{"id":2616,"date":"2026-03-26T05:10:42","date_gmt":"2026-03-26T05:10:42","guid":{"rendered":"https:\/\/blog.coffee.ai\/best-gong-calls-team-performance\/"},"modified":"2026-06-29T05:08:14","modified_gmt":"2026-06-29T05:08:14","slug":"best-gong-calls-team-performance","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/best-gong-calls-team-performance","title":{"rendered":"Best Way to Analyze Gong Calls for Team Performance"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: June 28, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Ad-hoc Gong call reviews create inconsistent feedback and miss systemic patterns that structured analysis frameworks can reveal and fix.<\/li>\n<li>A repeatable 7-step process identifies behavioral signals correlated with wins and converts them into a numeric scorecard for consistent coaching.<\/li>\n<li>Syncing Gong insights back to Salesforce or HubSpot fields ensures call quality data shapes pipeline reviews and forecast accuracy.<\/li>\n<li>Weekly coaching rhythms with fixed manager accountability turn scorecard data into measurable rep behavior change and time savings.<\/li>\n<li>Automating the full workflow with Coffee removes manual data entry and scales the framework without adding headcount, <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>see Coffee pricing and plans<\/strong><\/a>.<\/li>\n<\/ul>\n<h2>Step 1: Establish Won\/Lost Outcome Patterns<\/h2>\n<p><strong>Inputs:<\/strong> Closed-won and closed-lost deal records from the past 90 days, with associated Gong call transcripts. <strong>Decision:<\/strong> Identify which call behaviors appear disproportionately in won deals versus lost deals. <strong>Handoff:<\/strong> RevOps exports the deal list, and the analyst or agent tags each transcript with outcome. <strong>Output:<\/strong> A ranked list of behavioral signals, such as multi-threading mentions, pricing discussion timing, or next-step commitment language, correlated with wins.<\/p>\n<blockquote><p><strong>Common Pitfall: Missing CRM Outcome Data.<\/strong> If closed-lost reasons are blank or inconsistently categorized in Salesforce or HubSpot, the correlation analysis produces noise. Audit your CRM before running this step. Every closed deal must carry a standardized outcome reason before transcript analysis begins.<\/p><\/blockquote>\n<h2>Step 2: Build a 5\u20137 Metric Behavioral Scorecard<\/h2>\n<p>A scorecard converts subjective call impressions into a consistent numeric signal. Keep the scorecard to five to seven metrics so managers actually use it. Each metric needs a clear definition and a weight that reflects its contribution to win rate based on the outcome patterns identified in Step 1.<\/p>\n<p>The table below shows a sample scorecard with six core metrics, their definitions, relative weights, and scoring scales that you can adapt to your team.<\/p>\n<table>\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Definition<\/th>\n<th>Weight<\/th>\n<th>Scoring Scale<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Talk-to-Listen Ratio<\/td>\n<td>Rep talk time divided by prospect talk time<\/td>\n<td>20%<\/td>\n<td>1\u20135 (5 = \u226443% rep talk)<\/td>\n<\/tr>\n<tr>\n<td>Next Step Commitment<\/td>\n<td>Explicit agreed next action with date confirmed on call<\/td>\n<td>25%<\/td>\n<td>1\u20135 (5 = confirmed date)<\/td>\n<\/tr>\n<tr>\n<td>Objection Handling<\/td>\n<td>Number of objections acknowledged and addressed<\/td>\n<td>20%<\/td>\n<td>1\u20135 (5 = all addressed)<\/td>\n<\/tr>\n<tr>\n<td>Multi-Threading<\/td>\n<td>Additional stakeholders identified or introduced<\/td>\n<td>15%<\/td>\n<td>1\u20135 (5 = \u22652 new contacts)<\/td>\n<\/tr>\n<tr>\n<td>Competitor Mention Response<\/td>\n<td>Structured response delivered when competitor named<\/td>\n<td>10%<\/td>\n<td>1\u20135 (5 = full response)<\/td>\n<\/tr>\n<tr>\n<td>Discovery Depth<\/td>\n<td>Business impact and timeline questions asked<\/td>\n<td>10%<\/td>\n<td>1\u20135 (5 = both confirmed)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<blockquote><p><strong>Common Pitfall: Inconsistent Call Tagging.