# Win/Loss Analysis: 12 Questions, 2 About Features

> Most teams reduce a lost deal to a missing feature. A real win/loss survey interrogates the whole sales act. Here is the full question bank, both surveys.

- Source: https://calven.ai/resources/product-marketing/win-loss-analysis
- Author: David Kolinek
- Published: 2026-07-08
- Tags: win/loss analysis, product marketing, competitive intelligence, PMM, sales enablement

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The reason your CRM gives for a lost deal, "price" or "missing feature", is usually the smallest and most convenient part of the truth. A rep picks it from a dropdown on the way to the next opportunity. Win/loss analysis is the discipline of going back to the buyer after a deal closes, won or lost, to learn why it really went the way it did, then acting on the patterns across deals. Done well, it covers the whole sales act: the competitive set, the sales experience, pricing and procurement, not only which features were missing. The proof is in the survey itself.

## Why do B2B deals actually get lost?

Often not to a competitor, and often not to a feature. Between 40 and 60 percent of B2B deals end in no decision at all, and of those, roughly 56 percent are driven by buyer indecision, a fear of making the wrong call, rather than a genuine preference for the status quo. That finding comes from Matthew Dixon and Ted McKenna's analysis of more than 2.5 million recorded sales conversations in [The JOLT Effect](https://www.amazon.com/JOLT-Effect-Performers-Overcome-Indecision/dp/0593538102). The deal you think you lost to a missing integration was, more likely, a buyer who never got comfortable enough to sign anything.

Even among the deals that do close, the reason lives across the whole buying experience. Which vendors the buyer weighed, and how you actually compared. Whether the sales team knew the product cold and spoke the buyer's industry. How pricing landed against the runner-up, and whether procurement was smooth or a fight. The one-line reason the buyer would give a peer over coffee. A loss is usually about one of those five things, and only one of them is the product.

Read a loss as a feature request and you spend the next quarter building something that was never the reason, while the actual cause, a weak second call or a licensing model finance hated, goes unexamined.

**Further reading:** [The JOLT Effect: How High Performers Overcome Customer Indecision — Matthew Dixon and Ted McKenna](https://www.amazon.com/JOLT-Effect-Performers-Overcome-Indecision/dp/0593538102) (Book)

The canonical, data-backed case that most losses are indecision and risk, not a feature gap.

## What questions should a win/loss survey ask?

Five areas, in this order: the competitive set and how you compared, product fit, the sales team's performance, pricing and contract experience, and one open question on why. The bank below is the survey [Calven's Win/Loss Agent](/platform/research#win-loss) actually runs, not a generic sample list. Count the questions and the argument makes itself.

**Table A: the win survey (12 questions).**

| Section | Question | What it uncovers | Type |
|---|---|---|---|
| Competition | Besides [Your Company], which vendors did you evaluate? | The real competitive set, in the buyer's words | Vendor picker |
| Competition | How did [Your Company] compare to each vendor? | Where you won and lost against each rival, side by side | Comparison table |
| Product | Which features did you like the most? | The specific capabilities that pulled the deal in | Open text |
| Product | Which features were missing or did you like least? | Gaps that nearly cost you a deal you still won | Open text |
| Sales Team | How would you rate your experience with the sales team? | Whether the team, not the product, closed it | Rating 1-4 |
| Sales Team | How would you rate the team's technical expertise? | Whether buyers trusted you on the hard questions | Rating 1-4 |
| Sales Team | How would you rate the team's industry expertise? | Whether the team spoke the buyer's language | Rating 1-4 |
| Sales Team | Overall, how would you rate the sales process? | The overall friction of buying from you | Rating 1-4 |
| Pricing & Legal | How did pricing compare to the second-best vendor? | Whether you won or lost on price, not guesswork | Single-select |
| Pricing & Legal | What feedback do you have on the licensing model? | Where packaging helped or nearly blocked the deal | Open text |
| Pricing & Legal | How smooth was the contract negotiation? | Whether procurement was easy or a near-miss | Rating 1-4 |
| Closing | In short, why did you pick [Your Company]? | The buyer's own one-line reason, unprompted | Open text |

Now the math. Of those 12 questions, exactly **two ask about product features**: the open-text questions on features liked and features missing. The other ten interrogate the competitive set, the sales team, pricing, procurement, and the buyer's own summary. Build your survey to answer "which features were we missing" and you have designed it to miss almost everything that decides a deal.

The [competitive set questions](/resources/competitive-intelligence/competitor-analysis) are the ones most teams skip and most regret skipping, because they name who you actually lose to and where. And when a loss traces back to a buyer who was never a fit in the first place, that belongs in your [ideal customer profile](/resources/product-marketing/ideal-customer-profile), not your roadmap.

Same shape, trimmed: the loss survey turns the competition section into a direct question about who won.

