 85%<\/a> as this data-quality shift takes hold.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee&#8217;s Pipeline Compare feature shows week-over-week changes such as progressed deals, stalled opportunities, and new additions without spreadsheets or CSV exports. Pipeline reviews become strategic discussions about risk and momentum instead of interrogation sessions about field updates.<\/p>\n<p><strong>Implementation note:<\/strong> SMBs running weekly pipeline reviews can replace ad hoc spreadsheet exports with Pipeline Compare on day one. Mid-market RevOps teams layer Pipeline Compare on top of Salesforce or HubSpot through Coffee&#8217;s Companion App, which keeps the existing system of record while removing manual override cycles.<\/p>\n<h2>Spotting Deal Risks Early with Conversation Intelligence<\/h2>\n<p><a href=\"https:\/\/mindtickle.com\/blog\/use-conversation-intelligence-for-buyer-intent-in-sales-calls\" target=\"_blank\" rel=\"noindex nofollow\">Deal-risk indicators flagged by conversation intelligence include sudden negative sentiment shifts late in calls, language moving from exploratory to evasive, slowed response times, decreased question rates, long monologues, and late-stage competitive mentions<\/a>. These patterns signal that a buyer is disengaging before they say so directly. <a href=\"https:\/\/getrafiki.ai\/sales\/buyer-intent-signals-sales-conversations\" target=\"_blank\" rel=\"noindex nofollow\">Vague post-call next steps such as &#8220;Let&#8217;s reconnect soon&#8221; instead of &#8220;Send SOW by Friday&#8221; act as especially reliable stall indicators<\/a> because they show reluctance to commit to a clear action. Surfacing these signals before they show up as stage changes in the CRM gives managers time to intervene while the deal still has a chance, instead of discovering the problem after the quarter closes.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee&#8217;s post-call summaries flag unresolved objections and missing next steps right after each meeting. Reps and managers then gain a specific intervention point instead of a vague stalled opportunity weeks later.<\/p>\n<p><strong>Implementation note:<\/strong> SMB teams without a deal-desk function use Coffee&#8217;s automated risk flags as a simple early-warning system. Mid-market teams route high-risk alerts into Slack for manager review, which enables fast responses without new headcount.<\/p>\n<h2>Cutting Admin Work with Conversation Intelligence<\/h2>\n<p><a href=\"https:\/\/r-sun.ai\/insights\/ai-driven-b2b-sales-2026\" target=\"_blank\" rel=\"noindex nofollow\">The average seller spends only 40% of their time actually selling per Salesforce State of Sales 2026<\/a>, while CRM updates, note-taking, and follow-up drafting consume the rest. <a href=\"https:\/\/traq.ai\/what-roi-should-you-expect-from-conversation-intelligence-software\" target=\"_blank\" rel=\"noindex nofollow\">Conversation intelligence recovers 5\u20137 hours per rep per week on post-call admin tasks, with time savings visible from the first recorded call<\/a>. That recovered time converts directly into more selling capacity without adding headcount.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee extends these gains by also handling contact creation and enrichment. The agent saves reps 8\u201312 hours per week by creating and enriching contacts, logging last and next activity, generating post-call summaries, identifying next steps, and drafting follow-up emails in Gmail for one-click review and send.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678321672-5c8717cf0024.gif\" alt=\"Create instant meeting follow-up emails with the Coffee AI CRM agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Create instant meeting follow-up emails with the Coffee AI CRM agent<\/em><\/figcaption><\/figure>\n<p><strong>Implementation note:<\/strong> SMBs connect Google Workspace or Microsoft 365 and see automatic contact creation and activity logging start immediately. Mid-market teams using the Companion App send the same structured data into Salesforce or HubSpot fields without changing current workflows.<\/p>\n<h2>Reading Buyer Intent Signals from Conversations<\/h2>\n<p><a href=\"https:\/\/mindtickle.com\/blog\/use-conversation-intelligence-for-buyer-intent-in-sales-calls\" target=\"_blank\" rel=\"noindex nofollow\">Explicit buyer-intent signals detected by conversation intelligence include budget discussions, implementation timelines, procurement questions, stakeholder involvement, and decision-making process details<\/a>. These signals show how serious a buyer feels about moving forward. <a href=\"https:\/\/getrafiki.ai\/sales\/buyer-intent-signals-sales-conversations\" target=\"_blank\" rel=\"noindex nofollow\">Champion signals appear in possessive, forward-looking language such as &#8220;When we implement this&#8221; instead of &#8220;If this were adopted&#8221;<\/a>, which helps AI separate true internal sponsors from friendly but low-influence contacts. Detecting these signals in real time lets reps advance deals while momentum stays high instead of chasing buyers after interest fades.