Connectors

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Chorus

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Chorus rep coaching review
Report

Coach reps on their real weak spots, backed by Chorus scorecards

For sales managers who want a rep's scorecards, reviewer comments and past coaching pulled together before a session, by asking their AI assistant.

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Chorus rep coaching review
Prepare a coaching review for Jordan Lee from the last 90 days of Chorus scorecards.
Jordan Lee has 9 scorecards from 41 conversations in the last 90 days. - Strength: discovery questions, average 4.6 of 5 - Weak: next steps, average 2.4, reviewer wrote no clear close on the call - Raised before: talk ratio in July, now improved - Focus: agree a dated next step on every call Want the quoted reviewer comments for each area?
Chorus
Sample figures. Your results come from your own account.
chorus-get-users
chorus-get-scorecards
chorus-get-scorecard-scores
chorus-get-moments
chorus-get-engagements

On this page

The problem

The problem

The prompt

Who it's for

How it works

Tools used

Connector

Chorus

Chorus
Run it on your own Chorus data.

Connect Chorus, pick your AI tool and paste the prompt.

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The problem

Coaching sessions repeat old points or miss the weak spot

Preparing a coaching session means pulling a rep's Chorus scorecards, reading question level scores and comments, and checking feedback they already got. Without that, coaching repeats old points or misses the real weak spot.

Without PopdockAI

  • Pull the rep's scorecards for the period
  • Read each question's score and comment
  • Look back at earlier coaching moments
  • Guess at the trend from a small sample

With PopdockAI

  • Scorecards, question scores and comments are read
  • Past coaching moments are checked for what landed
  • Scorecard types are kept apart and small samples flagged
  • One focus area is suggested and nothing is changed

The prompt

Use this prompt

Copy it into any AI tool connected to PopdockAI. Swap the values in braces for your own.

Prompt
**Objective:** Build a coaching review for `{rep-name}` covering the last `{lookback-days}` days (default 90): what they have been scored on, where the scores are weak, and what has already been said to them.

**Step 1: Resolve the person**

Call `chorus-get-users` with `name.contains` set to `{rep-name}`. If more than one active user matches, list name, email, title and role and stop, asking which. Keep the exact `name` and the `email`, since the scorecard tools match on the full name and the conversation tools match on the email address.

**Step 2: Read the scorecards**

Call `chorus-get-scorecards` with `recipient-name` set to the exact name from step 1 and `submitted.from` set to the start of the window. Note `initiative-name`, `reviewer-name`, `submitted` and `scorecard-id`. If there are none, say so plainly, since an unreviewed rep is the finding.

**Step 3: Drill into the scores**

For each scorecard, call `chorus-get-scorecard-scores` with `scorecard-id` set to that scorecard's `scorecard-id`. Collect `question`, `score` and `comment`. Then work out, across all the scorecards together, which questions score lowest on average and which ones move over time.

**Step 4: Read the coaching already given**

Call `chorus-get-moments` with `recipient-name` set to the exact name and `submitted.from` set to the start of the window. These are the coaching moments already recorded. Note `initiative-name`, `reviewer-name`, `description` and `submitted`.

**Step 5: Ground it in activity**

Call `chorus-get-engagements` with `user-email` set to the email from step 1 and `date.from` set to the start of the window, to see how many conversations the scores are drawn from and how long they run.

**Step 6: Report**

Produce:

* **Summary** - how many conversations, how many scorecards, average score, and the direction of travel.
* **Strengths** - the two or three highest scoring questions, with a reviewer comment quoted for each.
* **Development areas** - the two or three lowest scoring questions, with the comments quoted.
* **Coaching already given** - what has been raised before, and whether the scores since suggest it landed.
* **Recommended focus** - one area to work on next, and why.

**Rules & Guidelines:**

* This is read only. Do not call `chorus-add-moment`, `chorus-add-playlist-moment`, `chorus-create-playlist`, `chorus-update-playlist`, `chorus-update-playlist-moment`, `chorus-update-moment`, `chorus-export-recordings` or `chorus-export-scorecards`.
* Do not average across different scorecard types as though they were one scale. Group by `scorecard-type` first and say so if they differ.
* Do not infer intent from a low score. Quote the reviewer comment or say there was none.

**Validation:**

* If no user matches `{rep-name}`, say so and suggest searching by part of the name.
* If the user exists but has no scorecards in the window, report the conversation activity alone and flag the review gap.
* If fewer than three scorecards exist, say the sample is too small to read a trend from.

Who it's for

One answer, three teams that use it

Sales managers

Walk into one to ones prepared

Strengths, weak areas and one focus come back with reviewer comments quoted as evidence.

Prepare a coaching review for Priya Shah from Chorus scorecards in the last 60 days.

Sales enablement

See whether coaching landed

What was raised before is checked against recent scores, so you know if feedback stuck.

Has the feedback we gave Sam Patel on discovery in Chorus improved his scores this quarter?

Team leads

Coach new reps on evidence

Small samples are flagged, so you do not read a trend into two calls.

Review Chorus scorecards for our new rep Alex Morgan since they started.

How it works

What PopdockAI does when you ask

You name the rep and your AI assistant prepares a coaching review from Chorus. It counts the conversations behind the scores so you know how solid the picture is.

01

Ask

You name the rep and how far back to look.

Your AI tool

02

Read

It reads their scorecards, each question's score and comment, and past coaching.

chorus-get-users
chorus-get-scorecards
chorus-get-scorecard-scores
chorus-get-moments

03

Ground

It counts the conversations behind the scores and keeps scorecard types apart.

chorus-get-engagements

04

Report

You get strengths, development areas, what landed and one area to focus on.

Your AI tool

What PopdockAI provides

The tools behind the answer

Ready made tools for Chorus and one secure connection to your AI tool. No code, no exports.

get-data

chorus-get-users

Returns Chorus users, with their name, email, title, role, license type and whether the account is active. Use it for license reviews and for matching reps to their conversations.
get-data

chorus-get-scorecards

Returns Chorus scorecards, with the initiative, the person scored, the reviewer and when the scorecard was submitted. The starting point for any coaching or review coverage report.
get-data

chorus-get-scorecard-scores

Returns the individual question scores and reviewer comments that make up Chorus scorecards. Use it to find which questions a team scores worst on, not just the headline number.
get-data

chorus-get-moments

Returns coaching moments recorded in Chorus, with the initiative, the person coached, the reviewer and when the moment was submitted. Filter by person or period to see what coaching has actually happened.
get-data

chorus-get-engagements

Returns recorded calls and meetings from Chorus, with the subject, date, duration, participants and the account each conversation relates to. Filter by date range, owner or account to pull the conversations behind a deal or a period.
Read only.Nothing in Chorus Changes

This report is built from Get data tools only. PopdockAI applies the row, field and parameter rules your admin sets, and every query is written to the audit trail.

How Popdock AI secures your data

The outcome

Coaching that targets the real weak spot

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Related use cases

More ways to put this data to work

All Ramp use cases

One prompt template with infinite possibilities.

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