Kea Insight / Analyst Relations

How Enterprise Buyers Build Trust Before They Talk to Sales

By Bram Weerts

Enterprise buyers start deciding whether to trust a technology vendor long before the first serious sales conversation.

By the time a buying team speaks to sales, it may already have formed a view from analyst research, customer evidence, peer reviews, consultants, search results, AI-generated summaries, prior experience and internal recommendations. The vendor still has influence, but it is joining a conversation that has already started.

This matters because many technology companies still behave as if trust begins when the account enters CRM. It does not. Buyers reduce uncertainty before they reveal intent. They compare claims, look for independent evidence, test whether other people recognise the same problem and decide which vendors deserve a place on the shortlist. Analyst Relations sits inside that wider trust system: important, but never operating alone.

Why enterprise buyers build trust before sales

Enterprise technology purchases carry risk. The buyer is not only choosing software; it may be committing budget, integration effort, security exposure, organisational change and personal credibility. That creates a basic requirement: reduce uncertainty before making the decision visible inside the organisation.

Vendor marketing can create awareness and explain a proposition, but buyers know the vendor controls that message. Trust therefore grows when important claims survive contact with sources the vendor does not control completely: an analyst’s market view, another customer’s experience, a consultant’s recommendation, a peer review, a technical community, a public case study or an internal colleague who has used the product before.

The process is not perfectly linear. A buyer can move backwards and forwards between these sources, return to the vendor, ask AI to summarise competing options and then validate the answer elsewhere. What matters is that the vendor is being interpreted across several environments at once.

That is why a market-perception problem can become a sales problem before sales sees it. If external sources still describe the company according to an old category, an outdated product limitation or the wrong competitive set, the buying team may remove the vendor before anybody gets the opportunity to correct the misunderstanding.

The signals buyers combine

No single source owns buyer trust. The useful way to think about the buying environment is as a set of overlapping signals, each answering a different question.

Vendor information: What does the company say it does, and can I understand the proposition quickly?
Analyst research and advisory: How does this provider fit into the market, what alternatives exist and what should I question?
Customer evidence: Has another organisation achieved something credible with the product in circumstances similar to mine?
Peer reviews: What do real users praise, dislike and repeatedly mention when the vendor is not controlling the conversation?
Consultants and partners: What does someone with implementation or market experience recommend in this situation?
Search and independent content: Does the wider information environment reinforce the vendor’s story or contradict it?
AI-generated answers: When a buyer asks a model to compare options, what version of the company does the model reconstruct from available evidence?
Internal experience: Has anybody inside the buying organisation used, rejected, shortlisted or heard of the vendor before?

These signals do not carry equal weight in every deal. A highly technical purchase may depend heavily on practitioner evidence. A board-visible transformation programme may create more demand for analyst or consultancy validation. A replacement purchase may be driven by prior experience. The point is not to build a universal attribution model. It is to recognise that trust is distributed.

Where industry analysts fit into buyer trust

Analysts matter because they help buyers interpret markets, not because they replace every other source. Depending on the firm and analyst, that influence may travel through published research, private inquiry, evaluation reports, market definitions, conference conversations or the language buyers use to compare different approaches.

That distinction is important when people try to measure analyst influence only by asking whether a buyer downloaded a report. A private advisory conversation can affect a shortlist without leaving a public trail. A market category can shape the questions in an RFP. An evaluation can give an unfamiliar vendor legitimacy, while an analyst’s caution can trigger more diligence rather than a simple yes or no.

At the same time, analyst influence should not be exaggerated. The 2026 TrustRadius B2B Buying Disconnect Report found that only 13% of the 1,862 technology buyers in its January 2026 sample said they used analyst reports in their purchase decision. That is a useful signal about this particular buyer population, but it is not a complete measure of analyst influence because it asks about report usage, not private advisory conversations or indirect market language.

This is why Kea’s position is deliberately wider than “analysts influence buyers”. Analysts are one part of the external evidence environment. The purpose of AR is to make sure relevant analysts understand the company accurately enough to discuss it in context, while the business strengthens the other proof buyers will encounter as well.

For a deeper argument on this point, see Before You Call Research Firms Irrelevant, Understand Where Their Influence Travels.

Why customer evidence and peer reviews matter

Customer proof answers a different question from analyst research. Analysts can explain a market and compare providers; customers show what happens when the technology meets an actual organisation. That is why credible customer evidence strengthens both buyer trust and analyst conversations.