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee&#8217;s Visitor Identification pixel adds website behavior to call-based intent signals by turning anonymous visitors into named, qualified prospects with name, title, email, LinkedIn profile, pages visited, and time on site. Reps then see website intent and call intent together in a single agent view.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678186019-5cc1a76ac78e.gif\" alt=\"Build people lists automatically with Coffee AI CRM Agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Build people lists automatically with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p><strong>Implementation note:<\/strong> SMBs activate the Visitor Identification pixel with one script tag and receive real-time Slack alerts for high-fit visitors. Mid-market teams push identified visitors directly into outbound sequences, which connects intent signals to pipeline creation without manual handoffs.<\/p>\n<h2>Improving Objection Handling with Conversation Intelligence<\/h2>\n<p><a href=\"https:\/\/pipeline.zoominfo.com\/sales\/conversation-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">Keyword and topic tracking in conversation intelligence captures competitor names, pricing discussions, objection patterns, and feature requests<\/a>, which reveals what drives or blocks deals at scale. Aggregated objection data across all reps shows which objections stem from messaging and which come from individual skill gaps. <a href=\"https:\/\/traq.ai\/conversation-intelligence-and-sales-coaching\" target=\"_blank\" rel=\"noindex nofollow\">Conversation intelligence also surfaces intent signals such as objections that were acknowledged but never explored<\/a>, for example when a buyer says, &#8220;We tried something similar before and it did not work,&#8221; and the rep moves on without follow-up.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee&#8217;s structured summaries tag objections by type and map them to deal stage. Managers then search a library of how top performers handled pricing pushback, competitive comparisons, or security concerns without listening to full recordings.<\/p>\n<p><strong>Implementation note:<\/strong> SMB teams use Coffee&#8217;s objection library to build onboarding playbooks and shorten ramp time. Mid-market enablement teams use the same data to refine talk tracks and measure whether new messaging reduces objection frequency over each quarter.<\/p>\n<h2>Automating CRM Data with Conversation Intelligence<\/h2>\n<p><a href=\"https:\/\/askelephant.ai\/blog\/improve-crm-data-quality-conversation-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">AI produces more accurate first-pass CRM data than manual entry because it extracts structured signals directly from the transcript as soon as the call ends<\/a>. <a href=\"https:\/\/askelephant.ai\/blog\/improve-crm-data-quality-conversation-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">Validity&#8217;s 2025 State of CRM Data Management report found that 37% of CRM users lost revenue directly because of poor data quality<\/a>. Automated CRM writes from conversation data remove the main cause of that revenue loss.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee auto-creates contacts and companies from emails and calendars, enriches records with job titles, funding data, and LinkedIn profiles, and logs every activity on its own. The CRM then reflects the current state of each deal without manual field updates.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678641499-bad085f8165f.gif\" alt=\"Building a company list with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Building a company list with Coffee AI<\/em><\/figcaption><\/figure>\n<p><strong>Implementation note:<\/strong> SMBs using Coffee&#8217;s Standalone CRM get automated data entry as the default behavior from day one. Mid-market teams deploy the Companion App to send structured conversation data into existing Salesforce or HubSpot fields, including custom qualification properties, while keeping their current system of record.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Eliminate manual CRM entry and let Coffee keep your pipeline accurate.