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Loss Reason Pattern Monitor

Computes the overall loss-reason distribution from a closed-lost deal export, flags a reason as rising only once a recent-vs-prior window comparison clears a minimum sample size, and flags segment-level clustering, a segment losing to one reason far more than the book overall, once that segment has enough losses of that reason to mean something.

Any CRM

The problem

A single lost deal has a story. A pattern across many lost deals has a cause, and most teams only find it by accident, months after it started, because nothing is watching the loss-reason distribution on an ongoing basis.

How it works

01Reads a closed-lost deal export with a structured loss-reason field, the same picklist most CRMs already capture
02Computes the overall distribution of loss reasons across the whole book
03Flags a reason as rising only when a recent-versus-prior window comparison clears a minimum sample size in both windows
04Flags segment-level clustering, one segment losing to a specific reason far more than the book overall, once that segment has enough losses of that reason to trust

Verified on real test data

Loss Reason Pattern Monitor

26 synthetic closed-lost deals: a prior 90-day window with an even spread across four loss reasons, a recent 90-day window where Pricing losses spike, plus a separate segment-clustering batch where Enterprise loses almost exclusively to Competitor.

Pricing, +41.8 pts

Rising reason correctly flagged

Enterprise / Competitor

Segment cluster correctly isolated

SMB

Unflagged segment correctly cleared

→Correctly computed the overall distribution across all 26 losses (Pricing 42.3%, Competitor 30.8%, Timing 15.4%, No Budget 11.5%) and correctly gated the trend comparison on both windows clearing the 5-loss minimum before trusting a percentage.
→Correctly flagged Pricing as rising, 60% of recent losses versus 18.2% of prior losses, a 41.8-point swing, once both the 10-loss recent window and the 11-loss prior window cleared the sample gate.
→Correctly isolated Enterprise as clustering hard on Competitor losses (83.3% of that segment's losses versus 30.8% book-wide, a 52.5-point gap on 5 qualifying losses), while correctly leaving the SMB segment unflagged since its mix never cleared the clustering threshold.

The book-wide loss reason looked like Pricing. One specific segment was actually losing almost exclusively to a named competitor, a pattern the overall distribution alone would have buried.

See the full test run →

Questions about Loss Reason Pattern Monitor

Why not read call notes and emails to infer the loss reason automatically?

That's judgment-heavy and inconsistent to test, an LLM inferring a category from free text will call similar deals differently depending on phrasing. Reading the structured loss-reason field instead keeps the result deterministic and auditable, if that field isn't being filled in consistently, that's the real finding to fix first.

Does this rank reps by their loss reasons?

No, on purpose. Clustering is reported at the segment level only, the same restraint Call Scorecard Builder applies to reps. A clustered loss reason is a packaging, positioning, or process signal to fix, not a scorecard for whoever worked those deals.

Runs against your CRM using your own credentials, through your own AI instance. We don't copy, store, or retain your data.

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