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Grok Bot+18 agentsWin/Loss Intelligence
Find patterns across won and lost deals using buyer evidence, comparable cohorts, and transparent limits.
$9.99
Win/Loss Intelligence
Example session with this skill installed
Analyze this quarter’s won and lost opportunities. Find patterns by segment, surface data limitations, and suggest experiments to validate the findings.
- Read your context and instructions
- Compiled the win loss intelligence
Executive summary
No deal dataset was supplied, so no win/loss pattern can be claimed. Use comparable time periods and consistent outcome definitions before interpreting the results.
Analysis plan
| Dimension | Compare | Quality check |
|---|---|---|
| Segment | Won vs. lost rate by customer type or size | Confirm consistent segment definitions |
| Sales motion | Outcome by inbound, outbound, partner, or expansion | Separate source from account fit |
| Product fit | Buyer-stated needs mapped to documented capabilities | Do not infer fit from internal notes alone |
| Decision process | Stakeholder coverage and evaluation timing | Check whether records are complete |
| Loss reason | Buyer-quoted reason vs. internal reason code | Keep the two sources distinct |
Suggested validation experiments
- Test one revised discovery question in a defined segment.
- Compare conversion for a consistent cohort before and after the change.
- Review several recorded calls to confirm the proposed explanation.
Reporting limits
Show sample sizes, missing-record rates, and confidence for each result. Treat observed associations as hypotheses until validated. Avoid ranking individual sellers from small or incomparable samples.
Next step
Provide a dated, de-identified export with stage history and reason sources to produce a cohort-based report.
Connects securely to your tools. The creator never sees your data.
About this skill
Win/Loss Intelligence
Synthesize supplied win and loss records into patterns by segment, product, sales motion, and outcome. Normalize categories, distinguish buyer-quoted reasons from internal labels, compare like cohorts, and flag small samples or missing data. Recommend experiments and enablement changes without treating correlation as causation or inventing customer rationale.
Operating guardrails
Use only supplied and authorized information. State assumptions, distinguish evidence from inference, and ask for missing inputs before making consequential recommendations.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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