G2 Review Win/Loss Analyzer

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.