<\/strong><\/a><\/p>\n<h2>Driving Win Rate Improvement with Conversation Intelligence<\/h2>\n<p><a href=\"https:\/\/sybill.ai\/blogs\/conversation-intelligence-roi\" target=\"_blank\" rel=\"noindex nofollow\">Conversation intelligence delivers productivity gains and revenue lift through higher close rates<\/a>. Win-rate gains compound across better coaching, stronger objection handling, and deeper discovery, all supported by the same conversation data. <a href=\"https:\/\/mindtickle.com\/blog\/use-conversation-intelligence-for-buyer-intent-in-sales-calls\" target=\"_blank\" rel=\"noindex nofollow\">Studies show conversation intelligence can increase revenue generated by reps<\/a> when teams act on these insights consistently.<\/p>\n<p><strong>Coffee agent example:<\/strong> Coffee&#8217;s agent captures structured qualification data on every call and surfaces it in Pipeline Compare. Managers then see which deal traits correlate with closed-won outcomes and can coach the team to repeat those patterns.<\/p>\n<p><strong>Implementation note:<\/strong> SMBs often see win-rate movement within one to two quarters as coaching and CRM data quality improve together. Mid-market teams track win-rate changes by rep, segment, and deal size using the structured data Coffee writes to Salesforce or HubSpot, which supports field-level attribution.<\/p>\n<h2>CRM Data Quality in Practice: Pipeline Compare<\/h2>\n<p>The Pipeline Compare feature shows how strong data quality changes daily sales work. Coffee maintains a built-in data warehouse that preserves historical context instead of overwriting fields. Week-over-week pipeline changes then appear automatically, including progressed deals, stalled opportunities, new additions, and lost deals, without spreadsheets or manual exports. Pipeline reviews turn into strategic conversations grounded in buyer words instead of subjective rep updates.<\/p>\n<h3>Implementation Roadmap<\/h3>\n<ul>\n<li><strong>Step 1 \u2014 Connect Google Workspace or Microsoft 365:<\/strong> Authenticate Coffee to email and calendar so the agent can scan for contacts, companies, and activities and auto-populate the CRM with accurate, enriched records.<\/li>\n<li><strong>Step 2 \u2014 Enable the AI meeting bot:<\/strong> Allow Coffee to join Zoom, Teams, and Meet calls to record, transcribe, and generate structured summaries, action items, and follow-up drafts aligned to BANT, MEDDIC, or SPICED.<\/li>\n<li><strong>Step 3 \u2014 Activate the Visitor Identification pixel:<\/strong> Add a single script tag to the site head so Coffee can identify anonymous visitors as named prospects, send real-time Slack alerts, and route high-fit leads into outbound workflows.<\/li>\n<\/ul>\n<p><strong>Common Adoption Pitfalls<\/strong><\/p>\n<ul>\n<li><strong>Manual overrides:<\/strong> Allowing reps to edit auto-populated CRM fields reintroduces the data-quality problem that conversation intelligence solves. Set a policy that conversation data serves as the system of record.<\/li>\n<li><strong>Incomplete integrations:<\/strong> Connecting the meeting bot without email and calendar leaves gaps in activity logging. All three data streams must run together for Pipeline Compare to show a complete deal history.<\/li>\n<li><strong>Skipping methodology configuration:<\/strong> Deploying the meeting bot without choosing a qualification framework such as BANT, MEDDIC, or SPICED creates unstructured summaries that cannot fill CRM fields or support consistent coaching.<\/li>\n<\/ul>\n<p><strong>Success Metrics to Track<\/strong><\/p>\n<ul>\n<li><strong>Win-rate lift:<\/strong> Compare baseline close rate with close rate at 90 and 180 days after deployment, segmented by rep and deal size.<\/li>\n<li><strong>Forecast accuracy:<\/strong> Measure forecast-to-actual variance before and after conversation intelligence activation and aim to move from the 60\u201370% range toward 85% or higher.<\/li>\n<li><strong>Time saved per rep:<\/strong> Track weekly hours recovered from admin tasks and target 5\u201312 hours per rep within the first month.<\/li>\n<li><strong>CRM field completeness:<\/strong> Measure the share of opportunity records with all required qualification fields filled and target more than 90%.<\/li>\n<li><strong>Coaching coverage:<\/strong> Track the percentage of calls reviewed with AI-generated summaries versus manual reviews and aim for 100% AI coverage within two weeks of bot activation.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>See Pipeline Compare on your live deals and start your Coffee trial today.<\/strong><\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does it take to set up Coffee conversation intelligence?<\/h3>\n<p>Most teams become fully operational within a single business day. Setup follows three steps: authenticate Google Workspace or Microsoft 365, enable the AI meeting bot for Zoom, Teams, or Meet, and add the Visitor Identification pixel to the site head tag. Coffee verifies each connection and starts capturing data immediately. Standard deployments avoid complex field mapping or professional services. Teams using the Companion App on Salesforce or HubSpot complete a simple authentication so Coffee can read and write to their existing CRM schema, including custom fields and qualification frameworks.