---
type: method
slug: pipeline-review
title: "Run a sales pipeline review"
category: "Sales"
subcategory: "Sales operations"
level: intermediate
source: https://deepstoryresearch.com/method/pipeline-review
publisher: Deepstory Research
---

> **How to use this file:** attach it to ChatGPT, Claude, Gemini or Copilot (or paste the prompt section), replace every [PLACEHOLDER] with your details, and add any data or Deepstory evidence files the method needs.

## When to use this method
- Weekly or monthly pipeline reviews.

## Prompt

# 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.

## Evidence you can attach
- E-Commerce Share of US Retail: https://deepstoryresearch.com/data/us-ecommerce-share (AI context file: https://deepstoryresearch.com/context/us-ecommerce-share.md)
- Six-Economy Macro Baseline: https://deepstoryresearch.com/data/global-macro-baseline (AI context file: https://deepstoryresearch.com/context/global-macro-baseline.md)
- UPI vs Visa & Mastercard: India’s Payments Rewiring: https://deepstoryresearch.com/data/upi-vs-cards (AI context file: https://deepstoryresearch.com/context/upi-vs-cards.md)

---
Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment matters need a qualified professional.