Run a Competitive Win and Loss Review

Revenue Operations, Sales Leader, Marketing, Enablement.

• You are deciding whether to change pricing, packaging, or positioning against one competitor.
• Different leaders tell different stories about why you lose to them.
• Before you fund a competitive programme, so you can measure whether it worked.

• Closed won and closed lost deals against the competitor for the last two quarters
• The stated reason per deal, ideally from the buyer rather than the rep
• Segment fields: size, industry, entry point, deal value

Best for one task. Copy it, add your information, and run it in your AI assistant.

You are a revenue analyst. Using only the closed deal data I paste below, find the pattern in our results against [COMPETITOR]. Produce: win rate against them overall and by segment, the reasons that appear in three or more losses, the reasons that appear in three or more wins, any segment where the pattern reverses, and what the data cannot answer. Count reasons, do not narrate individual deals. Tell me the sample size for every figure, and say plainly when a sample is too small to support a conclusion.

Deal data:
[paste closed won and closed lost records with reasons]

Best for repeatable work. The file includes the process, required inputs, decision rules, quality checks, and output format.

---
name: run-a-competitive-win-loss-review
description: A pattern read across deals against one competitor: why you win, why you lose, and the one change worth making.
license: MIT
metadata:
  author: The Revenue AI Report
  version: 1.0.0
  last-reviewed: 2026-09-22
  source: https://www.therevenueaireport.com/skills/run-a-competitive-win-loss-review
---

# Run a Competitive Win and Loss Review

A pattern read across deals against one competitor: why you win, why you lose, and the one change worth making.

## When to use this skill

- You are deciding whether to change pricing, packaging, or positioning against one competitor.
- Different leaders tell different stories about why you lose to them.
- Before you fund a competitive programme, so you can measure whether it worked.

## Inputs to collect

- Closed won and closed lost deals against the competitor for the last two quarters
- The stated reason per deal, ideally from the buyer rather than the rep
- Segment fields: size, industry, entry point, deal value

## Process

1. Pull every closed deal against the competitor for a fixed period. Do not cherry-pick.
2. Separate no-decision deals from competitive losses. They are different problems.
3. Code the stated reasons into a small set of themes, then count them.
4. Look for segments where the pattern reverses. That is usually where the decision is.
5. Pick one change, name the owner, and set the measure that says whether it worked.

## Decision rules

- A theme counts as a pattern at three or more deals. Below that it is an anecdote.
- Rep-stated loss reasons are a hypothesis. Buyer-stated reasons are evidence. Label which you have.
- If wins and losses share the same stated reason, the reason is not the cause. Keep digging.
- One change per review. A list of eight changes is a decision not made.

## Output requirements

- Win rate against the competitor, overall and by segment, with sample sizes.
- Counted loss and win reasons, with source labelled.
- One change, one owner, one measure, one date to check it.

## Quality checks

- Every percentage is shown with its sample size.
- No-decision deals are reported separately from competitive losses.
- The review states what the data cannot answer.

## Limitations

- CRM loss reasons record what the rep believed, which is often not what the buyer decided.
- Small samples produce confident nonsense. Say the sample is too small when it is.
- This tells you what already happened. It does not predict a competitor's next move.

## Example input

Pasted: 34 closed deals against one competitor over two quarters, with stated reason, segment, and value.

## Example output

Win rate 38 percent, 34 deals. Under 500 employees it is 52 percent, 21 deals. Over 500 it is 15 percent, 13 deals, and the stated reason in 7 of 11 losses is implementation support. Sample too small to split by industry. One change: named implementation plan in enterprise deals, owner and check date set.

## Review checklist

- Are buyer-stated and rep-stated reasons kept apart?
- Is every figure published with its sample size?
- Is there exactly one change with an owner and a date?

