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Sales Manager

Join a rep on a customer call and coach afterward

Enhances✓ Available Now

What You Do Today

Listen in on a discovery call or demo, observe the rep's approach, take notes on what worked and what didn't, and provide coaching feedback immediately after.

AI That Applies

Call analytics — AI analyzes the call recording for talk-to-listen ratio, question types, objection handling, and competitive mentions.

Technologies

How It Works

The system ingests call recording for talk-to-listen ratio as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Your coaching is data-backed: 'You talked 65% of the call. You asked 2 discovery questions. The buyer mentioned a competitor twice and you didn't follow up.'

What Stays

The coaching itself — building the rep's confidence, teaching deal craft, and knowing when they need encouragement versus tough love.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for join a rep on a customer call and coach afterward, understand your current state.

Map your current process: Document how join a rep on a customer call and coach afterward works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The coaching itself — building the rep's confidence, teaching deal craft, and knowing when they need encouragement versus tough love. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Gong tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long join a rep on a customer call and coach afterward takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Sales or CRO

What are the top 5 reasons customers contact us, and which of those could be resolved without a human?

They're evaluating AI tools that will change your workflow

your sales ops or RevOps lead

How do we currently measure service quality, and would AI-assisted responses change that measurement?

They manage the CRM and data infrastructure your AI tools depend on

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.