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Qualitate named in the 2026 Gartner® Market Guide for Win/Loss Analysis Solutions
The limitations of traditional win/loss analysis
A deal closes, someone marks a reason in Salesforce, and months later a team tries to reconstruct what actually happened. They might interview a few buyers or read call transcripts, then put the findings into a deck and do it again next quarter.
The challenge is that seller-reported reasons for winning or losing a deal often provide an incomplete account of the buying process. Sales teams may not have visibility into how buyers evaluated competing vendors, which product capabilities influenced their decisions, or what internal considerations ultimately determined the outcome.
Direct buyer interviews provide additional context, but when conducted infrequently or across a limited number of deals, it can be difficult to identify consistent patterns or track how purchasing criteria change over time.
Gartner notes:
“Marketing and sales leaders continue to express skepticism about the accuracy and completeness of seller-submitted win/loss data. Third-party-led interviews and surveys with buyers and customers remain essential for establishing best practices and ensuring unbiased, actionable win/loss insights.”
The quality of a win/loss program comes down to the evidence underneath it. Teams need to talk to the people who made the decision, with enough of those conversations to see what is happening beyond one deal.
AI is moving into the research process itself
AI is moving beyond transcript summarization and into the research process itself.
As Gartner writes in the 2026 Market Guide:
“AI — including generative and agentic AI — has become embedded throughout the win/loss process, powering adaptive interviews, automated analysis, real-time reporting and integration with enterprise AI assistants.”
This is beginning to change the economics of primary research.
Historically, getting real depth meant scheduling expensive one-hour interviews, one at a time. Getting scale meant sending a survey and giving up a lot of the follow-up that makes qualitative research useful.
AI moderation gives teams another option. A buyer can answer by voice on their own schedule. The moderator can listen to the answer, ask the next question based on what the buyer actually said, and keep going when something is worth pursuing. And because those interviews can happen in parallel, you can reach a much larger and more precise buyer population without turning the project into a months-long exercise.
That is how we built Qualitate. Our AI Interviewer conducts the interviews, adapts in real time and probes where more detail is needed. Leveraging our vetted proprietary expert panel, Qualitate sources and validate the right people. The platform then structures every conversation so teams can compare what buyers are saying across companies, competitors, products, and time.
The real value shows up when the research compounds
Most win/loss programs start from zero every time. You decide what you want to study, recruit the buyers, run the interviews, analyze the results, and publish the findings. Six months later, the next project starts again.
We built Qualitate around a different operating model. We already conduct thousands of buyer interviews every month. Those conversations are structured using a common taxonomy, so a team can isolate a company, buyer profile, evaluation outcome or spend direction and immediately see the evidence that already exists.
If you are looking at Snowflake, for example, you can separate buyers who are increasing spend from buyers who are decreasing spend, replacing the product or choosing something else. You can see which competitors are benefiting and what buyers say drove the decision. Every finding links back to the underlying conversation.
Then, when the existing evidence raises a new question, you can launch a custom study against that exact buyer population. A typical Qualitate project delivers roughly 20 to 25 interviews in three to 10 business days. Those private interviews stay private to the customer.
Over time, each study can build on what came before it. The evidence you already have helps identify where to look next, and new research answers the questions that follow. The findings become part of a body of research your team can keep working with beyond the initial project.
Traditional win/loss analysis is replaced by continuous buyer intelligence
Traditional win/loss programs focus on understanding why individual deals were won or lost. But the value of buyer research extends beyond those individual decisions.
Most programs are limited by the number of interviews they can conduct. Expanding the volume of research, including buyers never evaluated a company but chose a competitor, provides a more complete picture of the market.
- Which competitors are gaining consideration?
- Which product capabilities actually change the buying decision?
- Where is pricing becoming a problem?
- Which workloads are moving from one vendor to another?
- Why are customers increasing or decreasing spend?
- Are those patterns changing over time?
This is where win/loss research begins to inform broader buyer intelligence.
By capturing buyer decisions consistently and comparing findings across a larger sample over time, the same research can support product strategy, competitive intelligence, sales, customer success, and investment decisions.
The opportunity is to move from periodic analysis of individual deals to continuous research into how buyers evaluate vendors and make purchasing decisions.
From a one-off project to an always-on program
Gartner states: “Plan for scalability and ongoing value: Consider whether you need a one-time project or an ‘always-on’ program, and evaluate the vendor’s ability to scale with your needs and support continuous improvement.”
Companies are going to keep running discrete win/loss studies when they have a specific question. But the teams that do this well will also maintain a live body of buyer evidence they can query whenever a new competitor shows up, a product launch misses, a segment starts churning, or the market changes faster than their internal data can explain.
You should be able to ask what changed, inspect the source conversations, and commission fresh research when the evidence is not there yet.
That is the product we have been building.
Listen to your market
See why buyers choose you, why they choose competitors, and what is changing across your market.
GARTNER ATTRIBUTION / COMPLIANCE NOTE
Gartner, Market Guide for Win/Loss Analysis Solutions, Rahim Kaba, Dan Tolan, Liz Pellegrini, 1 October 2026.
Gartner is a trademark of Gartner, Inc. and/or its affiliates.
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