The same 2026 TrustRadius study found that 74% of its surveyed technology buyers used reviews to inform a purchase decision. The sample comes from TrustRadius’s global network, so it should not be treated as a universal market statistic. It is still directionally useful because it shows how prominent customer-generated evidence has become in software research.

Reviews are powerful precisely because they are imperfect. Buyers can see recurring strengths, implementation friction, support experiences, missing capabilities and the language customers use when describing the problem. A page containing only flawless praise is less informative than a body of evidence that looks like real product use.

For vendors, the implication is not “collect more five-star reviews”. It is to understand what the evidence says. If customers repeatedly describe the product differently from the company’s chosen positioning, that is market information. If the strongest use case appears consistently in reviews but barely appears in analyst briefings, the company may be underusing its own proof.

This is also why customer stories, peer reviews and AR should not live in completely separate organisational boxes. The same evidence can help a buyer validate a decision, help an analyst test a vendor claim and help product or marketing understand where the company’s real credibility sits.

What AI changes—and what it does not

AI has accelerated the research layer of B2B buying. A buyer can now ask for a shortlist, compare vendors, summarise reviews, identify apparent strengths and weaknesses or generate questions for a sales meeting in seconds. That makes the information environment faster and less predictable.

But faster synthesis is not the same as trusted evidence. TrustRadius reported that 63% of buyers in its 2026 technology-buyer sample used AI somewhere in the purchase journey, while 94% of those AI users said they fact-checked the output at least some of the time. The important point is not the exact percentage. It is the behaviour: AI often sends the buyer back into the underlying evidence.

That changes the visibility problem for vendors. It is no longer enough for the company website to contain the correct story. The wider information ecosystem has to support it. Analyst research, customer reviews, public documentation, independent coverage and consistent market language can all become inputs into the answer a buyer sees.

AI also makes old market memory more executable. A stale description that once sat quietly in an old report, review or webpage can now be collected and summarised instantly. If the company has evolved but the external evidence has not, the buyer may receive a clean, confident summary of an outdated version of the business.

The answer is not to optimise every sentence for a model. It is to create stronger, more consistent evidence across the sources buyers already use and make sure important third parties understand what has genuinely changed.

What vendors should do differently

The first change is to stop treating pre-sales trust as a marketing-only problem. Product, customer success, Analyst Relations, communications, sales and leadership all contribute evidence that reaches the buyer before the opportunity becomes visible.

Start with the questions the buyer must answer before taking a vendor seriously. Does this company solve the problem we actually have? Is it credible in our environment? Who else uses it? What are the trade-offs? Is the category mature enough? What would an independent expert challenge? What happens after implementation? Which alternatives should we compare?

Then map the evidence. Which questions are answered convincingly on the website? Which are supported by customer proof? Which analysts can put the company into market context? What do peer reviews reinforce or contradict? What does search currently surface? What do the main AI systems appear to reconstruct when asked about the category? Where is the evidence thin?

Next, connect those gaps to ownership. If analysts keep challenging enterprise readiness, the answer may require product and customer evidence rather than another briefing. If reviews consistently mention one weakness, marketing should not simply write around it. If buyers do not understand why the company belongs in a category, AR and positioning work need to move together.

Finally, measure trust through observable movement rather than one attribution number. Look for stronger analyst understanding, better customer evidence, more consistent market language, fewer recurring sales objections, better shortlist visibility and documented cases where independent sources contributed to a buyer or internal decision. None proves sole causation. Together they show whether the market has a stronger basis for believing the company’s story.

Sales still matters enormously. A strong seller creates confidence, resolves objections and helps the buying group make a decision. But the seller is more effective when the buyer arrives with a credible version of the company already in mind.

That is the commercial point of Analyst Relations in the wider trust system: not to control what the market says, but to reduce the chance that buyers are making important decisions from an incomplete or outdated understanding of the business.

Sources: TrustRadius, 2026 B2B Buying Disconnect research, based on 1,862 technology buyers and 444 technology vendors surveyed in January 2026. Kea uses these findings as one buyer-research signal, not as a universal benchmark.

Put this into practice

Your buyers are forming a view before sales gets the meeting.

Kea helps B2B technology companies strengthen the analyst understanding, evidence and market signals buyers encounter before the sales conversation starts.

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