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Lost-deal review: the patterns behind the deals and customers you lost

The team explains every lost deal with price. Review the actual records to see what patterns sit behind the losses.

The prompt — copy and run it

You are a commercial strategy analyst reviewing lost deals and churned customers for an executive team. I will paste anonymized deal notes, loss reasons recorded by sales, and any customer interview excerpts.

Produce:

A) STATED VS OBSERVED — the loss reasons sales recorded, and the reasons suggested by the notes and interviews, side by side. Flag where they disagree.

B) PATTERNS — the 3-5 recurring loss patterns, each with the number of deals it appears in, the deal IDs, and a representative excerpt.

C) SEGMENT VIEW — whether losses cluster by customer size, industry, region, deal source, or competitor type, based only on fields I provided.

D) CONTROLLABLE CAUSES — which patterns the company can address through product, pricing, sales process, or positioning, and which reflect customers we should not target.

E) NEXT STEPS — the 3 changes worth testing and how we will know if they helped.

Inputs: [PASTE ANONYMIZED DEAL RECORDS: ID, SEGMENT, STAGE LOST, RECORDED REASON, NOTES] · [CUSTOMER INTERVIEW EXCERPTS IF ANY] · [PERIOD COVERED]

Rules: Do not invent deals, reasons, or counts — every pattern must trace to deal IDs I pasted, and small samples are labeled "thin". Verify counts against the CRM before sharing. Strip customer names, contact details, and confidential terms before pasting; use only public information about competitors.

Why this prompt works

Recorded loss reasons are often the easiest explanation rather than the true one, and price is the default. Comparing stated reasons with what the notes and interviews show, counting patterns by deal ID, and checking for segment clusters reveals which losses are fixable and which come from targeting the wrong customers. Turning patterns into testable changes makes the review lead to action.

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