ConversationPilot aggregates your calls into trends, scores and coaching insights — talk-listen ratios, qualification coverage, objections and patterns across reps — so managers coach from evidence, not memory.
Works on Zoom, Teams & Google Meet · Mac & Windows · 7-day free trial
A single call tells you about one conversation. Conversation analytics tell you about how a rep, a team or an organisation sells — the patterns, trends and gaps that only become visible across many calls. ConversationPilot turns every call it coaches into structured data, then aggregates that data into analytics managers and reps can actually use to improve.
Because ConversationPilot already produces consistent, objective artefacts on every call — a call score, qualification coverage, talk-listen ratios, objection logs, detected signals — the analytics are built on a reliable foundation rather than on subjective impressions. The metrics come from the actual conversations, captured with exact speaker separation, so a talk-listen trend or a qualification gap is a measured fact, not a guess. Managers get team dashboards, leaderboards, playbook-compliance views and a searchable call review library; reps get visibility into their own numbers over time.
It runs as a discreet desktop overlay on Zoom, Teams and Meet during the call, and the analytics layer lives in the dashboard afterward. The point is to make coaching systematic: instead of guessing who needs help based on which calls a manager happened to hear, the analytics show exactly where the patterns are — and the same real-time engine that scores each call makes the aggregate trustworthy.
Conversation analytics are the aggregated metrics and trends derived from many calls, as opposed to the live coaching that happens on a single call. Where the in-call layer answers "what should I do right now," the analytics layer answers "how are we selling, and where are the patterns."
ConversationPilot aggregates the structured artefacts it produces on every call: call scores, qualification coverage across the scorecard criteria, talk-to-listen ratios, interruption and question-frequency stats, objection logs, and detected signals. Across many calls, those individual data points become trends — whether a rep's talk-listen ratio is improving, which objections come up most often, where qualification consistently breaks down. Each metric is consistent because every call is scored the same way, which is what makes comparison meaningful. The analytics turn a pile of individual calls into a clear picture of performance.
Any one call can be an outlier — a great rep can have a bad call, and a weak rep can stumble into a good one. Trends across many calls are where the real signal lives, and that is what conversation analytics surface. A rep who dominates the airtime on one demo might be coincidence; a rep whose talk-listen ratio runs high across thirty calls has a habit worth coaching.
ConversationPilot makes those trends visible. You can see whether a rep's qualification coverage is improving over time, whether a particular objection is becoming more common across the team, or whether call scores are trending up after a coaching push. These patterns are invisible from any single call and obvious in aggregate. The analytics give managers the longitudinal view that turns scattered observations into a clear narrative of what is working, what is slipping, and where to focus.
Conversation analytics are only useful if they are accessible and actionable. ConversationPilot surfaces them through a manager dashboard, call leaderboards, team benchmarks, playbook-compliance views and a searchable call review library.
The dashboard gives managers an at-a-glance read across the team — call scores, qualification coverage, talk-listen ratios, objection patterns. Leaderboards and benchmarks let reps and managers see where individuals stand relative to the team. Playbook-compliance views show whether reps are actually running calls the way the playbook intends. And the call review library lets a manager jump from a trend straight to the specific calls behind it — so an analytic finding like "this rep never qualifies budget" is backed by real moments to review and coach from. The analytics and the underlying calls stay connected, which is what keeps the insight grounded.
Most coaching is inconsistent because it depends on which calls a manager happened to hear, and the calls that get reviewed are rarely the ones that most needed help. Conversation analytics fix the sampling problem. Because every call is scored and aggregated, managers can see across the whole team rather than a handful of sampled calls.
This changes how one-on-ones work. Instead of "I think you talk too much," a manager can say "your talk-listen ratio is 70% across your last twenty calls, here are three examples, let's fix it." The analytics make the feedback objective and specific, which makes it harder to dismiss and easier to act on. And because reps can see their own analytics, the self-awareness often does half the work — reps who can see their numbers trending the wrong way tend to correct course before a manager has to intervene. Coaching becomes systematic and evidence-led rather than anecdotal.
Analytics are only as trustworthy as the data underneath them, and ConversationPilot's data is unusually solid. Because the product captures each speaker as a separate stream, the metrics that feed the analytics — talk-listen ratios, interruptions, question frequency, speaker-attributed objections and signals — are exact rather than estimated. A trend built on precise per-call numbers is a trend you can act on.
This matters because analytics built on shaky inputs lead managers to coach the wrong things. If the talk-listen ratio is a rough guess from a mixed audio channel, a trend in it might be noise. With exact speaker separation, the underlying numbers are reliable, so the aggregated trends genuinely reflect behaviour. The same architectural choice that makes the live coaching trustworthy makes the analytics trustworthy — accuracy at the level of a single call rolls up into accuracy at the level of the team.
Conversation analytics apply to recruitment just as much as sales. ConversationPilot produces the same structured artefacts on recruitment calls — a recruitment call score, scorecard coverage across salary, notice period, motivation, eligibility, availability and culture-fit, candidate-signal logs and speaking analytics — and aggregates them the same way.
That means a recruitment manager gets the same dashboards, leaderboards, benchmarks and call library as a sales manager, surfacing trends across the talent function: which screening criteria reps consistently miss, how candidate signals trend, where call scores are improving. Because one product covers both motions, an organisation can run conversation analytics across its entire revenue-and-talent operation rather than buying separate analytics tools for sales and recruitment. The same exact, objective, aggregated approach gives both kinds of team a clear, evidence-based view of how their conversations are actually going.
| Capability | ConversationPilot AI | Recorders / note-takers |
|---|---|---|
| Coverage of calls | Every call scored | Sampled reviews |
| Trends over time | Aggregated automatically | Manual, ad hoc |
| Metric accuracy | Exact via separate streams | Estimated |
| Link to source calls | Searchable call library | Scattered recordings |
| Coaching basis | Objective evidence | Memory and impressions |
| Sales and recruitment | Both, same analytics | Sales-only / generic |
They're the aggregated metrics and trends derived from many calls — call scores, qualification coverage, talk-listen ratios, objection logs and detected signals — as opposed to live coaching on a single call. ConversationPilot turns every call into structured data, then aggregates it into trends managers and reps can act on.
A call report tells you about one conversation; conversation analytics tell you how a rep or team sells across many. Trends across calls reveal patterns a single call hides — like a talk-listen ratio that runs high over thirty calls, which is a habit worth coaching rather than a one-off.
It includes call scores, qualification coverage, talk-listen ratios, objection patterns, team benchmarks, leaderboards, playbook-compliance views and a searchable call review library — so a manager can jump from a trend straight to the specific calls behind it and coach from real moments.
Yes, because they're built on exact data. ConversationPilot captures each speaker as a separate stream, so talk-listen ratios, interruptions, question frequency and attributed objections and signals are precise rather than estimated. Accurate per-call numbers roll up into trustworthy team-level trends.
They fix the sampling problem. Instead of coaching from the handful of calls a manager happened to hear, analytics show patterns across every call. A manager can say "your talk-listen ratio is 70% across your last twenty calls, here are three examples" — objective, specific feedback that's easy to act on.
Yes. ConversationPilot produces the same structured artefacts on recruitment calls — a recruitment call score, scorecard coverage, candidate-signal logs and speaking analytics — and aggregates them into the same dashboards and trends, so recruitment managers get an evidence-based view of their team too.
Real-time prompts, objection handling and qualification — while the call is happening.