<\/strong> Gong&#8217;s automatic topic tags are a starting point, not a finished scorecard. Without a human or agent reviewing transcripts against a defined rubric, tag quality degrades quickly. Create a tagging protocol before scoring begins.<\/p><\/blockquote>\n<h2>Why These Six Gong Metrics Move Win Rates<\/h2>\n<p>The six metrics above tie directly to deal outcomes. Talk-to-listen ratio reflects whether the rep is diagnosing or pitching, and deals where reps talk more than 57% of the time close at materially lower rates. Next-step commitment acts as the strongest leading indicator of deal progression because it shifts part of the accountability to the prospect. Objection handling separates reps who surface risk early from those who allow unspoken concerns to kill deals at the final stage.<\/p>\n<p>Multi-threading reduces single-threaded deal risk, which often causes late-stage losses when a champion leaves or loses influence. Competitor mention response measures whether reps have internalized positioning and can respond with confidence. Discovery depth shows whether the rep has built a business case strong enough to survive a procurement review.<\/p>\n<h2>Step 3: Benchmark Top 20% Performers<\/h2>\n<p>Once you know which metrics matter, you need to define what strong performance looks like by analyzing your top reps. Pull scorecard data for the top quintile of reps by win rate over the past two quarters. Calculate their average score per metric and compare it to the team average. Use this table as the coaching target for each metric.<\/p>\n<table>\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Top 20% Average<\/th>\n<th>Team Average<\/th>\n<th>Gap<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Talk-to-Listen Ratio Score<\/td>\n<td>4.4<\/td>\n<td>3.1<\/td>\n<td>\u22121.3<\/td>\n<\/tr>\n<tr>\n<td>Next Step Commitment Score<\/td>\n<td>4.7<\/td>\n<td>2.9<\/td>\n<td>\u22121.8<\/td>\n<\/tr>\n<tr>\n<td>Objection Handling Score<\/td>\n<td>4.2<\/td>\n<td>3.0<\/td>\n<td>\u22121.2<\/td>\n<\/tr>\n<tr>\n<td>Multi-Threading Score<\/td>\n<td>4.0<\/td>\n<td>2.6<\/td>\n<td>\u22121.4<\/td>\n<\/tr>\n<tr>\n<td>Competitor Mention Response<\/td>\n<td>4.5<\/td>\n<td>3.2<\/td>\n<td>\u22121.3<\/td>\n<\/tr>\n<tr>\n<td>Discovery Depth Score<\/td>\n<td>4.6<\/td>\n<td>3.3<\/td>\n<td>\u22121.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>These figures are illustrative targets. Replace them with your team&#8217;s actual Gong scorecard data once Step 2 is live.<\/p>\n<h2>Step 4: Create Objection and Competitor Trackers<\/h2>\n<p><strong>Inputs:<\/strong> Gong transcripts tagged with objection and competitor topics. <strong>Workflow:<\/strong> Configure Gong&#8217;s Trackers feature to flag keyword clusters for each known objection category, such as pricing, timing, incumbent vendor, and security, and for each named competitor. <strong>Output:<\/strong> A weekly frequency report that shows which objections and competitors appeared most often, broken down by deal stage and rep.<\/p>\n<p>This tracker feeds the coaching agenda with the most urgent skill gaps. It also surfaces competitive intelligence that should flow into sales enablement content. Assign a RevOps owner to review the tracker output every Monday before the coaching session so coaching themes stay grounded in current calls.<\/p>\n<h2>Step 5: Sync Gong Insights to Salesforce\/HubSpot Pipeline Fields<\/h2>\n<p>Gong insights only affect revenue when they reach your CRM. Each scored call should write structured data back to the deal record so pipeline reviews reflect call quality alongside stage and amount.