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Win/Loss Analyzer

Most salespeople think they know why they win and lose — and most are wrong. 'We lose on price' almost always turns out to be 'we lose when the champion can't reach the decision maker.' The Win/Loss Analyzer pulls real patterns out of deal data and replaces guesswork with a clear action list.

What this skill does

Gut feel about why you win and lose is almost always wrong. Common self-diagnosis is "we lose on price." Common actual answer is "we lose when discovery doesn't surface a quantified pain point" or "we lose when the champion never gets us in front of the budget owner." The fix for the wrong diagnosis is the wrong fix — better pitch training when the real need was better qualification. This skill exists to replace the wrong diagnosis with the right one.

Analysis runs across seven dimensions. Win patterns — what won deals share, by source, buyer profile, and process markers. Loss patterns — which stages deals die in, which objections recur, which competitors beat you consistently. Speed analysis — where deals stall and the point-of-no-return delay threshold. Competitive intel — when you win against each competitor versus when you lose. Process gaps — which sales steps, when skipped, correlate with losses. Pricing and value — the deal-size sweet spot and where you consistently lose. Champion and stakeholder analysis — how the number of contacts and the seniority of the champion shifts win rate.

Confidence gets called out honestly. Five to ten deals gets "signals, not certainties." Eleven to twenty gets "emerging patterns." Twenty-plus gets "reliable patterns." Below five deals, the skill flags that this is guesswork and asks for more data rather than producing confident-sounding noise. The minimum viable dataset is five; the ideal is fifteen-plus; and the skill is explicit about which.

Two design choices that matter. Before presenting findings, the skill asks for the user's pre-existing theory and tests it against the data explicitly — the most valuable insight is usually "you think X, but the data says Y," and confirming bias is worse than disagreeing with it. And stalled deals count as losses: if a deal has been stuck for twice the average cycle, it's lost, and pretending otherwise inflates the pipeline forecast and prevents the post-mortem that would catch the pattern. Every insight ends with "therefore, change X" — analysis without an action is trivia.

When this triggers

  • ·You have 15+ closed deals from the last 12 months and have never analysed the pattern
  • ·You think you know why you lose but want to test that theory against the data
  • ·A specific competitor keeps beating you and you want to know in what scenarios
  • ·Your sales cycle is stretching and you don't know which stage is dragging
  • ·You're building a qualification checklist and want it grounded in actual wins, not gut feel

Example

Trigger

User pastes a CSV: 22 deals over 12 months, 8 won and 14 lost, with deal size, source, competitor, and reason-for-loss columns.

Output

Deals analysed: 22 (8 won, 14 lost). Confidence: emerging patterns, not certainties. Insight 1: lead source predicts win rate more than anything else. Pattern: referrals close at 67% (4 of 6). Outbound closes at 11% (2 of 18). You think you have a sales problem; you have a lead-source problem. Action: stop outbound for 30 days, invest the time in a referral programme. Re-measure. Insight 2: you don't lose on price — you lose on authority. Pattern: of 14 losses, 11 had no decision-maker contact by the third call. The user's stated theory was "too expensive"; price was cited in only 4 of 14 losses. Action: add a stage gate at call 2 — no decision-maker contact = no proposal. Insight 3: deals over £15k take 4x longer to close. Pattern: median cycle was 21 days for sub-£15k, 84 days for £15k+. You're underestimating cycle on enterprise by ~3 months. Win profile: referrals, sub-£15k, single decision maker. Loss profile: outbound, multi-stakeholder, no champion.

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What you get

  • 153-line SKILL.md, ready to drop into ~/.claude/skills/
  • Tested through 3 Karpathy-loop iterations (versions v1.0.0 → v1.3.0)
  • Triggers automatically when relevant — no command to remember
  • Lifetime updates as the skill is refined further

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