**Table B: the loss survey (also 12 questions, deltas from the win survey).**

| Section | Question | What it uncovers | Type |
|---|---|---|---|
| Competition | Besides [Your Company], which vendors were you evaluating? | The set you actually lost inside | Vendor picker |
| Competition | Which of those vendors did you ultimately choose? | Who beat you, named | Single-select |
| Competition | How did [Your Company] compare to each of those vendors? | Where the winner was stronger, item by item | Comparison table |
| Product | Which features did you like the most? | What still landed, even in a loss | Open text |
| Product | What features did [Your Company] lack versus the winner? | The real product gap, if there was one | Open text |
| Sales Team | The same four 1-4 ratings as the win survey | Whether the sell, not the product, lost it | Rating 1-4 |
| Pricing & Legal | How did pricing compare to the vendor you chose? | Whether price was a factor or an excuse | Single-select |
| Pricing & Legal | What feedback do you have on the licensing model? | Whether packaging pushed them away | Open text |
| Closing | Why did you not select [Your Company]? | The buyer's own reason, in one line | Open text |

Treat this as a starting point, not gospel. Every company is different, and some of these questions will matter less to you: maybe your buyers never test the sales team's technical depth, or licensing feedback is moot because you sell one standard license. Cut what does not apply and add what does. What carries across is the shape, the types of question worth asking, not the exact wording of each one.

The bank is also deliberately short. You could ask about onboarding expectations, champion strength, timeline, and budget authority, and each would tell you something. But completion falls as a survey grows: [Survicate's 2025 benchmark](https://survicate.com/reports/survey-completion-rate-benchmarks/) shows median B2B completion dropping from roughly 87 percent on the shortest surveys toward the high 70s as length climbs. A survey nobody finishes teaches nothing, so this one stays trimmed to the load-bearing questions.

**Free template:** [Win/Loss Survey Question Bank](https://docs.google.com/document/d/1tm9Cui4lMhaHiUmEyJQqAPXGe5xwfdsq0wp11A8VLKQ/edit?usp=sharing)

The full win and loss surveys, five sections each, ready to copy and send.

## Collection is the easy part. Analysis is the hard part.

Gathering responses is trivial. The value is created after, when you aggregate across deals, surface the themes that repeat, and push them to the teams that can act. One deal is an anecdote. The pattern across forty is a decision, and most programs never get to the pattern.

The evidence is blunt about it. Only about 39 percent of companies run an ongoing, cross-functional win-loss program, per Clozd's 2025 State of Win-Loss report; the rest do it once and file the deck. That same report finds 63 percent of companies see a win-rate increase from win-loss, rising to 84 percent for programs that have run two or more years. On average, only about 40 percent of deals get analyzed at all, per Klue's 2025 survey of 313 leaders. The insight exists. It is sitting in a folder nobody opens.

Treat completed surveys as a live repository instead, and put analysis on top of it. [Calven's Win/Loss Agent](/platform/research#win-loss) is one way to do that: it runs the survey as a standing discipline and keeps a single cumulative read on why you win, why you lose, and the themes that recur, updated as each new response lands. It ties every claim back to the actual response counts, so a theme it reports maps to a real number of deals rather than a hunch, and thin evidence gets flagged instead of dressed up as a trend. From there the finding goes where it can act: a repeated objection sharpens your [message map](/resources/product-marketing/message-map), a pattern of losing to one rival feeds the battle card, a win reason that keeps recurring belongs in your [positioning](/resources/product-marketing/how-to-write-a-positioning-statement).

## Can AI run win/loss interviews?

Yes, and the economics changed recently enough that most teams have not caught up. Conversational voice AI tools now let a company stand up its own interviewer for cents per minute, work that used to require a research vendor. A buyer who opts in speaks their answer instead of typing it, and spoken answers to open questions run roughly twice as long and cover more ground than typed ones, per [Höhne and colleagues' 2024 study](https://journals.sagepub.com/doi/full/10.1177/08944393231160961). Buyers are also more forthcoming with a neutral interviewer than with the vendor who just lost or won the deal.

That neutrality matters most on the deals you least control. If you run win/loss yourself, expect the loss interviews to be the hard part: a buyer who chose someone else rarely wants a call with the vendor they passed on. Win interviews are easier, since a happy buyer will usually talk. For the losses, a neutral party is often the only way to get the conversation at all, whether that is dedicated win/loss software, an outsourced program, or an agent like [Calven's Win/Loss Agent](/platform/research#win-loss), which runs both the survey and the interview and reads as neutral because it is not the rep who just lost the deal.

There is an honest trade-off worth naming. Typed answers are more precise, more comparable across deals, and easier to count. Spoken answers win on depth. The best programs offer both and let the respondent choose, because a buyer who would click through a short form may not want a call, and coverage matters more than richness on any single deal.

| Approach | Typical cost | Deal coverage | Answer depth |
|---|---|---|---|
| Managed / outsourced program | ~$25K to $80K per year | A small share of deals | Deep, human-led |
| Win-loss software | ~$15K to $50K+ per year | Broader, survey-based | Structured, comparable |
| AI-moderated interview | From ~$20 to $25 per completed interview | Can scale to most deals | Spoken, richer on the "why" |
| Calven Win/Loss Agent | No extra cost, part of your subscription | Unlimited | Structured and spoken, both |

Cost and coverage figures for the first three rows are vendor-published ranges from User Intuition, Won.Studio, and Koji; treat them as directional, not fixed.

Win/loss stops being a quarterly deck and becomes a standing instrument the moment two things are true: the whole sales act is on the survey, not just the product, and something is reading every response as it lands. Get those right and the CRM dropdown stops being the story you tell yourself about why you lost.