For a buyer who is convinced and scared: find what the champion stands to lose, shrink the first step until they could survive it failing, and put the product in their hands.
The buyer wants it and has said so. Then, late in the deal, everything stops.
When a convinced buyer goes quiet this late, the block is often fear of getting it wrong, not doubt about the problem, and arguing the case again makes it worse. Make the purchase safer instead. First find out what your champion personally stands to lose if it goes through: what they would be blamed for, what they would have to give up. Then cut the first step down until they could live through it going wrong: a pilot with success criteria and a price agreed before it starts, a phased rollout, an opt-out clause, a guarantee, or a choice between a committed price and a dearer contract they can leave. And put the product in the buyer's own hands, with the people who would have to run it in the room, because a buying group that cannot picture using the thing will not sign for it.
Finding what the champion is protecting. Robert Kegan and Lisa Lahey set out an interview in Harvard Business Review in November 2001 that surfaces what they call a competing commitment. You ask what the person would like to see changed, what commitment that implies, what they do or fail to do that works against it, what they fear when they imagine doing the opposite, and what outcome that behaviour protects them from. Their manager Tom wanted his team to bring him problems and punished whoever did. Underneath was a commitment not to hear about problems he could not fix. The small test that follows, one the person can back out of, is the point of the exercise, and their book Immunity to Change builds the method around it. No sales source publishes a version for a buying champion.
The safety nets the JOLT authors name. Matthew Dixon and Ted McKenna's summary of their method lists "trials, opt-outs, land-and-expands", and tells the seller to ask how to give the buyer a safety net at this stage and how to make it easier for them to succeed or harder for them to fail.
Pilots with prices on them. Monetizely lists five structures: a paid pilot at 10 to 30% of annual contract value, credited in full if they buy; a paid implementation for one department at 25 to 40% of the full cost; a money-back guarantee against success criteria agreed in advance; a small base fee with payments released at milestones; and a commitment priced phase by phase. Monetizely sells pricing advice and gives no source for the percentages. Dock's proof-of-concept template gives the plan three sections: the goals, approach and people on both sides; milestones with the success criteria both sides agreed; and the supporting material.
Contracts with a way out. Jason Lemkin names two structures: an annual contract that renews automatically with a 90-day out clause, or monthly billing with a volume commitment. In an earlier post he argues for letting any customer cancel at any time with a pro-rated refund. Offering both at once, a committed price and a dearer contract the buyer can leave, has an economic model behind it: in Pascal Courty and Li Hao's airline example, travellers who are unsure of their plans pay more for the refundable ticket and the sure ones take the cheap committed one.
A guarantee in writing. Todd Zielinski and Lisa Benson published a worked offer in AICC Now, the corrugated packaging association's magazine, on 30 August 2024: custom packaging in as little as two weeks, no minimum order, and for qualified customers a promise to "credit you back up to X% of your order each day it is late". It builds on the "unrefusable offer" in Eliyahu Goldratt's novel It's Not Luck.
Hands on, not a tour. Kyle Asay puts it as "no one buys something that they don't know how they'd use", and lists the questions a buying team has to answer before it signs: who will implement this, how will it connect to what they already run, what rules will govern its use. His three steps: drop the words "high-level overview" and build the demo around how you would use the product if you had the buyer's job; bring IT, project management and the other supporting teams in early; and hand the buyer the controls, in the demo or in a full proof of concept.
Fear, not doubt, in the call data. Dixon and McKenna report, from more than 2.5 million recorded sales conversations analysed on the platform of Tethr, which sells the analytics, that of the deals lost to indecision, 44% came from a preference for the status quo and 56% from fear of failure. On their book site they add that doubling down on the case against the status quo backfired 84% of the time, and that sellers who used their four behaviours reached nearly double the win rate. No method or control group is published, and the authors sell the book and the training.
Talking about safety nets goes with winning, in Gong's data. Chris Orlob reported for Gong in January 2017 that across 25,537 sales conversations, win rates were about 32% higher where sellers talked about easy cancellation, 90-day opt-outs, fast implementation, no long-term contract or money-back guarantees, and that cancellation rates rose about one point where they did. Gong sells the software that recorded the calls, and the figure counts words said, not terms signed.
What buyers say they want. G2's 2026 Buyer Behavior Report found that buyers who had lived through a late-stage veto pushed for contracts shorter than twelve months at 40% against 18% of other buyers, and that the share preferring prices tied to outcomes rose from 11% to 23% in a year. TrustRadius's survey of 1,604 technology buyers in 2023 found that 77% named free trials among the three sources that most influenced their decision, up from 67% the year before. G2 and TrustRadius both sell to software vendors, and these are preferences reported after the fact, not deals won or lost.
Winning demos look like the buyer's own problem. Gong's analysis of 67,149 recorded demos found that winning demos followed the order of topics from the discovery call and opened with the problem the buyer had spent most time on, and that no demo that closed contained more than 76 seconds of uninterrupted pitching. That measures how a demo is paced, not whether the buyer can picture the product in their own building.
The pilot figure has no source. Monetizely cites "a 2023 Forrester study" putting pilots with criteria agreed in advance at 3.2 times the conversion of open-ended evaluations, and links neither the study nor a method. Nobody has compared deals that got a safer first step with deals that did not.
Unknown odds feel worse than bad odds. Daniel Ellsberg showed in the Quarterly Journal of Economics in 1961 that people pay to avoid ambiguity, preferring a bet with known odds to one with unknown odds even when the known odds are no better. A buyer who does not know how often this works is in a worse place than one who knows it works two times in three, which is why real base rates, failures included, lower the fear.
Using a product predicts buying it; watching it does not. Robert Smith and William Swinyard found in the Journal of Marketing Research in 1983 that attitudes formed by trying a product predicted purchase well, and attitudes formed from advertising much less. Joann Peck and Suzanne Shu found in 2009 that merely touching an object raises the feeling of owning it. Viswanath Venkatesh followed 246 employees in three organisations and found that their early view of how easy a new system was rested on their general feelings about computers, and adjusted to the real system only once they had used it.
It convinces the people who touched it, and stops there. Hugh Waddington and colleagues' review of farmer field schools, 92 evaluations, found that farmers who took part adopted the practices and gained roughly 13% in yields and 20% in net revenue, with no convincing evidence that any of it reached neighbours who had not taken part.
Pricing on results is easy to propose and hard to run. Of 84 risk-sharing agreements for medicines identified in Portugal, Gonçalves and colleagues report that 74 were purely financial and only two rested on clinical outcomes, and whether any met their goals is unknown. On the priced exit, Diego Escobari and Paan Jindapon's study of US airline fares supports Courty and Li's model: the gap between refundable and non-refundable fares narrows as travellers learn what the trip is worth to them. Both are a long way from a buying group.
Nobody has compared deals that got a safer first step, or a hands-on trial, with deals that did not. The B2B figures are vendor correlations and buyer self-reports, and the strongest outside findings come from consumers, employees trained on a system already bought, and farmers. The field schools carry the warning that matters most here: what the person who touched the product now believes does not travel by itself to the colleagues who did not.
For a buyer who is convinced and scared, stop arguing the case. Ask what the champion stands to lose, shrink the first step until they could survive it failing, give real base rates with the failures in them, and put the product in the hands of everyone whose approval you need. Each piece has support somewhere; the combination has never been tested in a deal.