<\/p>\n<table>\n<thead>\n<tr>\n<th>Gong Insight<\/th>\n<th>CRM Field<\/th>\n<th>Field Type<\/th>\n<th>Data Quality Check<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Composite scorecard score<\/td>\n<td>Call Quality Score<\/td>\n<td>Number<\/td>\n<td>Null = flag for re-review<\/td>\n<\/tr>\n<tr>\n<td>Next step confirmed (Y\/N)<\/td>\n<td>Next Step Committed<\/td>\n<td>Checkbox<\/td>\n<td>Must match activity date<\/td>\n<\/tr>\n<tr>\n<td>Competitor mentioned<\/td>\n<td>Competitor Identified<\/td>\n<td>Picklist<\/td>\n<td>Validate against approved list<\/td>\n<\/tr>\n<tr>\n<td>Objection category<\/td>\n<td>Primary Objection<\/td>\n<td>Picklist<\/td>\n<td>Required on Stage 3+ deals<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Run a weekly data-quality audit and treat missing scores as a clear risk. Any Stage 3 or later deal without a Call Quality Score represents a forecast risk because you are forecasting without visibility into call quality. When you see blank fields, treat them as a process failure that signals a broken tagging workflow, not as an acceptable data gap.<\/p>\n<h2>Step 6: Set Up a Weekly Manager Dashboard and Coaching Rhythm<\/h2>\n<blockquote><p><strong>Common Pitfall: Lack of Manager Follow-Through.<\/strong> Dashboards without a fixed meeting cadence become decoration. The framework requires a non-negotiable weekly slot where scorecard data drives the agenda. Without that slot, reps learn that call quality scores have no consequences, and adoption collapses within 60 days.<\/p><\/blockquote>\n<h2>Weekly Gong Coaching Rhythm<\/h2>\n<table>\n<thead>\n<tr>\n<th>Day<\/th>\n<th>Activity<\/th>\n<th>Owner<\/th>\n<th>Time Required<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Monday<\/td>\n<td>Review objection and competitor tracker, set coaching theme<\/td>\n<td>RevOps<\/td>\n<td>20 min<\/td>\n<\/tr>\n<tr>\n<td>Tuesday<\/td>\n<td>Manager reviews 2\u20133 flagged calls against scorecard<\/td>\n<td>Manager<\/td>\n<td>45 min<\/td>\n<\/tr>\n<tr>\n<td>Wednesday<\/td>\n<td>1:1 coaching session per rep using scorecard data<\/td>\n<td>Manager + Rep<\/td>\n<td>30 min\/rep<\/td>\n<\/tr>\n<tr>\n<td>Friday<\/td>\n<td>Pipeline review, correlate call scores with stage movement<\/td>\n<td>Head of Sales<\/td>\n<td>30 min<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The Friday pipeline review acts as the accountability mechanism. If a deal advanced but the call score was low, investigate whether the score rubric needs adjustment. If a deal stalled and the call score was low, treat that pattern as a coaching data point instead of a forecast surprise.<\/p>\n<h2>Step 7: Automate the Entire Workflow with an Agent<\/h2>\n<p>Steps 1 through 6 work manually, but manual execution does not scale past 10 reps without consuming a full-time RevOps resource. Coffee&#8217;s Companion App deploys an autonomous agent on top of existing Salesforce or HubSpot instances to handle every data-capture and enrichment task in this framework without human intervention.<\/p>\n<p>The Coffee Agent solves the scaling problem by automating the entire data pipeline. It ingests Gong transcripts, applies your scorecard rubric, writes structured outputs to the correct CRM fields, and surfaces the weekly coaching dashboard automatically. Managers open their pipeline view and see call quality scores, objection flags, and next-step commitments already populated, which removes the three hours a RevOps analyst would otherwise spend on Monday morning manual data entry. The agent also runs the data-quality checks from Step 5, flags null fields, and routes them for review before the Friday pipeline session.<\/p>\n<p> <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>See Coffee&#8217;s pricing and deployment options for your team size.<\/strong><\/a> <\/p>\n<h2>Validate Results: Data-Quality Checks, Adoption Signals, and Time Savings<\/h2>\n<p>At 30 days, measure three validation signals that together show whether the framework works end to end. First, check data capture: CRM field completion rate on Stage 3+ deals should exceed 90 percent, because lower completion means your tagging workflow has gaps that corrupt downstream analysis. Second, confirm efficiency gains: manager time spent on call review should drop by at least 40 percent compared to the pre-framework baseline, since the agent should now handle data capture automatically. If time has not dropped, the automation or process configuration needs attention.<\/p>\n<p>Third, confirm coaching impact: rep scorecard scores should show measurable week-over-week movement for at least 60 percent of the team. Flat scores signal that coaching sessions are not turning into behavior change, so you may need to adjust the rubric or how managers deliver feedback.