cotera

G2 Review Win/Loss Analyzer

Stop guessing why you win and lose deals. Let G2 reviews tell you what your customers actually think.

Win/loss analysisChurn preventionSegment researchProduct feedback

The Challenge

Traditional win/loss analysis requires expensive interviews and months of data collection. Meanwhile, your G2 page has hundreds of reviews where customers explain exactly why they chose you or why they're unhappy. The signal is there, but extracting patterns from unstructured reviews at scale is nearly impossible manually.

What This Prompt Does

Segment Reviews

Break down reviews by company size, role, and rating

Find Win Patterns

Identify the top reasons customers choose your product

Find Loss Patterns

Uncover why customers leave or rate you poorly

Post to Slack

Share actionable insights with your team automatically

The Prompt

The Prompt

Task

Use @G2/Get ReviewsName it "G2/Get Reviews" and call it with @G2/Get Reviews to pull reviews for your product, then segment the data to understand win/loss patterns. Cross-reference with @Salesforce/Query RecordsName it "Salesforce/Query Records" and call it with @Salesforce/Query Records to match review insights against your pipeline. Post a weekly digest to @Slack/Send MessageName it "Slack/Send Message" and call it with @Slack/Send Message.

Example: Analyze the last 500 G2 reviews for Datadog to understand why enterprise customers rate it highly but mid-market companies give more mixed feedback.

Input

The user will provide a product name and optionally a focus area (company size segment, specific competitor comparison, or time range).

Example: "Analyze win/loss patterns for Figma" or "Why are enterprise users rating us lower than SMB on G2?"

Context

What to Analyze

Win signals (4-5 star reviews):

  • What specific features drove the positive review?
  • What was the reviewer's company size and role?
  • Were they switching from a competitor? Which one?
  • What use case are they solving?

Loss signals (1-3 star reviews):

  • What triggered the negative review?
  • Is it a product issue, support issue, or pricing issue?
  • What company size and role is most likely to churn?
  • Are they mentioning a specific competitor as an alternative?

Analysis Strategy

  1. Pull G2 reviews for your product (500+ for statistical relevance)
  2. Segment reviews by star rating: Wins (4-5 stars) vs Losses (1-3 stars)
  3. Within each segment, categorize by company size and reviewer role
  4. Extract competitor mentions from both positive and negative reviews
  5. Identify the top 3 reasons customers stay and the top 3 reasons they leave
  6. If Salesforce is connected, pull closed-won and closed-lost deals to compare patterns

What Counts as a Valid Result

  • Minimum 50 reviews per segment to draw conclusions
  • Always include sample size with percentages
  • Separate product complaints from support/pricing complaints
  • Note any seasonal patterns in review sentiment
  • Flag if review volume has changed significantly (possible review campaign)

Output

Win/Loss Summary for [Product]

Sample: X total reviews analyzed (Y wins, Z losses)

Why Customers Choose Us:

  1. Reason (X% of positive reviews mention this) - Representative quote
  2. Reason (X% of positive reviews mention this) - Representative quote
  3. Reason (X% of positive reviews mention this) - Representative quote

Why Customers Leave:

  1. Reason (X% of negative reviews mention this) - Representative quote
  2. Reason (X% of negative reviews mention this) - Representative quote
  3. Reason (X% of negative reviews mention this) - Representative quote

Segment Breakdown: | Company Size | Avg Rating | Top Win Reason | Top Loss Reason | |-------------|-----------|----------------|-----------------| | Enterprise | X.X | ... | ... | | Mid-Market | X.X | ... | ... | | SMB | X.X | ... | ... |

Competitor Mentions:

  • [Competitor 1]: Mentioned in X reviews. Customers switching FROM them cite [reason]. Customers switching TO them cite [reason].

Action Items: 3 specific recommendations based on the data.

Example Usage

Try asking:

  • →"Why are enterprise customers rating us lower on G2 than SMBs?"
  • →"Analyze win/loss patterns for Datadog from their G2 reviews"
  • →"What are the top 3 reasons people leave Intercom based on G2?"