<\/p>\n<h3>Is Coffee compatible with Salesforce and HubSpot?<\/h3>\n<p>Coffee works with both Salesforce and HubSpot through a Companion App model. For teams on these CRMs, Coffee acts as an intelligent agent layer that handles data-in processes and writes structured conversation data, enrichment, and qualification signals back to the primary CRM. This approach preserves current workflows, quotas, forecasting setups, and required fields while removing manual data entry. Coffee understands Salesforce and HubSpot integration complexity, including custom objects, picklists, required fields, and forecast categories, which separates it from newer CRM tools with shallow integrations. Additional integrations with other sales tools are available through Zapier, with deeper native options planned.<\/p>\n<h3>How does Coffee protect sales conversation data?<\/h3>\n<p>Coffee is SOC 2 Type 2 certified and GDPR compliant. Conversation data from the AI meeting bot, including recordings, transcripts, and extracted signals, lives in Coffee&#8217;s built-in data warehouse and never trains public AI models. This protection matters for sales teams whose calls contain sensitive information such as pricing, competitive details, and customer-specific terms. Teams in regulated-adjacent industries can review Coffee&#8217;s security documentation to confirm alignment with internal data governance requirements before deployment.<\/p>\n<h3>What is Coffee&#8217;s pricing model?<\/h3>\n<p>Coffee uses a straightforward seat-based model. Each human seat carries a fixed price, and the Coffee agent&#8217;s work, including unlimited recording, transcription, CRM writes, enrichment, pipeline analysis, and visitor identification, stays included in that price without extra metering on AI usage or data volume. This model supports scaling SMBs that need predictable costs and do not want usage caps to limit agent value. There are no setup fees, and teams can review current pricing and plan options on the pricing page.<\/p>\n<h3>How does Coffee scale from 5 to 50 seats?<\/h3>\n<p>Coffee&#8217;s agent-first architecture scales linearly with team size because the agent handles data entry, enrichment, meeting management, and pipeline analysis regardless of call volume. A 5-person founding team and a 50-person sales org use the same core agent, with only conversation volume and seat count changing. SMBs that start on the Standalone CRM can move to the Companion App model if they later adopt Salesforce or HubSpot, while keeping all historical conversation data and pipeline context in Coffee&#8217;s data warehouse. There are no seat minimums, and teams can add seats gradually as headcount grows without contract changes or new integration work.<\/p>\n<h2>Conclusion: Turning Conversations into Revenue<\/h2>\n<p>Legacy CRMs produce weak data because they depend on busy humans to type it in. Weak data then produces bad forecasts, hidden deal risks, unscalable coaching, and reps who spend more time updating fields than closing deals. Conversation intelligence fixes the root problem by capturing structured and unstructured data from every call automatically, writing it into the CRM in real time, and surfacing the signals that move deals forward or put them at risk.<\/p>\n<p>Coffee&#8217;s agent-first approach applies this logic across the entire revenue workflow, from the first anonymous website visit through post-call follow-up, pipeline review, and coaching. Every conversation becomes a reusable data asset instead of a forgotten moment. The result is a CRM that stays accurate on its own, forecasts that reflect real buyer words, and a sales team that spends more time selling.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Turn every conversation into accurate pipeline intelligence with Coffee.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discover how conversation intelligence drives higher win rates, smarter coaching &amp; accurate forecasting. Turn every call into data with Coffee.<\/p>\n","protected":false},"author":11,"featured_media":1983,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2002","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/2002","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/comments?post=2002"}],"version-history":[{"count":3,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/2002\/revisions"}],"predecessor-version":[{"id":8105,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/2002\/revisions\/8105"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/1983"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=2002"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=2002"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=2002"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}