Win/Loss Analysis Builder
Diagnose exactly why you won or lost a specific bid or listing.
What is the Win/Loss Analysis Builder?
The Win/Loss Analysis Builder is a free AI skill that produces a structured retrospective on why a food or beverage company's own team specifically won or lost a bid, a retailer listing, or a shelf-space decision. You give it what happened, what you know about the decision process, and any feedback the buyer gave; it returns a ranked list of likely factors — price, relationship, category fit, presentation, timing, or competitor strength — each checked against the evidence you actually have, a confidence level per factor, the questions still open, and specific process changes to carry into the next pitch. It is built for insights and sales teams who want more than a one-line explanation like 'they went with someone cheaper' before the same mistake repeats. Because it separates what the buyer actually said from what your team is inferring, the retrospective stays honest rather than becoming a comfortable story. Layering in live food and beverage demand data checks whether a lost listing reflects a real category shift or a fixable gap in your own pitch.
Who it's for
- Insights analysts documenting the reasons behind a specific win or loss
- Sales leaders debriefing a team after a bid or listing decision
- Category managers reviewing why a reset decision went a certain way
- Founders learning from an early retailer pitch before the next one
What you get
- A ranked list of likely win or loss factors
- Each factor checked against the evidence you actually have
- A confidence level per factor
- The open questions still unanswered about the decision
- Specific process changes to carry into the next pitch
- A one-page debrief summary for the team
How to use it
- 1Paste the prompt into ChatGPT, Claude or Gemini.
- 2Fill in the brackets (what happened, buyer feedback, decision context).
- 3Answer its clarifying questions about what you know versus are guessing.
- 4Ask it to turn the process changes into a checklist for the next pitch.
See it in action
Example input
We lost the salty snacks reset at fictional 'Alderwick Grocers' to a competitor, fictional 'Foxglove Foods'. Buyer feedback was brief: 'went a different direction on assortment this cycle.' We know Foxglove offered a lower per-unit cost and had 2 more SKUs in the pitch. Our brand, fictional 'Ridgeline Snacks', had stronger velocity data in our existing 4 stores there. We want to know what actually happened before our next pitch to them in 6 months.
Example output
Win/loss analysis — Ridgeline Snacks vs. Foxglove Foods, Alderwick Grocers salty snacks reset Ranked factors, checked against what you actually know 1. Assortment breadth (high confidence) The buyer's own feedback — "went a different direction on assortment" — directly points here, and it's corroborated by the fact that Foxglove's pitch had 2 more SKUs than yours. This is the strongest, most directly evidenced factor: buyers running a reset are often solving for a total-set story (variety, price ladder, incremental occasions covered), not just per-SKU strength. A 4-SKU pitch competing against a 6-SKU pitch may have simply looked thinner on the page, independent of any single SKU's merits. 2. Price (moderate-to-high confidence) Foxglove's lower per-unit cost is a fact you have, and price is almost always a live factor in a reset decision even when not named explicitly in buyer feedback; buyers rarely cite price directly because it makes the conversation adversarial for next time. Treat this as a real contributing factor, but not proven as the deciding one, since the buyer's own words point to assortment first. 3. Existing velocity data (investigate further — the most important open question) This is the piece that doesn't fit neatly: you had stronger velocity in your existing 4 stores, which should be a strong argument in your favor, yet you still lost. Two explanations are both plausible with what you have: (a) your velocity data wasn't weighted heavily in this particular buyer's decision process, which would be useful to learn directly, or (b) it was outweighed by the assortment-breadth and price factors above. This is the single most important thing to ask the buyer directly before your next pitch; don't let a strong data point go unused without understanding why it didn't move the decision this time. 4. Relationship and timing (low confidence, data not provided) Nothing in what you've shared indicates whether this was a relationship factor (a newer or more frequent Foxglove contact) or bad timing (a category-wide push toward more SKUs this cycle across all vendors, not specific to you). Flag both as open questions rather than dismissing them. What the feedback doesn't tell you "Went a different direction on assortment" is buyer-diplomatic language that could mean several different things: more SKUs, a specific format Foxglove had that you didn't, or a category-wide strategic shift toward variety this cycle that would have disadvantaged any vendor pitching a tighter set, not just you specifically. Getting more specific feedback matters more here than guessing. Open questions to ask the buyer directly 1. Was assortment breadth the primary factor, or is that shorthand for something more specific (a format gap, a flavor gap)? 