Win Loss

Win-Loss Analysis

Perform rigorous, data-driven win-loss analysis by pulling from HubSpot, Fireflies, and Apollo, then cross-referencing everything before drawing conclusions.

Execution Steps

Step 1: Determine Scope

Ask the user (or infer from their request):

• Single deal analysis — "why did we lose Action Seating" or "analyze the [Company] deal"
• Portfolio analysis — "win-loss analysis", "analyze our pipeline", "what patterns do you see"

Step 2: Pull All Data

Regardless of scope, gather everything first. Do NOT skip any source.

2a. HubSpot — Deal Data

Use the HubSpot MCP tools to pull:

1. All deals — search for deals across all stages (won, lost, open)
   • Properties to fetch: dealname, amount, dealstage, closedate, createdate, pipeline, hs_lastmodifieddate, hs_deal_stage_probability, closed_lost_reason, closed_won_reason, notes_last_updated, num_notes, num_associated_contacts, hs_analytics_source
2. Associated contacts for each deal — get contact names, emails, titles, company
3. Associated companies for each deal — get company name, domain, industry, size
4. Deal notes and activity — get notes, emails, calls, meetings logged against each deal
5. Stage history — track how long each deal spent in each stage

2b. Fireflies — Conversation Intelligence

Use the Fireflies MCP tools:

1. Search transcripts by contact email addresses from the deals
2. Get full transcripts for every meeting associated with deal contacts
3. Get summaries for quick scanning
4. Extract from each transcript:
   • Direct quotes about pain points
   • Objections raised (pricing, timing, competition, internal resistance)
   • Competitor names mentioned
   • Pricing discussions (what was quoted, reactions)
   • Buying signals ("when can we start", "who else needs to approve")
   • Decision-maker identification
   • Next steps discussed vs. what actually happened

2c. Apollo — Company Enrichment

Use the Apollo MCP tools:

1. Enrich each company associated with deals:
   • Company size (employees)
   • Industry and sub-industry
   • Annual revenue
   • Funding stage and total raised
   • Technologies used
   • Location
2. Enrich key contacts — titles, seniority, department
3. Use this to build ICP pattern analysis

Step 3: Internal Fact-Check Layer

Before any analysis, run these integrity checks. Flag every issue found.

Data Quality Checks

Cross-Reference Checks

• If HubSpot says "closed lost — went with competitor", search Fireflies for which competitor and why
• If HubSpot says "closed lost — pricing", check Fireflies for what price was quoted and what their reaction was
• If a deal has no close reason in HubSpot, attempt to find the reason in Fireflies transcripts
• Compare deal timeline in HubSpot stages vs. actual meeting cadence in Fireflies

Step 4A: Single Deal Analysis

When analyzing a specific deal, produce this report:

## Win-Loss: [Deal Name]

### Deal Snapshot
| Field | Value |
|-------|-------|
| Company | [name] |
| Amount | $[amount] |
| Stage | [stage] |
| Created | [date] |
| Closed | [date] |
| Days in Pipeline | [n] |
| Outcome | Won / Lost / Open |
| Loss Reason (HubSpot) | [reason or "not recorded"] |

### Company Profile (Apollo)
- Industry: [industry]
- Size: [employees]
- Revenue: [revenue]
- Funding: [stage, amount]
- ICP Fit: Strong / Moderate / Weak — [why]

### Timeline
Map every touchpoint chronologically:
- [Date] — First contact (source: [how they came in])
- [Date] — Discovery call (Fireflies transcript available: yes/no)
- [Date] — Stage moved to [stage]
- [Date] — Demo / proposal sent
- [Date] — Last activity
- [Date] — Outcome

### What They Said (from Fireflies transcripts)
Pull DIRECT QUOTES. Do not paraphrase. Organize by theme:

**Pain Points:**
> "[exact quote]" — [Contact Name], [Date]

**Objections:**
> "[exact quote]" — [Contact Name], [Date]

**Competitor Mentions:**
> "[exact quote]" — [Contact Name], [Date]

**Buying Signals (if any):**
> "[exact quote]" — [Contact Name], [Date]

**Pricing Reactions:**
> "[exact quote]" — [Contact Name], [Date]

### Pricing Analysis
- Amount in HubSpot: $[amount]
- What was discussed in calls: [details from Fireflies]
- Match: Yes / No — [explain discrepancy]
- Price sensitivity level: Low / Medium / High

### Root Cause Analysis
Go beyond the surface. "Went dark" is not a root cause. Dig into:
1. **Primary cause** — the single biggest factor
2. **Contributing factors** — secondary issues that compounded
3. **Process failures** — what we could have done differently
4. **Timing factors** — was the timing right for them?

