How buyers use ChatGPT in the vendor research process

TL;DR: B2B buyers open ChatGPT before they open your website. They use it to define requirements, build shortlists, compare features, and check what a sales rep told them. Brands the model doesn't mention are invisible to this part of the funnel. Knowing the exact questions buyers type tells you exactly what content to publish.

Why are buyers using ChatGPT to research vendors at all?

Buyers aren't using ChatGPT because it's trendy. They're using it because the old path got exhausting. Typing a category into Google and sorting through paid placements and vendor-written comparison posts is slow work, and buyers know most of what they're reading was written to sell them something.

Forrester found that B2B buyers finish about 70% of their research before they ever contact a vendor [1]. ChatGPT slots right into that self-directed phase because it feels like asking a smart colleague, not searching a library. The buyer can start vague, get oriented, and narrow down without booking a demo call or handing over an email address.

There's a trust dimension too. Buyers distrust anything a vendor wrote about itself, and reasonably so. An AI that pulls from many sources feels more neutral, even though sophisticated buyers know that neutrality is partly an illusion. The perception drives real behavior anyway.

Some buyers now spend more time with ChatGPT than with your website before the first sales call. If your brand isn't in those conversations, you're losing conversations you can't even see.

What does the typical buyer journey look like with ChatGPT involved?

The journey isn't uniform, but a pattern shows up again and again in behavior research. Think of four rough stages, each with its own kind of question.

First, orientation. The buyer has a problem they can't quite name. They ask something like "what should I look for in a contract management platform" or "what's the difference between a CDP and a CRM." This stage builds vocabulary and defines the category. No vendors get named yet.

Second, shortlist generation. Now the buyer has a mental model and wants names. "What are the best contract management tools for a 200-person professional services firm" is a real example of the query type. ChatGPT returns a list, usually 4 to 8 names, each with a short description. Visibility here is binary. You're on the list or you're not.

Third, comparison and due diligence. The buyer picks two to five vendors and digs in: "compare Ironclad and Contractbook for a team that needs Salesforce integration" or "what do customers complain about with [vendor]." Sentiment and known weaknesses surface here.

Fourth, verification. Often after a demo, buyers go back to ChatGPT to check claims. "Does [vendor] actually support SSO" and "what's the typical implementation time for [platform]" are common. When the answer contradicts what a rep said, you've got friction.

Gartner reports that B2B buyers now consult an average of 10 information sources before deciding [2]. ChatGPT has become one of those sources, and it shows up hardest in the first and third stages.

What specific questions do buyers ask ChatGPT about vendors?

This is the section your content calendar should steal from. The questions cluster into five predictable groups, and each group needs different content to influence the answer.

Category definition questions These set the frame before any vendor is named:

• "What is the difference between a DAM and a CMS?"
• "What does a good data observability tool actually do?"
• "What features separate enterprise HR platforms from SMB ones?"

Shortlist and discovery questions Here you either appear or you don't:

• "What are the top [category] tools for [industry or company size]?"
• "Which [category] vendors are known for strong customer support?"
• "What [category] platforms have SOC 2 Type II certification?"

Comparison questions These demand nuanced knowledge of your positioning:

• "Compare [Vendor A] vs [Vendor B] on implementation complexity"
• "What does [Vendor A] do better than [Vendor B]?"
• "Which is better for [specific use case], [Vendor A] or [Vendor B]?"

Weakness and risk questions Buyers hunt for problems before they commit:

• "What are the most common complaints about [Vendor]?"
• "What should I watch out for in [Vendor]'s contract?"
• "Has [Vendor] had any security incidents?"

Verification questions These come after vendor contact:

• "Does [Vendor] support multi-tenancy?"
• "What programming languages does [Vendor]'s API support?"
• "What's [Vendor]'s uptime SLA?"

Shortlist questions need broad brand authority and tight category association. Comparison questions need detailed, factual content about your product. Weakness questions need you to publish honest content about your own tradeoffs before a critic writes it for you.

