Run a sales pipeline review
A deal-by-deal and stage-level review with risks, stuck deals, coverage and actions.
This is a practical, copy-paste process for weekly or monthly pipeline reviews.. Use it when you want a clear structure, then add your own details and the linked evidence.
Published by Deepstory Research. Reviewed Sep 2026.
How to use this method
- 1. Copy or downloadCopy the prompt below, or download the method file and attach it to your chat.
- 2. Paste into any AIChatGPT, Claude, Gemini or Copilot. Replace each [PLACEHOLDER] with your details, or let the AI ask you.
- 3. Add evidenceAttach the evidence files listed on this page so the AI works from sourced numbers, not guesses.
The prompt
Paste it as-is. The AI will ask for anything still in [BRACKETS].
# Run a sales pipeline review Method by Deepstory Research: deepstoryresearch.com/method/pipeline-review You are a sales director running the weekly pipeline meeting. Follow the method below step by step. Goal: A deal-by-deal and stage-level review with risks, stuck deals, coverage and actions. ## My inputs - Pipeline export: [PIPELINE_EXPORT] (example: attach CSV) - Quota and period: [QUOTA_AND_PERIOD] (example: $400k this quarter) Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source. ## Steps to follow 1. Compute coverage vs quota and stage conversion. 2. Flag deals with stale activity, slipping dates or missing next steps. 3. Assess top deals against qualification. 4. Recommend actions per risky deal. ## Deliver the output in this structure - Coverage summary - Risk flags table - Top-deal assessment - Actions ## Quality rules - Base risks on data fields, and say which fields were missing. - Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION. - If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED]. - End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.
What you need to give the AI
| Input | Placeholder | Example |
|---|---|---|
| Pipeline export | [PIPELINE_EXPORT] | attach CSV |
| Quota and period | [QUOTA_AND_PERIOD] | $400k this quarter |
Use it when
- Weekly or monthly pipeline reviews.
What you get
- Coverage summary
- Risk flags table
- Top-deal assessment
- Actions
The procedure, step by step
Read this before you run it, so you can check the AI actually followed the method.
Compute coverage vs quota and stage conversion.
Flag deals with stale activity, slipping dates or missing next steps.
Assess top deals against qualification.
Recommend actions per risky deal.
Quality checks built into the prompt
- • Base risks on data fields, and say which fields were missing.
- • Every number labelled FACT, ESTIMATE or ASSUMPTION; claims outside attached evidence tagged [UNVERIFIED].
Evidence to use with this method
Methods tell the AI how to think. Evidence gives it sourced numbers to think with. Download a context file and attach it with the prompt.
E-Commerce Share of US Retail
Online sales as a share of total US retail, and the growth gap that keeps pushing that share up.
Six-Economy Macro Baseline
A common-country, common-period baseline for real growth, inflation, modeled unemployment and trade exposure across the United States, China, India, Germany, the United Kingdom and Japan.
UPI vs Visa & Mastercard: India’s Payments Rewiring
Ten years of UPI measured against the US card networks, against cash, and against the merchant-discount-rate question reopened by the Taxation and Other Laws (Amendment) Bill, 2026.
Run next
Methods that chain well after this one.
Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment decisions need a qualified professional.