See why you're winning and losing deals — based on what buyers actually said, not what your CRM disposition field captured.
A win-loss report shows why deals are won and lost by running paired analysis across your sales conversations — surfacing win factors, loss factors, swing factors, objection survival rates, and competitive displacement with evidence counts and direct buyer quotes across every recorded deal.
Most win-loss analysis tells you what happened. This report tells you why — and what to change.
| Question | What you'll see |
|---|---|
| Why are we winning deals? | What buyers say we do well, which capabilities tip deals in our favor, and what reps do differently in wins |
| Why are we losing deals? | Where we fall short, which objections we can't overcome, and what competitors are beating us on |
| Are we losing to competitors or to "do nothing"? | Which competitors we beat and lose to, plus how often the status quo or internal tools win instead |
| Which deals should we stop chasing? | Persona, company size, and entry point combinations that rarely convert — so you can qualify out earlier |
| Which objections are killing deals vs. just slowing them down? | Survival rate for each objection — how often it appears in wins vs. losses, and what the winning reps did differently |
| What should sales, marketing, and product each change? | Specific actions for each team based on the data — qualification criteria, messaging adjustments, coaching priorities, product gaps |
Copy this skill and run it with Claude using OnePerfectSlice via MCP. The skill figures out what data you have, picks the best analysis approach, then runs paired analyses across your won and lost deals to find what's different.
You are a win-loss analyst producing a data-driven win-loss report for a GTM leadership team. Your job is to compare patterns across won and lost deals using buyer conversation data and produce actionable insights about why deals are won and lost — and with whom.
## Step 1: Discover Available Filters and Select Approach
1. Run `get_filter_options(crm_deal_stages)` to find available deal stages. Identify which stages represent Closed Won and Closed Lost (or equivalent).
2. Run `get_filter_options(call_types)` to see available call type classifications and their volumes.
3. Select the analysis approach based on data availability:
### Approach A: Clean Paired Analysis (preferred)
**Use when:** CRM deal stages have 15+ calls in both Closed Won and Closed Lost cohorts.
- Filter slices directly by deal stage.
- Produces the highest-confidence findings.
### Approach B: Proxy Paired Analysis (fallback)
**Use when:** CRM deal stage data is sparse (<15 calls per cohort) but call type data is rich.
- **Won cohort:** Post-sale call types (e.g., Onboarding & Training, Customer Growth) = confirmed wins. They bought and are implementing.
- **Loss/stall cohort:** Pre-sale call types (e.g., Demo, Discovery) that also show deal risk signals. Use `search_evidence(element_keys=["ops_deal_risks"])` to identify calls with stall/loss language, then intersect with pre-sale calls.
- Caveat in the report: "Won" cohort reflects post-sale conversations (different moment in the deal than pre-sale), so behavioral comparisons should be interpreted carefully.
- Still produces diffable cohorts and swing factor analysis.
### Approach C: Loss Autopsy (last resort)
**Use when:** Neither CRM stages nor call types provide enough volume for two cohorts.
- Run slices unfiltered against the full call set for baseline patterns.
- Use `search_evidence(element_keys=["ops_deal_risks"], query="lost OR chose competitor OR went with OR decided against OR no decision OR not moving forward OR went another direction OR too expensive OR not a fit OR passed OR decided to stay")` to identify calls with loss/stall signals.
- Cross-reference loss-signal calls against baseline to find what was different about them.
- Caveat in the report: findings are directional. Without a clean win cohort for comparison, you can identify what losing deals have in common but cannot confirm whether those same patterns also appear in wins.
- This approach CANNOT produce swing factors, survival rates, or win-rate-by-segment analysis. It produces a qualitative loss profile only.
Present the selected approach and rationale to the user:
> "CRM data shows X won / Y lost calls. Call type data shows [volumes]. I recommend Approach [A/B/C] because [reason]. Proceed?"
Wait for confirmation before running slices.
## Step 2: Check Data Volume
Before spending runs, validate you have enough data in each cohort:
1. Run `preview_slice_count()` with the selected won/loss filters.
2. If either cohort has fewer than 10 calls, flag it as a small sample and proceed with caveats. If fewer than 5, note that findings are directional only.
Present the volume to the user:
> "I found X calls in the won cohort and Y calls in the loss/stall cohort. Enough to proceed? Or do you want to adjust the date range or filters?"
Wait for confirmation before running slices.
## Step 3: Run Paired Analyses
Run the following slices TWICE each — once for the won cohort, once for the loss/stall cohort (using whatever filter method the selected approach requires):
| Slice | Why |
|---|---|
| `decision_criteria` | What factors buyers weigh — and which ones tip toward us vs. away |
| `alternatives_evaluated` | Who we competed against in wins vs. losses |
| `objection_patterns` | Which objections were overcome vs. fatal |
| `entry_points` | What triggered the buying process — do different triggers convert differently? |
| `personas` | Which buyer roles and motivations appear in wins vs. losses |
| `segments` | Which company profiles (size, industry, tech stack) win vs. lose |
| `whats_working` | Successful rep behaviors (expect stronger signal in wins) |
| `whats_breaking` | Failing rep behaviors (expect stronger signal in losses) |
Run slices in parallel where possible to save time. Use `get_run(summary_only=true)` for initial synthesis, then drill into evidence with `get_run(include=["evidence"], element_keys=[...])` only where deeper quotes are needed.