## Works with

- Playbook: Make conversation data do work (L3) (sales, L3) https://www.therevenueaireport.com/playbooks/conversation-data-to-work-l3
- Playbook: Put an answer layer on the warehouse you already paid for (L4) (revops, L4) https://www.therevenueaireport.com/playbooks/answer-layer-on-warehouse-l4
- Playbook: Point retrieval at the revenue corpus (L4) (enablement, L4) https://www.therevenueaireport.com/playbooks/point-retrieval-at-revenue-corpus-l4
- Tool: Gong (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/gong
- Tool: Perplexity Comet (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/perplexity-comet
- Tool: AlphaSense (Data & Analytics) https://www.therevenueaireport.com/tools/alphasense
- Tool: InsightSquared (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/insightsquared
- Tool: Mediafly (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/mediafly

## Rules of conduct

- Write for a Director, VP, or operator. Short sentences. Explain uncommon terms.
- Separate facts from assumptions. Never hide uncertainty.
- Do not invent numbers, benchmarks, quotes, or customer names.
- Do not send messages, change CRM records, or publish anything unless the user explicitly asks.
- Flag when a decision needs human review.

## Evidence

This skill is grounded in The Revenue AI Report research:
- https://www.therevenueaireport.com/research/proof-gap
- https://www.therevenueaireport.com/research/spend-vs-attribution





Source and updates: https://www.therevenueaireport.com/skills/run-a-competitive-win-loss-review

1. 1.Pull every closed deal against the competitor for a fixed period. Do not cherry-pick.
2. 2.Separate no-decision deals from competitive losses. They are different problems.
3. 3.Code the stated reasons into a small set of themes, then count them.
4. 4.Look for segments where the pattern reverses. That is usually where the decision is.
5. 5.Pick one change, name the owner, and set the measure that says whether it worked.

• A theme counts as a pattern at three or more deals. Below that it is an anecdote.
• Rep-stated loss reasons are a hypothesis. Buyer-stated reasons are evidence. Label which you have.
• If wins and losses share the same stated reason, the reason is not the cause. Keep digging.
• One change per review. A list of eight changes is a decision not made.

• Win rate against the competitor, overall and by segment, with sample sizes.
• Counted loss and win reasons, with source labelled.
• One change, one owner, one measure, one date to check it.

Pasted: 34 closed deals against one competitor over two quarters, with stated reason, segment, and value.

Win rate 38 percent, 34 deals. Under 500 employees it is 52 percent, 21 deals. Over 500 it is 15 percent, 13 deals, and the stated reason in 7 of 11 losses is implementation support. Sample too small to split by industry. One change: named implementation plan in enterprise deals, owner and check date set.

• Are buyer-stated and rep-stated reasons kept apart?
• Is every figure published with its sample size?
• Is there exactly one change with an owner and a date?

What does the Run a Competitive Win and Loss Review skill do?

A pattern read across deals against one competitor: why you win, why you lose, and the one change worth making.

Who is the Run a Competitive Win and Loss Review skill for?

Revenue Operations, Sales Leader, Marketing, Enablement. It sits at the advanced level and takes about two hours.

What do I need before I start?

Collect these first: Closed won and closed lost deals against the competitor for the last two quarters; The stated reason per deal, ideally from the buyer rather than the rep; Segment fields: size, industry, entry point, deal value.

What is the difference between the quick prompt and the SKILL.md file?

The quick prompt is for one task. Copy it, add your information, run it. The SKILL.md file is for repeatable work: it carries the process, required inputs, decision rules, quality checks, and output format so an AI assistant runs the same way every time.

What should I check before trusting the output?

Are buyer-stated and rep-stated reasons kept apart? Is every figure published with its sample size? Is there exactly one change with an owner and a date?

Is it free to use?

Yes. Every skill on The Revenue AI Report is free and published under the MIT license. Attribution is welcome, not required.

• CRM loss reasons record what the rep believed, which is often not what the buyer decided.
• Small samples produce confident nonsense. Say the sample is too small when it is.
• This tells you what already happened. It does not predict a competitor's next move.

Run the skill, then roll it out with a playbook. Vendor links are supporting context, not a recommendation.

• Playbook: Make conversation data do work (L3) (sales, L3 L3 Integrated)
• Playbook: Put an answer layer on the warehouse you already paid for (L4) (revops, L4 L4 Orchestrated)
• Playbook: Point retrieval at the revenue corpus (L4) (enablement, L4 L4 Orchestrated)
• Tool: Gong (Sales & Revenue Intelligence)
• Tool: Perplexity Comet (Sales & Revenue Intelligence)
• Tool: AlphaSense (Data & Analytics)
• Tool: InsightSquared (Sales & Revenue Intelligence)
• Tool: Mediafly (Sales & Revenue Intelligence)

• Research: proof gap
• Research: spend vs attribution