<\/p>\n<h2>Scaling the Framework Across Team Sizes and Sales Motions<\/h2>\n<p>Teams of 10 to 20 reps can run this framework with a single RevOps owner while Coffee handles automation. Teams of 20 to 50 reps should segment the scorecard by sales motion, such as inbound versus outbound or SMB versus mid-market, because behavioral benchmarks differ by motion. Top-performer benchmarks from an inbound closing motion do not apply to an outbound prospecting team.<\/p>\n<p>Run separate benchmark tables per segment and assign coaching themes for each group. Keep the CRM field structure and agent configuration consistent so reporting stays clean, and adjust only the rubric weights per segment to reflect different motions.<\/p>\n<p> <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>See how Coffee&#8217;s automation scales across teams of 50+ reps<\/strong><\/a> <\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does initial setup take?<\/h3>\n<p>For teams already using Gong and either Salesforce or HubSpot, the Coffee Companion App connects through a simple authentication flow. CRM field mapping and scorecard configuration stay manageable for a RevOps administrator. The first automated scorecard outputs appear after the first Gong call is processed following setup. The full coaching rhythm, including manager dashboards and the weekly cadence, typically becomes operational shortly after setup.<\/p>\n<h3>Who should own the Gong analysis process?<\/h3>\n<p>RevOps owns the technical configuration, including field mapping, scorecard rubric, data-quality audits, and agent settings. The Head of Sales owns the coaching rhythm, including setting the weekly agenda, conducting 1:1 sessions, and holding managers accountable to the cadence. This split prevents the framework from becoming either a pure data project with no coaching output or a pure coaching initiative with no data discipline. Both roles need defined responsibilities and a shared dashboard before the framework launches.<\/p>\n<h3>Is my call data secure when using an automation agent?<\/h3>\n<p>Coffee is SOC 2 Type 2 certified and GDPR compliant. Call transcript data processed by the Coffee Agent is not used to train public models. Data flows through secure connections between Gong and your CRM to produce structured outputs. Teams in regulated industries should review Coffee&#8217;s security documentation with legal and compliance teams before deployment.<\/p>\n<h3>How often does the framework need maintenance?<\/h3>\n<p>The scorecard rubric should be reviewed quarterly. As your sales motion evolves, such as new competitors, pricing changes, or ICP shifts, the behavioral benchmarks and metric weights must reflect current won and lost patterns instead of outdated ones. The CRM field mapping needs maintenance only when Salesforce or HubSpot schema changes occur. The agent configuration updates automatically when new Gong Tracker keywords are added. Plan a 60-minute quarterly review session between RevOps and the Head of Sales to keep the framework calibrated.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Analyze Gong calls with a proven 7-step framework. Coffee scales rep coaching, extracts insights, and logs them to your CRM automatically. See how.<\/p>\n","protected":false},"author":11,"featured_media":2615,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2616","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\/2616","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=2616"}],"version-history":[{"count":3,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/2616\/revisions"}],"predecessor-version":[{"id":7966,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/2616\/revisions\/7966"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/2615"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=2616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=2616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=2616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}