For Approach C: Run slices unfiltered only (no pairing). Then use `search_evidence` to identify loss-signal calls and cross-reference their evidence against the overall patterns.
## Step 4: Diff Analysis
For Approaches A and B, compare the Won vs. Lost results for each slice pair and categorize every finding as:
- **Win Factor:** Present in wins, absent or rare in losses. These are your competitive advantages and successful behaviors.
- **Loss Factor:** Present in losses, absent or rare in wins. These are your vulnerabilities and failure modes.
- **Swing Factor:** Present in BOTH, but handled or resolved differently. These are where execution makes the difference — same objection, same competitor, but different outcome depending on how the rep responded.
Swing factors are the highest-leverage insights. Prioritize them.
For Approach C: Categorize findings as "loss-correlated" (present in loss-signal calls) vs. "baseline" (present across all calls). Flag that without a clean win cohort, these cannot be confirmed as true differentiators.
## Step 5: Produce the Report
Structure the report with these sections:
1. Executive Summary (top 3 win/loss/swing factors, ICP sweet spot)
2. Entry Point Analysis (which triggers predict outcomes)
3. Win-Loss by Buyer Profile (personas that win vs. lose)
4. Win-Loss by Company Profile (segments that win vs. lose)
5. Decision Criteria (what tipped the scale)
6. Competitive Displacement (who we beat vs. lose to)
7. Objection Patterns (fatal vs. survivable)
8. Rep Behavior Differences (what winning reps do differently)
9. Recommended Actions (for sales, marketing, product, enablement)
## Guidelines
- Every claim must cite evidence counts from both cohorts.
- Flag any finding based on fewer than 3 data points as "early signal."
- Swing factors (same input, different outcome) are the most actionable findings. Prioritize them.
- When quoting, always label whether the quote is from the won or loss cohort.
- Do not attribute losses to individual reps by name. Focus on behaviors, not people.
- If the cohorts are unbalanced (e.g., 30 wins, 8 losses), note this and caution against over-indexing on the smaller sample.
- If using Approach B, note that post-sale calls capture a different moment than pre-sale calls — behavioral comparisons (whats_working, whats_breaking) reflect different conversation contexts and should be interpreted with that in mind.
- If using Approach C, clearly state that all findings are loss-correlated patterns, not confirmed win-vs-loss differentiators.
- Where possible, compare the current period to any prior win-loss analysis to surface trends.
Prerequisites:
Recommended timing:
Skill version: v2 — May 2026
Here's what the report looks like when you run it. The structure and depth are real — the company names, numbers, and quotes are illustrative.
Period: [Last 30 days]
Deals analyzed: [24] won, [11] lost
Win rate: [69%] (CRM baseline: [65%] — slightly above trend)
Top 3 Win Factors:
Top 3 Loss Factors:
Top 3 Swing Factors:
ICP sweet spot: [Mid-market] companies ([100–500 employees]), [VP-level sponsor], evaluating because of [growth pain / scaling existing process], coming from [manual or spreadsheet-based workflow].
| Entry Point | Won | Lost | Implication |
|---|---|---|---|
| Scaling pain / outgrowing current process | [12] | [2] | Best entry point. These buyers already feel the problem. |
| New leadership / mandate to modernize | [6] | [3] | Good but slower. New leaders evaluate more options. |
| Inbound from content / SEO | [4] | [5] | Below average win rate. Often early-stage researchers, not active buyers. |
| Competitor frustration | [2] | [1] | Early signal. Small sample but high conversion when it appears. |
| Alternative | We Won | We Lost | Why we win | Why we lose |
|---|---|---|---|---|
| [Competitor A] | [8] | [6] | Depth of [key capability]. Buyers who see a demo choose us. | Price. Buyers who don't see a demo default to the cheaper option. |
| Status quo (spreadsheets) | [5] | [4] | ROI math landed. Buyer quantified the cost of manual work. | "It works fine for now." No urgency to change. |
| [Competitor B] | [3] | [1] | Right-sized. They felt overbuilt and expensive. | Early signal only — 1 loss. |
Swing factor — [Competitor A]: The difference between winning and losing against [Competitor A] was whether the rep ran a TCO comparison early. In [7] of [8] wins, the rep introduced total cost of ownership in the second call. In [5] of [6] losses, pricing was only discussed when the buyer raised it — by then, the cheaper option had already framed the conversation.
Won deal: "Once we saw the full cost picture including the upgrades we'd need with [Competitor A], it was actually cheaper to go with you."
Lost deal: "Honestly, [Competitor A] is good enough for what we need and it's half the price."
| Objection | In Wins | In Losses | Survival Rate |
|---|---|---|---|
| Pricing / budget concerns | [12] | [7] | 63% — Swing factor |
| Missing [integration] | [1] | [5] | 17% — Mostly fatal |
| Security / compliance review | [6] | [4] | 60% — Swing factor |
| "We need to see it work first" | [8] | [2] | 80% — Usually overcome |
Swing factor — Pricing: Pricing came up in both wins and losses. The difference was when and how reps responded. Winning reps reframed around ROI within the first response — "here's what this costs you every month without it." Losing reps defended the price or offered to "check on discounts."
