Categorize your losses and name the fix.
“Why do we actually lose deals, and which losses could we have prevented?”
Sorts lost deals into no-decision, price, competitor, timing, or fit, pulls competitive intel, and splits preventable from structural.
What this skill does
Reads your closed-lost deals and tells you why they died, in buckets you can act on: no decision, price, competitor, timing, or fit. It pulls the competitive intel out of the losses, then splits them into two piles that matter more than the total: the losses you could have prevented, and the ones that were structural and never yours to win. Each theme ends with the fix. The point is not to feel bad about the number. It is to stop losing the same deal twice.
Inputs
- Works today with: a list of your closed-lost deals, with segment or size, stage at loss, deal size, the loss reason (if captured), the competitor (if known), and any loss notes. Paste it or upload a CSV.
- More powerful connected to a CRM: it reads all of the above automatically, across every lost deal in the window you pick, including the raw loss notes.
- Sharper with a meeting or transcript tool: reads what the buyer actually said before they went dark, so "no decision" gets a real cause.
- Sharper with a product-analytics tool: on lost trials, shows whether they ever reached first value, which separates a product gap from a sales gap.
How it works
- 1Loss categorization (clean buckets)
Sort every lost deal into one primary reason: NO_DECISION (they picked nothing, status quo won), PRICE (budget or value gap), COMPETITOR (they picked someone else), TIMING (real but not now), or FIT (never a match). When the reason field is blank, read the notes or the transcript to place it. Report the share and the lost deal size behind each bucket, so a common reason and an expensive reason are both visible.
- 2Competitive intel pull
For every COMPETITOR loss, pull who won and, where the notes say so, why. Roll it into a per-competitor view: how often they beat you, at what stage, in which segment, and the reason that keeps repeating. This is the sheet your reps wish they had before the next competitive deal.
- 3Preventable vs structural split
Split every loss into two piles. PREVENTABLE: the deal was winnable and something in the motion lost it (single-threaded, slow follow-up, wrong persona, weak business case, late competitor entry). STRUCTURAL: it was never yours (no budget, no real need, wrong segment, a feature you do not build). This split matters more than the raw count, because only one pile is yours to fix.
- 4The fix per theme
Every theme ends with a specific, do-this-next fix, not a platitude. NO_DECISION driven by no compelling event points to earlier qualification of the trigger. PRICE losses that cluster at one tier point to packaging. A competitor winning late points to multi-threading sooner. Name the change, not the feeling.
Output format
CLOSED-LOST ANALYSIS · 41 losses, trailing 4 quarters
Loss reasons
Bucket Share Lost $ Note
No decision 37% $520K Status quo, no compelling event
Competitor 27% $410K Lost late, after Solution Validation
Price 20% $300K Clustered at the mid tier
Timing 10% $140K Real, revisit next cycle
Fit 6% $70K Wrong segment, disqualify earlier
Preventable vs structural
Preventable: 54% (no-decision + late competitor losses)
Structural: 46% (timing, fit, no-budget)
Who we lose to
Competitor Losses Wins late? Segment
Rival A 8 Yes, 6 of 8 mid-market
Rival B 3 No enterprise
Fixes:
1. No decision. Qualify the compelling event before Stage 3, or park it.
2. Rival A. Multi-thread by Stage 2. They win by out-flanking a single champion.
3. Price at the mid tier. Revisit packaging, not discounting.
Tips
- No theme reported below MIN_SAMPLE. A single bad-luck loss is not a trend.
- Every loss lands in exactly one primary bucket, so shares add up and nothing is double-counted.
- Preventable is only claimed with a reason from a field, a note, or a transcript, never a hunch.
- Competitive claims name the source. No invented win reasons for the other side.
Extract the repeatable reason you win.
Reads your won deals, finds the signals, personas, and motions that repeat, and ranks active lookalikes to work.
Turn churn themes into an early-warning system.
Reads churned and downgraded accounts, extracts the themes, and flags active accounts that resemble recent churners.