Win/loss analysis

Analyze sales call transcripts to extract actionable insights on why deals are won, lost, retained, or churned. Cross-reference findings with ICP, firmographics, and competitive context to produce strategic recommendations.

---

Claude Code triggers

Invoke when user says:

• "Win/loss analysis"
• "Analyze sales calls"
• "Why did we win/lose"
• "Churn analysis"
• "Retention analysis"
• "Sales call insights"
• "Deal outcome patterns"
• "Customer feedback synthesis"
• "Analyze these transcripts"
• "What patterns in our sales calls"

Do NOT invoke when:

• User wants general transcript analysis → use transcript-analysis
• User wants competitor research → use competitor-research
• User wants single customer interview analysis → use transcript-analysis
• User wants sales enablement assets → use sales-enablement

---

Input requirements

Required

Input	Description	Source
Transcripts	Sales call transcripts with customer name and outcome	User provides
Outcome	Win/Loss/Retention/Churn for each call	User specifies or infer

Optional (improve quality)

Input	How it helps
Website URL per customer	Firmographics cross-reference
Product/ICP document	Define in-scope product capabilities
Market/GTM document	Positioning and competitive landscape
Sales notes column	Additional context (stage, deal size)
Competitor names	Pre-identify competitors to watch for

Validation

Before proceeding: at least one transcript provided; outcome known or inferable from transcript; customer name identifiable.

If inputs are missing: ask the user for transcripts. Clarify if outcome should be inferred from transcript signals.

Transcript intake — normalize any recorder format

Transcripts arrive in many shapes: Gong, Fireflies, Otter, Grain exports, Zoom/Avoma VTT, SRT, recorder JSON, or plain pasted text. Before Phase 1, normalize whatever you're handed into one shape — speaker-attributed turns, timestamps where present. See the premium reference for the sniff-and-parse table per format and the normalized target shape. /transcripts inherits the same reference.

Two rules apply to every transcript before analysis:

• Redact PII first — .claude/rules/pii-redaction.md. Mask end-client names, emails, account numbers before processing; keep roles, company, deal context. (Load-bearing for ClientCo and any regulated client.)
• Bind every claim to evidence — .claude/rules/evidence-bound-outputs.md. Every extracted pattern cites a verbatim quote + speaker; normalized turns make speaker attribution reliable.

---

Process

The analysis runs in 3 phases. Read the premium reference for the full step-by-step (4 transcript-processing steps, 4 aggregation steps, 4 synthesis steps, plus per-phase checkpoints and the process flowchart).

Phase summary:

1. Transcript processing — classify outcome, identify speakers, extract customer context, pull verbatim quotes for the 6 dimensions
2. Pattern aggregation — group by outcome, count frequency, rank patterns (3+ mentions), cross-reference by ICP/competitor/persona
3. Insight synthesis — state pattern, provide evidence with frequency + confidence, identify opportunity, generate executive summary
4. Canonical-doc drift → propose diff (optional — active client, cadenced batch only) — check recurring patterns against the client's current positioning/messaging docs and emit gated diff proposals. See the premium reference.

Cadence

For an active client, run the batch analysis on a monthly cadence — the review cycle that keeps positioning/messaging current between quarterly refreshes. Phase 4 rides that same monthly run; it adds no separate schedule. Single-call and ad-hoc runs skip Phase 4.

Phase 4 — when it runs

Phase 4 fires only on a cadenced batch for an active client with canonical docs (projects/consulting/active/{client}/positioning/ or messaging/). It proposes diffs; it never applies them — the merge is a separate, two-layer human gate (Genesys PMM, then client signoff). Win-loss stays additive; positioning/messaging stay human-locked. Skip Phase 4 silently for single-call runs, comparison-only runs, prospects, or clients with no canonical docs. Full protocol, eligibility (recurrence gate), voice gate, and merge bridge: the premium reference.

---

Core frameworks

Analysis modes

Mode	When to use	Output
Single call	Deep analysis of one transcript	Full insight extraction per dimension
Batch analysis	Multiple transcripts (3-20 calls)	Aggregated patterns with frequency counts
Comparison matrix	Win vs. loss OR retention vs. churn	Side-by-side pattern comparison

Default to batch analysis mode when multiple transcripts are provided.

6 analysis dimensions

#	Dimension	Win signals	Loss signals
1	Product	"Exactly what we need," feature praised	"Missing [feature]," "Doesn't do [X]"
2	Messaging	"Now I understand why this matters"	"What does it actually do?"
3	GTM/Sales	"You really understand our problem"	"Demo didn't address our needs"
4	Pricing	"Fair price," "good value"	"Too expensive," "over budget"
5	Competition	"Chose you over [competitor]"	"Going with [competitor]"
6	Customer context	"Need this now," deadline-driven	"No rush," "maybe next year"

Confidence scoring

Level	Definition	When to apply
High	3+ calls with consistent pattern	Clear recurring theme
Medium	2 calls or inferred from strong signals	Emerging pattern
Low	Single mention or indirect reference	Possible outlier

Outcome classification

Outcome	Definition	Key signals
Win	Deal closed, contract signed	"We're moving forward," pricing confirmed
Loss	Deal lost to competitor or no-decision	"Going with [competitor]," "Not right now"
Retention	Existing customer renewing/expanding	Renewal discussion, expansion
Churn	Existing customer leaving/reducing	Cancellation, "not getting value"

---

Anti-hallucination guardrails

1. Quote verbatim. All insights must trace to specific transcript quotes.
2. Never invent patterns. If a pattern appears in only one call, label it "Single mention — pattern unconfirmed."
3. State frequency. Always note how many calls support each finding (e.g., "4 of 7 calls").
4. Acknowledge gaps. If a dimension has no data, mark "Not discussed in transcripts."
5. Distinguish roles. Tag who said what — prospect vs. sales rep vs. champion.

---

Gotchas

• Correlation as causation. Reports "deals with longer sales cycles were lost" as if cycle length caused the loss → always distinguish patterns from causes. Use "associated with" not "caused by".
• Small sample bias. Draws conclusions from 2-3 deals instead of waiting for sufficient data → flag sample size prominently. Minimum 5 wins and 5 losses for reliable patterns.
• Missing verbatim quotes. Summarizes what buyers said instead of extracting exact quotes → verbatim quotes are the primary deliverable. Summaries are secondary.
• Single-dimension analysis. Only looks at win/loss by competitor, missing dimensions like deal size, ICP segment, or sales cycle stage → cross-tabulate across at least 3 dimensions.
• Conflates product feedback with sales insights. Mixes "they wanted feature X" with "they didn't trust our team" → separate product gaps from sales execution issues. They feed into different downstream skills.

---

Integration with other skills

Skill	Relationship	Usage
transcript-analysis	Related	Use for general transcripts, not sales calls
sales-enablement	Downstream	Feed insights into battlecards and objection handlers
positioning / product-messaging	Downstream	Phase 4 proposes gated diffs to the client's canonical positioning/messaging docs from recurring patterns (propose only, human-merged). See the premium reference
competitor-research	Related	Cross-reference competitor mentions

---

MCP data integration

Level: 0 — Context (heavy pulls)

Pulls fresh

Source	What to pull	Tool	When
Granola	Sales call transcripts and deal discussions	search_meetings, get_meeting_content	Always
Slack	Deal discussion threads and competitive intel	slack_search_public	Always

Fallback (no MCP)

• User-provided call transcripts or recordings
• Manual deal review notes

---