Bain chose NewtonX to conduct the research behind a new metric for predicting B2B deal wins.

The research revealed why most B2B vendors are already losing before formal evaluation begins, and gave Bain the evidence to prove it.

This metric stands apart because it focuses on decisions that actual buyers make in head-to-head comparisons… Likelihood to Buy turns growth from guesswork into precision, linking targeted actions directly to higher win rates and tangible financial results.

Most B2B vendors assume they lose deals in the final stages. Bain saw a different problem: many losses are effectively decided before the formal evaluation begins. To test that idea, Bain needed evidence from verified buyers showing which vendors made the initial list, which ones won, and what shaped the decision. NewtonX conducted the survey behind Bain’s Likelihood to Buy^SM metric, giving the firm a sharper way to link buyer perception to commercial performance.

A better way to understand why deals are won

Bain’s idea started with a simple point. A vendor has to make the buyer’s Day 1 list before it has any real chance to win. From there, it has to earn confidence across the full buying committee. Likelihood to Buy^SM was designed to measure both. Instead of treating awareness, leads, or pipeline coverage as proxies for demand, the metric focused on whether a vendor was considered at all and how well it met the needs of the people shaping the decision.

That distinction matters because the buying committee is more complex than many go-to-market teams assume. Bain and LinkedIn’s analysis found that B2B decisions are shaped by both target buyers and hidden buyers, with hidden buyers accounting for half of the influence over the Day 1 list decision. Those stakeholders are not simply checking features or price. Brand reputation, trust, and perceived risk all play a meaningful role in who gets considered and who ultimately wins.

For B2B marketing and product marketing teams, that matters because it shifts the focus from broad awareness to the specific messages, proof points, and signals that earn consideration early in the buying process. It also makes clear that strong positioning has to work across the full committee, not only with the most visible buyer.

Research built around the buying committee

The quality of the research design was central to the value of the work. Bain did not need broad market sentiment or surface-level brand familiarity. It needed verified professionals, not panels, and it needed those respondents to include both target and hidden buyers who had recently bought in the category, including both target buyers and hidden buyers. Powered by NewtonX Graph and Expert Surveys, the study grounded Bain’s insights in actual purchase decisions rather than abstract intent.

Bain used the NewtonX Graph to survey 750 buyers across two enterprise software sectors: human capital management and cloud data platforms. The study asked which brands made the Day 1 list, which vendor respondents ultimately chose, and how five decision themes shaped the outcome.

Those themes reflected the factors that consistently matter in real purchase decisions: confidence in meeting current and future needs, value relative to total cost, being a safe internal choice, confidence in implementation, and ease of working with the vendor. Bain then translated those responses into a score that predicts win rates against named competitors and shows where a vendor falls short with both target and hidden buyers.

From diagnosis to action

The research helped Bain make a critical commercial problem visible: companies often do not know whether they are losing because buyers never consider them, because they fail to build confidence once considered, or because both problems are happening at once.

That distinction matters. A visibility problem calls for a different strategy than a confidence problem. A vendor that is not making the Day 1 list may need sharper positioning, broader reach across the buying committee, or stronger signals of relevance before the sales process begins. A vendor that makes the list but loses later may need better proof, a clearer value story, stronger implementation evidence, or a more defensible case for internal stakeholders.

For product marketers, the research made those trade-offs much easier to see. It showed whether the gap was awareness, value communication, internal defensibility, implementation confidence, or some combination of the four.

The result was not just a data set or a publishable idea. It was a commercial diagnostic Bain could use with leadership teams to get to the root of why deals were being won or lost, then translate that diagnostic into practical strategies for sales, marketing, and product teams.

That level of clarity matters because the research shows that around 90% of buyers purchase from their Day 1 list. Once Bain could map vendor performance against shortlist presence and buyer confidence, the next moves became much more practical. Teams could strengthen proof earlier in the journey, sharpen the value story, reduce perceived implementation risk, and align sales, marketing, and product around a short list of weaknesses that actually change outcomes.

A more credible way to talk about growth

For Bain, the research did more than support a publishable point of view. It gave Likelihood to Buy^SM the evidence base to function as a practical tool for go-to-market strategy, sales and marketing planning, product direction, and commercial due diligence.

The value was in getting closer to the real reasons behind commercial performance. Why do some vendors make the first list while others do not? Why do some brands inspire confidence across the committee while others create hesitation? And what should teams change once they know the answer?

Because the evidence came from verified buyer data, the argument landed with more precision. It gave Bain data it could trace back to real buying behavior and findings leadership teams could defend. Growth is not simply a matter of closing harder. It starts earlier, with getting on the Day 1 list and building confidence where it counts.

The strongest thought leadership does more than name a trend or introduce a new metric. It helps leaders see a problem clearly enough to act on it. That is what Bain build with NewtonX buyer data: a better way to understand how buyers make decisions, and a more practical way to improve the commercial system around those decisions.

What Bain has built with Likelihood to Buy has the potential to do for B2B growth what NPS did for customer loyalty: give commercial teams a single, predictive signal worth building strategy around. That kind of framework only holds up when the research behind it is airtight. Verified buyers, real purchase decisions, no panels. That is the standard NewtonX brings to every engagement, and it is what gives this work its credibility in the market.
— Leon Mishkis, COO, NewtonX

Footnote: Likelihood to Buy ^SM is a service mark of Bain & Company, Inc.