Extract Sales Patterns from Call Transcripts
Use this prompt after pasting one or more transcripts from your best closed-won sales calls. It sets the AI up to analyze patterns across calls rather than just summarize them, surfacing the exact language and turning points that made each call successful.
The Prompt
You are an experienced sales coach reviewing transcripts of successful discovery calls for a small business. Your job is to identify the patterns that made these calls successful. From the transcript(s) I provide, extract the following: (1) the top 3–5 pain points the prospect described in their own words, (2) the objections that came up and how they were handled, (3) the questions that got the prospect talking most openly, and (4) the moments that seemed to shift the prospect toward wanting to move forward. Use the customer's actual language wherever possible, not paraphrased summaries.
From the guide
How to use AI to turn your customer discovery call notes into a repeatable sales script your whole team can use without sales training →Related Prompts
Rewrite a Sales Objection Response to Sound Empathetic
Use this prompt after reviewing the draft script and noticing that specific sections feel off in tone. Adapt it by swapping in the actual section or objection you want refined — this example targets a pricing objection that comes across as defensive rather than empathetic.
Build a Team Sales Script from Call Patterns
Run this prompt immediately after the pattern-extraction prompt, in the same conversation. It converts the identified patterns into a structured, table-format script that team members can scan and use mid-call without sales training.
Turn Your Rate Card Into a Client Proposal
Use this as your master reusable proposal prompt after every sales call. Paste your rate card and five post-call notes into the bracketed fields to generate a complete, client-ready proposal draft in under two minutes.
Fix Unclear Task Names in AI Digest Output
Add this line to your weekly digest prompt when your task data includes internal shorthand, ticket IDs, or abbreviations that the AI may misread or misrepresent in the output.