kenny589/gtm-flywheel/deal-patterns
Claude Code and agent skill by kenny589/gtm-flywheel
- community
Checking that the SKILL.md file is still in the repository.
Analyze closed-won and closed-lost deals to identify the patterns that predict revenue. Surface what your best deals have in common, why deals die, and how to replicate your wins systematically.
Install
npx vskill@latest install kenny589/gtm-flywheel/deal-patternsSKILL.md
Deal Pattern Analysis
When to Use
- Quarterly business reviews when analyzing sales performance
- Refining your ICP based on which deals actually close (vs. which you thought would close)
- Building predictive models for deal scoring and pipeline forecasting
- Training new sales hires on what "good" looks like at your company
- Diagnosing a pipeline problem: "We have meetings but can't close"
Framework
The Deal Pattern Hypothesis
Every company has hidden patterns in their deal data. The companies that find and exploit these patterns close 2-3x more efficiently than those that don't.
The core question: What do your best deals have in common that your worst deals don't?
If you can answer that question with data, you can:
- Target more prospects that look like your best deals
- Disqualify prospects that look like your worst deals
- Optimize your sales process around what actually drives closes
- Forecast revenue more accurately based on deal characteristics
The Deal Anatomy: 12 Variables
For every closed deal (won or lost), capture these 12 variables:
Deal Profile Variables
| Variable | What to Capture | Why It Matters | |
|---|---|---|---|
| 1. Deal size | Final contract value | Revenue per deal affects everything downstream | |
| 2. Sales cycle length | Days from first touch to close | Longer cycles = higher cost to serve | |
| 3. Source channel | Outbound, inbound, referral, event, partner | Which channels produce the best deals | |
| 4. Number of touches | Emails, calls, meetings before close | Effort required per deal | |
| 5. Decision committee size | How many people were involved | More stakeholders = slower, riskier deals |
Account Profile Variables
| Variable | What to Capture | Why It Matters | |
|---|---|---|---|
| 6. Company size | Employee count at time of deal | Which company sizes close best | |
| 7. Industry | Vertical/sector | Which industries convert best | |
| 8. Growth stage | Seed, Series A/B/C, PE-backed, public | Stage affects urgency and budget | |
| 9. Tech stack | Relevant tools they were using | Tech affinity patterns | |
| 10. Trigger event | What triggered the evaluation | Which events produce deals |
Stakeholder Variables
| Variable | What to Capture | Why It Matters | |
|---|---|---|---|
| 11. Champion title | Who internally pushed for the deal | Which personas champion most effectively | |
| 12. Economic buyer title | Who signed the check | Which titles have budget authority |
Analysis 1: Win Pattern Analysis
Pull your last 20-50 closed-won deals and analyze:
Firmographic Patterns
Source Channel Analysis
Trigger Event Analysis
Champion Analysis
Analysis 2: Loss Pattern Analysis
Equally important — analyze your last 20-50 closed-lost deals:
Loss Reason Distribution
Preventable Losses
The most actionable analysis: which losses could have been prevented with better targeting, qualification, or sales process?
| Loss Category | Prevention Strategy | |
|---|---|---|
| Lost to competitor | Improve competitive positioning. Build battlecards. Address differentiators earlier. | |
| No decision (stuck) | Better qualification upfront. Identify urgency drivers earlier. Set mutual action plans. | |
| Budget rejected | Validate budget authority earlier. Build ROI models. Multi-thread (reach the economic buyer). | |
| Champion left | Multi-thread from day one. Never rely on a single contact. | |
| Bad fit discovered late | Tighten qualification criteria. Disqualify faster. |
Analysis 3: Speed-to-Close Patterns
Which deal characteristics correlate with faster closes?
Analysis 4: Deal Size Patterns
What predicts larger deal sizes?
Analysis 5: The Ideal Deal Profile
Combine all analyses into a single "ideal deal" composite:
Feeding Deal Patterns Back into Outbound
The ultimate purpose of deal pattern analysis is to improve your targeting and messaging:
| Pattern Insight | Outbound Action | |
|---|---|---|
| Best deals come from Series B companies | Prioritize Series B in your ICP matrix | |
| Deals triggered by "new VP Sales" close 2x faster | Build a trigger campaign for leadership changes | |
| VP Sales as champion has highest win rate | Target VP Sales as primary persona | |
| Deals with 4+ stakeholders take 3x longer | Qualify for committee size early, multi-thread from day one | |
| Referral deals close 40% faster | Build a referral ask into your sequence Step 4 | |
| Lost deals often cite competitor X | Address competitor X positioning in Step 2-3 |
Analysis Cadence
| Frequency | Analysis | Minimum Sample | |
|---|---|---|---|
| Monthly | Quick review of new wins and losses | All deals that closed this month | |
| Quarterly | Full deal pattern analysis (all 5 analyses) | Last 50+ closed deals | |
| Annually | Trend analysis: how are deal patterns shifting? | Full year of data | |
| Ad hoc | Specific investigation (e.g., "why are enterprise deals stalling?") | Relevant subset |
Templates
Deal Pattern Report
Tips
- You need at least 30 closed deals (combined wins and losses) for patterns to be statistically meaningful. Below that, treat findings as hypotheses, not conclusions.
- The most common mistake: only analyzing wins. Your losses contain just as much — often more — actionable intelligence. The pattern of WHY deals die is the pattern of what to fix.
- Don't confuse correlation with causation. If your biggest deals come from referrals, it might be because referrals come pre-qualified — not because the referral channel itself is magic. Test before restructuring.
- Share deal pattern insights with everyone, not just sales. Marketing needs to know which triggers to target. Product needs to know which features close deals. Leadership needs to know which segments to invest in.
- The ideal deal profile should directly inform your ICP matrix, your trigger campaigns, your persona development, and your lead prioritization. If your deal patterns say one thing and your targeting says another, one of them is wrong.
Progressive disclosure: load CRM-specific data extraction queries and advanced statistical analysis frameworks only when running deal pattern analysis on a specific dataset.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingsales-intelligence/deal-patterns/SKILL.md
Popularity
- Repository stars
- 48
- Repository forks
- 23
- 7-day trend score
- 19
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 10 May 2026, 6 months ago.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/kenny589/gtm-flywheel/deal-patterns)