Run a Win/Loss Analysis on Last Quarter's Deals

You are an analytics-minded sales leader for a digital marketing agency. You read deal data like a strategist, not a scorekeeper, and you only make claims you can tie back to a specific deal in the input.
Agency: [AGENCY_NAME] — [SERVICES] | Period: [PERIOD] | ICP: [ICP_DESCRIPTION] | Data:
[DEAL_LIST]
(Each row should include: deal name, service line, deal size, source, sales cycle days, stage reached, won/lost, primary reason, competitor if any.)
Run a structured win/loss analysis on the deals above. Identify the top 3 patterns in wins, top 3 patterns in losses, and any cross-cutting themes (source, deal size band, ICP fit, competitor). Then give me 3 prioritized actions for next quarter ranked by expected impact on close rate.

- Every pattern must cite at least 2 specific deal names from the input.
- If a segment has fewer than 4 deals, flag it as "low sample — directional only."
- Do not invent reasons that are not present in the data; if a field is blank, say so.
- No rep-bashing language — frame gaps as process or qualification issues.

Output in 4 sections: (1) Headline metrics table (deals, win rate, avg cycle, avg deal size for won vs lost), (2) Win patterns, (3) Loss patterns, (4) 3 prioritized actions with expected impact + owner suggestion.

• [AGENCY_NAME] — Your agency name
• [SERVICES] — Service lines sold in the period (e.g., SEO, Paid Social, Web Design)
• [PERIOD] — Quarter or date range being analyzed
• [ICP_DESCRIPTION] — 1-line description of your ideal client profile
• [DEAL_LIST] — CSV or table of closed deals from your CRM — won and lost — with the fields listed in the context block

Agency: Northstar Digital — SEO, Paid Search, Web Design | Period: Q1 2026 | ICP: $5M–$50M DTC brands
Deal data:
FitGear Co | SEO | $4.8k/mo | Referral | 18d | Won | trusted referral | -
BrewHaus | Paid Search | $6k/mo | Inbound | 41d | Lost | price | AdVenture
Lumen Lighting | Web Design | $32k project | Outbound | 62d | Lost | timing | -
PeakPet | SEO | $5.2k/mo | Referral | 21d | Won | clear ROI case | -
MetroMattress | Paid Search | $9k/mo | Inbound | 38d | Lost | scope mismatch | AdVenture
Solavida | SEO + PS | $11k/mo | Referral | 27d | Won | bundled offer | -

Headline metrics: 6 deals, 50% win rate, won avg cycle 22d vs lost 47d, won avg $7k/mo vs lost $9k/mo.

Win patterns: (1) Referral-sourced deals closed 3x (FitGear, PeakPet, Solavida) — short cycle, no competitor. (2) SEO-led offers won every time in sample. (3) Bundled pricing (Solavida) closed at the highest ACV.

Loss patterns: (1) Paid Search losses to AdVenture (BrewHaus, MetroMattress) cite price/scope — competitive gap on positioning. (2) Outbound web-design deal (Lumen) stalled on timing — qualification gap. Low sample, directional only.

Actions: (1) Build a paid-search differentiation one-pager vs AdVenture (high impact, AE-owned). (2) Add a referral-ask trigger at month 3 of retainers (medium, CSM). (3) Tighten outbound web-design qualification on launch timeline (medium, SDR).

• Export directly from your CRM with consistent loss-reason picklist values — free-text fields produce noisy patterns.
• Run this same prompt monthly with the trailing 90 days to spot drift early instead of waiting for QBR.
• Pair the output with the Win/Loss Interview Guide prompt to validate the top loss pattern with 3 buyer calls.