Promises Don't Close Deals – Verifiable Proof Does

01 /The Concept

The B2B buying process has a structural problem: buyers want proof from peers, and sellers rarely have the right proof ready at the right moment. UserEvidence was built to close that gap – collecting customer feedback, turning it into success stories, and deploying those stories as proof of value for new buyers.

The target audience is B2B software vendors selling relatively expensive products in competitive markets – companies with at least 500 existing customers, enough to generate a meaningful pool of stories worth mining.

The platform's mechanics are refreshingly simple.

Users create surveys on any topic, then distribute them through app integrations, email campaigns, or any other customer-facing channel.

Responses flow automatically into the platform's database, enriched with customer data pulled from the connected CRM.

At the core is a smart search layer. Sales reps can query it with requests like "show me customers who switched from Salesforce," "I need a healthcare success story from the Middle East," or "how many hours per week have customers saved since adopting the product?" The search surfaces the closest matches instantly.

That last point is critical: the people who need this most are salespeople. The more closely a success story mirrors a prospect's industry, company size, and use case, the more persuasive it becomes.

Marketers get a complementary workflow – one-click export of any story into branded visual blocks ready for landing pages, newsletters, or pitch decks.

UserEvidence was [covered previously](/review/takoe-prodajot-luchshe-chem-samoreklama) back in 2023. The company has since raised a new $7M round, bringing total funding to $21M.

02 /Why It Matters

Alongside the funding, UserEvidence acquired Zealot – a startup that, in the company's own words, "picks up where UserEvidence left off."

Where UserEvidence captures and surfaces success stories, Zealot handles the next step: connecting prospective buyers directly with existing customers for live peer conversations. New customers can hear firsthand – from someone who's already deployed the product – what the real experience looks like.

The acquisition closes the loop. B2B vendors can now not only surface the right success stories for prospects, but also set up direct conversations with the customers behind those stories.

The research behind this is compelling:

- 67% of buyers say ROI data from existing customers is the single most influential factor in their purchase decision.

- 90% of sellers admit they can't reliably provide prospects with the right peer references.

- As a direct result, 67% of deals are lost because the seller couldn't produce relevant proof from comparable customers in comparable situations.

The pain is real enough to have spawned an entire category of startups working the same angle.

Deeto ([related review](/review/novaya-era-b2b-prodazh-klienty-klientam)) raised $17M – $12.5M of it in early July – to build an AI engine that auto-pilots peer-matching: finding the right existing customer to talk to the right prospect without any manual coordination.

Champion ([related review](/review/neozhidannye-pomoshhniki-v-prodazhah)) pulled in $3.3M in its first round last autumn on a comparable platform.

SlashExperts ([related review](/review/samye-luchshie-prodazhniki-jeto-tvoi-klienty)) raised $2M in its first round in April on a similar peer-reference model.

03 /Opportunities

The obvious trajectory here is building platforms that systematically collect, organize, and deploy customer proof during sales cycles. But two details stand out.

Every platform mentioned here still relies on traditional form-based surveys. A [recent review](/review/bolshie-dengi-na-malenkoj-melochi) covered RowFlow, a Y Combinator company that replaces static forms with AI-powered "chat forms" – conversational interfaces that draw richer, more detailed answers out of respondents. RowFlow claims its chat forms double completion rates compared to standard forms. Plugging that into a peer-reference platform would mean twice as many stories collected, with significantly more depth.

The other gap: all these platforms currently live in B2B. That's partly because collecting peer stories is a hassle – the economics of doing it for low-ticket B2C purchases haven't made sense. Manually connecting buyers and sellers at B2C scale? Even harder.

But here's the counterintuitive angle: consumers already rely heavily on peer proof when making purchase decisions. We read reviews, weigh ratings, and mentally compute a kind of "expected ROI" even when buying a $30 kitchen gadget. The desire for authentic peer validation is just as strong in B2C as in B2B.

That's what makes CourseCrect ([related review](/review/kak-sdelat-pravilnyi)) so interesting – it built an AI that scours the internet for outcome data about course graduates (who got hired, who got promoted) and ranks courses by real results rather than star ratings.

If you layered a Deeto-style peer-matching engine on top of that – automatically connecting prospective buyers with people who already purchased and can speak to real outcomes – you'd have something genuinely powerful for B2C.

AI can make the process of gathering and surfacing authentic buyer proof cheap and transparent enough to work at B2C scale. That's a much bigger market – and wide open for whoever figures out how to do it right. Any thoughts on where to start?