Win-loss interview template
A win-loss interview template covering recent buying journeys, unaided decision criteria, alternatives, outcome branches, and evaluation friction.
Questions and setup
Customize the placeholders and apply the setup notes in the study builder before launch.
- 01
Multiple Choice
When was the decision on the most recent evaluation that included [Product] finalized?
- Within the past 30 days
- 31-60 days ago
- 61-90 days ago
- More than 90 days ago
- The evaluation is still underway
- I am not familiar with this evaluation
Setup: By default, qualify the first two options; include 61-90 days only when sample availability requires it.
- 02
Multiple Choice
Which best describes your direct involvement in that evaluation?
- I made or shared the final decision
- I was part of the evaluation team and compared options
- I provided end-user or subject-matter input
- I was informed but did not participate
- I was not involved or do not remember
Setup: For a primary win-loss sample, qualify the first two options.
- 03
Multiple Choice
What was the final outcome of the evaluation?
- We selected [Product]
- We selected another vendor
- We kept or renewed the existing approach
- We chose an internal solution
- We deferred the decision or took no action
Setup: Use this question to route participants to the matching outcome probe below.
- 04
AI Question
Take me back to the first event that caused the team to consider changing the status quo. What happened, who noticed it, and what did the team do next?
Setup: Allow up to two follow-ups to establish the timeline, stakeholders, and trigger without suggesting a reason.
- 05
Multiple Choice
Which alternatives reached a serious evaluation stage, such as a demo, trial, security review, pricing review, or proposal? Select all that apply.
- [Competitor A]
- [Competitor B]
- [Competitor C]
- Keeping or renewing the existing approach
- Building an internal solution
- No alternative besides [Product]
Setup: Enable multi-select and the built-in Other response, randomize substantive options, and keep No alternative last. Do not combine it with another choice.
- 06
AI Question
What were the two or three factors that most affected the final decision? Which, if any, was decisive?
Setup: Allow up to two follow-ups. Elicit criteria unaided and ask what evidence supported each factor.
- 07
Multiple Choice
Which of these materially affected the final decision? Select up to three.
- Ability to meet the key use case
- Implementation or switching risk
- Total cost and commercial terms
- Security, compliance, or procurement fit
- Integration with the existing environment
- Confidence in the vendor and relationship
Setup: Enable multi-select with a maximum of three and the built-in Other response, then randomize options.
- 08
AI Question
For each seriously considered option, what evidence made it stronger or weaker on the criteria that mattered?
Setup: Allow up to two follow-ups; skip when no alternative besides [Product] was evaluated.
- 09
AI Question
What nearly prevented [Product] from being selected, and what resolved it?
Setup: Show only when [Product] was selected; route to the shared evaluation rating afterward.
- 10
AI Question
What evidence or experience most weakened [Product]'s position, and when did that happen?
Setup: Show when another vendor, the existing approach, or an internal solution was selected; route to the shared rating afterward.
- 11
AI Question
What prevented the organization from acting after the evaluation?
Setup: Show only when the decision was deferred or no action was taken; route to the shared rating afterward.
- 12
Rating Scale
Overall, how easy or difficult did [Product] make it to obtain the information and evidence needed to evaluate fit?
Setup: Use a 1-5 scale from Very difficult to Very easy.
- 13
AI Question
What is the main reason for that ease rating?
Setup: Allow one neutral follow-up tied to a specific event in the evaluation.
- 14
AI Question
What, if anything, should [Product] keep, stop, or change about its evaluation process?
Setup: Allow one follow-up and ask for something that actually happened during this evaluation.
- 15
Open Text
Is there anything else about how you made this decision that would be useful for us to know?
This template examines how a buying decision happened. It qualifies a recent, involved buyer, reconstructs the trigger and alternatives, elicits decision criteria before showing a list, and routes each outcome to a specific probe.
What this template measures
The template follows one buying decision in order: trigger, serious alternatives, unaided criteria, structured confirmation, comparative evidence, and outcome. The structured question asks for up to three material factors rather than forcing participants to rank criteria that may not have mattered.
Outcome-specific AI Questions distinguish a near-loss on a win from the evidence that weakened [Product] on a loss or the blockers behind no action. A shared ease rating measures whether buyers could obtain the evidence needed to evaluate fit, followed by a concrete process-improvement question.
Who it's for
This template is for revenue, product marketing, and sales enablement teams investigating the reasoning behind win rates. It fits a quarterly win-loss review, a competitive positioning refresh, or repeated evaluations against the same competitor.
How the AI moderator probes
The opening questions establish recency, involvement, and outcome. When a buyer names a competitor, the moderator asks what evidence made that option stronger on a criterion the buyer raised unaided. If price appears, it distinguishes budget, commercial terms, and perceived value instead of treating “price” as a complete explanation.
The outcome branches keep wins, losses, retained status quo, internal builds, and no-decisions from being forced through the same wording. Add interviewer notes that ask for evidence and timing without steering people toward a favored competitor or pricing explanation. See AI follow-up questions for more on adaptive probing.
Tips before you launch
Replace the product and competitor placeholders before sending. Add forward branches from the outcome question to the matching probe, then an unconditional jump from each branch to the shared ease rating. Keep the trigger and decision-criteria questions identical across outcomes so themes remain comparable.
Interview within 30 to 60 days of the decision closing, before the specific comparisons fade. The recency and involvement questions should screen; the outcome question should route. How to write screener questions that actually screen covers the distinction. Analyze outcomes separately before looking for themes across the whole program.
Frequently asked questions
Who should you interview for a win-loss program?
Interview the primary decision maker or key evaluator from both closed-won and closed-lost deals, ideally within a few weeks of the decision while the evaluation criteria are still fresh.
Should wins and losses use separate templates?
One study can cover wins, losses, retained status quo, internal builds, and no-decisions when the outcome question routes participants to a distinct probe and rejoins at the shared evaluation measures.
Why use an AI-moderated interview instead of a survey for win-loss research?
A survey can record which vendor a buyer picked. An AI-moderated interview can follow an answer with a question such as "what made that option feel like less risk" and capture the reasoning behind the decision.
How many win-loss interviews do you need before patterns show up?
There is no fixed number. Keep interviewing within a single segment or deal size until new interviews stop surfacing new decision criteria or competitor objections, then review the themes as a batch.
Full reference
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