2. How was our stronger existing velocity data weighted in the decision, if at all? 3. Is a broader assortment a one-cycle preference or a standing category strategy going forward? Process changes for the next pitch 1. Bring a broader assortment option to the table next cycle, even if some SKUs are positioned as "available if needed" rather than core asks; don't let a thinner pitch be the reason you lose on a factor unrelated to product quality. 2. Lead harder with the velocity data next time; make it impossible for the buyer to overlook, since it clearly didn't carry enough weight this cycle. 3. Request a specific, structured debrief call rather than accepting the one-line email feedback; "different direction on assortment" is not specific enough to fully act on. One-page debrief summary Ridgeline Snacks lost the Alderwick Grocers salty snacks reset to Foxglove Foods primarily on assortment breadth, with price as a likely secondary factor; both are reasonably well-evidenced. The open question that matters most for the next 6 months is why our stronger existing velocity data didn't outweigh those factors; get a direct answer from the buyer before assuming what happened. The clearest process fix is bringing a broader SKU set to the table next cycle while leading harder with velocity data than we did this time. Want me to draft the follow-up email requesting a more specific buyer debrief, or turn the process changes into a pre-pitch checklist for the next cycle?
The prompt
Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.
# Role You are a senior win/loss analyst for food & beverage suppliers. You produce honest retrospectives on why a specific bid, listing, or reset decision went a certain way, separating what the buyer actually said from what your team is inferring, and you never let a comfortable story replace an unanswered question. # Context I'll provide - What happened: [SITUATION — the bid, listing, or reset, and the outcome] - Competitor(s) involved: [COMPETITOR(S)] - Buyer feedback received, if any: [BUYER FEEDBACK — quote it directly] - What you know about the decision: [KNOWN FACTS — price, assortment, relationship, timing, whatever you have] - Your own strengths going in: [YOUR STRENGTHS — data, relationship, product edge] - What this analysis needs to inform: [NEXT STEP e.g. next pitch, retailer relationship, internal review] # Your task
Frequently asked questions
- What is a win/loss analysis?
- A win/loss analysis is a structured retrospective on why a specific bid, retailer listing, or shelf-space decision was won or lost, going beyond a one-line explanation to rank the actual contributing factors, check them against real evidence like buyer feedback, and flag what's still unknown. This skill builds that retrospective and turns it into concrete changes for the next pitch.
- How is this different from the Competitive Launch Brief skill?
- The Competitive Launch Brief decodes a competitor's product launch generally, reading their strategic bet, positioning, and target consumer, regardless of whether your company was directly involved in a decision. This skill is a retrospective specifically on a win or loss YOUR OWN team experienced in a bid, listing, or shelf decision, working from the buyer feedback and outcome you were actually part of. Use the launch brief to read a rival's move in the market; use this skill to understand why a specific decision went for or against you.
- Which AI models can run this prompt?
- Any capable chat model — ChatGPT, Claude, or Google Gemini. The prompt is model-agnostic, so paste it into a chat, save it as a Custom GPT, or store it as a reusable skill so every bid or listing debrief follows the same structured, evidence-first process.
- Will it tell me exactly why we lost?
- No. It structures a confidence-ranked analysis from the evidence you provide and flags what's still unknown; it won't fabricate the buyer's real motivation, and it explicitly separates confirmed feedback from inference, so you don't mistake a plausible story for a proven cause.
Related skills
Assortment Gap Finder
Find the SKUs your shelf is missing before a buyer does.
Get it freeBrand Health Report Translator
Turn brand tracker scores into a plain-English story and a next-actions list.
Get it freeBrand Positioning Statement Builder
Nail the positioning your whole brand can build on.
Get it freeWant the live data behind sharper outputs?
These skills get better with real-time F&B intelligence. See what Tastewise can do for your team.