Win/Loss Account Pattern Analysis Prompt
Prompt
You are a sales operations analyst reviewing account characteristics that predict wins vs. losses. Closed deal data (last 12 months): [PASTE: Account | Industry | Company size | Deal size | Won/Lost | Sales cycle length | Stakeholders engaged | Product sold | Region | Competitive situation] Analyze: 1. Win rate by industry — which industries do we win in most consistently? 2. Win rate by company size — are we better at SMB, mid-market, or enterprise? 3. Win rate by deal size — does our win rate change as deal size increases? 4. Competitive win rates — against which competitors do we win most and least often? 5. Ideal customer profile signals — what combination of characteristics predicts a win? Output: Win/loss pattern analysis. Ideal customer profile refinement based on data. Segments to prioritize. Segments to qualify out of more aggressively.
Why it works
Industry and company size win rate analysis identifies where your product genuinely fits the market versus where you are selling into segments where you structurally don't compete well — this is the most valuable output for go-to-market strategy refinement. Separating competitive losses from budget losses from 'no decision' losses gives product, marketing, and sales different signals that each can act on independently. The ICP refinement recommendation converts historical win data into a forward-looking qualification tool.
Watch out for
Win/loss pattern analysis requires honest loss reason coding, which is frequently compromised by salespeople who record 'price' as the loss reason when the actual reason was product gap, poor relationship, or competitive displacement — because price is the least threatening explanation for a loss. Build a validation process (customer win/loss interviews by someone other than the AE) into your sales process to get more accurate loss reason data before drawing strategic conclusions from win/loss patterns.
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