### Data Quality Issues
Flag anything found in the fact-check layer for this deal.

### Lessons
- **Repeat:** [what worked or would work again]
- **Change:** [what to do differently next time]
- **Watch for:** [early warning signs we missed]

Step 4B: Portfolio Analysis

When analyzing all deals, produce this report:

## Win-Loss Portfolio Analysis
*Analysis Date: [today's date]*
*Deals Analyzed: [n won] + [n lost] + [n open] = [total]*

### Pipeline Metrics
| Metric | Value |
|--------|-------|
| Total Deals | [n] |
| Open Deals | [n] |
| Won | [n] |
| Lost | [n] |
| Win Rate | [%] |
| Avg Deal Size (Won) | $[amount] |
| Avg Deal Size (Lost) | $[amount] |
| Avg Sales Cycle (Won) | [days] |
| Avg Sales Cycle (Lost) | [days] |
| Pipeline Value (Open) | $[amount] |
| Weighted Pipeline | $[amount] |

### Stage Funnel
Show deals by stage with average days in stage and conversion rate to next stage:
| Stage | Deals | Avg Days | Conversion to Next |
|-------|-------|----------|-------------------|
| [stage] | [n] | [days] | [%] |

### Patterns in Wins
Analyze won deals for common traits:

**ICP Profile of Winning Deals:**
- Company size range: [range]
- Industries: [list]
- Common pain points: [list with frequency]
- Decision-maker titles: [list]
- Lead source: [breakdown]

**What Worked:**
- Messaging that resonated (with transcript evidence)
- Objections we overcame and how
- Average touchpoints to close

### Patterns in Losses
Analyze lost deals for common traits:

**Why We Lose:**
| Reason | Count | % of Losses |
|--------|-------|-------------|
| [reason] | [n] | [%] |

**Where Deals Stall:**
- Stage with highest drop-off: [stage]
- Average days before going dark: [days]

**Competitor Displacement:**
| Competitor | Times Mentioned | Deals Lost To | Key Differentiator They Cited |
|-----------|----------------|---------------|-------------------------------|
| [name] | [n] | [n] | [what prospects said] |

### Zombie Deals (Immediate Action Required)
Deals that need attention right now:

| Deal | Issue | Amount | Last Activity | Recommended Action |
|------|-------|--------|---------------|--------------------|
| [name] | Stale close date | $[x] | [date] | [action] |
| [name] | Closed lost but still active | $[x] | [date] | [action] |
| [name] | No activity 60+ days | $[x] | [date] | [action] |

### Data Quality Report
| Issue | Count | Deals Affected |
|-------|-------|----------------|
| Missing close reason | [n] | [list] |
| No associated contacts | [n] | [list] |
| No notes or activity | [n] | [list] |
| Amount = $0 or blank | [n] | [list] |

### Recommendations

**ICP Refinement:**
Based on win patterns, the ideal customer profile should be refined to:
- [specific, data-backed recommendations]

**Process Improvements:**
- [specific changes to sales process with evidence]

**Pricing Adjustments:**
- [data-backed pricing observations]

**Pipeline Hygiene:**
- [specific cleanup actions needed]

**Follow-Up List:**
Deals worth re-engaging and why:
| Deal | Why Re-engage | Suggested Approach |
|------|---------------|--------------------|
| [name] | [reason from transcript/activity] | [approach] |

Rules

1. Never guess. If data is missing, say so. Do not fill in blanks with assumptions.
2. Use direct quotes. When citing what prospects said, pull exact quotes from Fireflies transcripts. Paraphrasing loses the signal.
3. Cross-reference everything. HubSpot data alone is unreliable. Always verify against Fireflies and Apollo.
4. Flag data quality issues prominently. Bad data leads to bad decisions. Surface every inconsistency.
5. "Went dark" is not a root cause. Dig deeper. Why did they go dark? What was the last thing discussed? Was there a trigger?
6. Separate facts from interpretation. Present the data first, then your analysis. Make it clear which is which.
7. Be blunt. This is an internal tool. Do not soften bad news. If the pipeline is fiction, say so.
8. Include the "so what." Every insight should connect to a specific action.
9. Time-box the data pull. If a data source is unavailable or returns errors, note it and continue with what you have. Do not block the entire analysis on one failed API call.
10. Save the output. Write the final report to ~/Documents/Claude/Agents/reports/win-loss-[date].md so it is preserved.

Related Skills

• founder-sales
• sales-qualification
• enterprise-sales
• pricing-strategy
• pricing-coach