Won: "When we did the math on how many hours our team spends on this manually, it was obvious."
Lost: "We liked your product but couldn't justify the price difference to our CFO."
Mostly fatal — Missing [integration]: When [specific integration] was a requirement and we didn't have it, we lost [5] of [6] times. This is a product gap, not a positioning gap. Only [1] win where the buyer accepted a workaround.
For Sales Leadership:
For Marketing:
For Product:
For Enablement:
Data: [24] won deals, [11] lost deals, [last 30 days]. 8 paired analyses across cohorts. Claims with <3 data points flagged as "early signal." Prompt v1 — May 2026.
What this is: A report that compares what happened in your won deals against what happened in your lost deals — using the same analysis framework on both sides. Every claim is backed by evidence counts from each cohort. This isn't CRM data or exit surveys — it's what buyers actually said during the sales process.
This is the most important concept in the report. A swing factor is something that appears in BOTH wins and losses — but with different outcomes depending on how it was handled. Same objection, same competitor, same buyer profile — different result.
Swing factors are higher leverage than pure win or loss factors because they're not about finding new advantages or fixing fundamental gaps. They're about doing what you're already doing, but better. That usually means coaching, not product changes or strategy shifts.
Examples:
| Survival Rate | What it means | What to do |
|---|---|---|
| 75%+ | Your team mostly handles this. A few reps need help. | Share what working reps do. Light coaching. |
| 40–75% | Swing factor. Some reps overcome it, some don't. High coaching leverage. | Build a talk track from what winning reps do. Train the team. |
| Below 40% | Mostly fatal. This objection kills deals regardless of the rep. | Is it a product gap, a positioning gap, or a qualification gap? Fix the root cause or qualify out earlier. |
| Win-Loss Report | Competitive Landscape Brief | |
|---|---|---|
| Focus | Why deals are won and lost (outcome-driven) | Who you compete against and where you stand (landscape-driven) |
| Data split | Won vs. lost cohorts compared side by side | All deals together |
| Unique value | Swing factors — same situation, different outcome. Where coaching and process changes have the most impact. | Competitive position and alternative landscape — who you're up against and how often |
| Cadence | Monthly or quarterly | Monthly |
| Best for | Sales leadership, enablement, product | PMM, leadership, strategy |
| Primary question | "Why are we winning and losing?" | "Who are we competing against?" |
Monthly (recommended for most teams):
Quarterly (strategic reviews):
You need deal outcome data in your CRM (Closed Won / Closed Lost stages) and call recordings from both cohorts. If you have fewer than 10 deals in either cohort, extend the time window to 60 or 90 days. The report flags small samples automatically. Start with the swing factors — they're the quickest wins because they're about doing what you're already doing, but better.
| What to do | How |
|---|---|
| Update your qualification criteria | The report shows which entry points, personas, and segments convert — and which don't. Use the data to tighten stage gates and help reps focus on deals they're likely to win. |
| Build coaching around swing factors | Swing factors are same-situation-different-outcome findings. The report includes quotes from both sides showing what winning reps did differently. Turn those into talk tracks and training. |
| Update your battlecards | The competitive displacement section shows who you beat and lose to, and why. Map the "why we win" and "why we lose" columns directly to battlecard updates. Learn more about keeping battlecards current → |
| Flag product gaps | Loss factors tagged as product gaps come with evidence counts and buyer quotes. Share them with product directly — this is stronger evidence than feature requests from a sales call summary. |
| Refine your ICP | The ICP sweet spot in the executive summary tells you which persona + segment + entry point combination wins most often. Compare it against where marketing is actually spending. |
Parent concept
Job that produces this output
Sibling outputs (same program, different deliverable)
What to do with this output
Traditional win-loss interviews are deep but narrow — most teams do 5–10 per quarter with a third-party firm. They give you strategic nuance but can't show you patterns across 50 or 100 deals. This report analyzes every deal with call recordings, structured by the same framework. The tradeoff: interviews give you depth on a few deals, this gives you patterns across all of them. Most teams use both — the report for broad patterns, interviews for strategic accounts where you need more context.
You need at least 10 deals in each cohort (won and lost) for the report to surface reliable patterns. The sweet spot is 20–50 in each. Below 10, findings are directional only and the report flags them as small sample. If your monthly volume is low, run it quarterly instead — a 90-day window usually gives you enough. The report automatically flags anything based on fewer than 3 data points as "early signal."
They're different things. CRM disposition fields capture what the rep selected from a dropdown — useful for pipeline reporting but it's the rep's summary, not the buyer's words. This report captures what buyers actually said across the full sales process: which objections came up, how they described competitors, what criteria they used. The CRM says "lost to price." The report says "pricing came up in 7 losses — in 4, buyers acknowledged we were better but couldn't justify the premium to their CFO." Use both.
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