At which buying stage do B2B buyers use AI tools (including ChatGPT)?

| | | |---|---| | Early research / orientation | 67% | | Vendor evaluation / comparison | 34% | | Final selection / negotiation | 18% |

Source: Demand Gen Report, B2B Buyer Behavior Survey, 2024

How does ChatGPT decide which vendors to recommend?

ChatGPT doesn't work like a search engine, and that trips up a lot of marketing teams. It doesn't crawl the web in real time or rank pages by backlinks. It generates answers from patterns in training data: web text, forums, review sites, documentation, and much more.

The implication is direct. Your visibility in AI answers depends on how much high-quality, specific, consistent text about your brand existed on the internet before the model's training cutoff. Brands written about widely on G2, Reddit, industry blogs, and their own docs get mentioned. Brands that live only on their marketing pages don't.

A few factors seem to matter most, based on research into AI citation patterns [3]:

1. Category association density. How often does your brand name appear next to the category name across the web? When thousands of pages say "[Vendor] is a [category] tool," the model has strong evidence for that link.

2. Specific factual claims. Models mention vendors more readily when they can attach hard facts: pricing range, notable customers, named integrations, compliance certifications. Vague claims don't survive training.

3. Third-party mentions. Your own content counts for less than independent sources. Review sites, analyst reports, forum threads where a stranger recommends you by name, press coverage. These carry more weight.

4. Recency, for retrieval-augmented systems. Perplexity and ChatGPT with browsing pull current content. For those queries the rules shift back toward traditional SEO and freshness. For base GPT-4o queries without browsing, training data rules.

Generative engine optimization gives you a framework for treating these factors as things you can change, more than things you observe.

Does ChatGPT use reviews and third-party sites to form vendor opinions?

Yes, and it's one of the most underrated dynamics in the whole system. G2, Capterra, Reddit, Quora, industry forums, and independent analyst blogs are heavily represented in large language model training data. When a buyer asks "what do customers complain about with [Vendor]," the model is drawing on patterns from thousands of review samples and forum threads.

BrightEdge found that Google's AI Overviews cited third-party sources more than 60% of the time for commercial queries [4]. That's a different system than ChatGPT, but the underlying behavior is the same: models trust distributed, multi-source corroboration over a single vendor's claims.

Here's what that means. A vendor with 400 detailed G2 reviews beats a vendor with a gorgeous website and 20 reviews, all else equal. The reviews don't need to be uniformly glowing. Models pick up nuance. A review that says "the onboarding is rough but the product is worth it" can read as more trustworthy than a wall of suspiciously perfect five-star ratings.

Negative patterns accumulate though. If your brand carries a consistent complaint (slow support, thin docs, billing errors) across many reviews, that pattern lands in AI answers. You can't SEO your way out of a product problem.

So treat third-party review content as a core part of your strategy. Ask happy customers for detailed, specific reviews. Respond to the negative ones on the platform. All of it becomes training signal.

At what stage of the buying process does ChatGPT have the most influence?

The top of the funnel, specifically the shortlist and category-definition stages, is where ChatGPT carries the most weight and where brands have the most to gain or lose. By the time a buyer is negotiating, they've moved into human-sourced validation: references, security questionnaires, legal review. ChatGPT plays a smaller direct role there.

The shortlist stage is brutal in its simplicity. A buyer asks for recommendations. The model names 5 to 8 vendors. Those vendors get demo requests. The ones it skips don't. There's no position four that still gets a click. You're in the answer or you're gone.

A 2024 Demand Gen Report survey found that 67% of B2B buyers said they use AI tools including ChatGPT during early research, compared to 34% during vendor evaluation and 18% during final selection [5]. The early-stage concentration fits how the tool actually gets used.

The middle stages still count. If a buyer walks out of a demo and asks ChatGPT to validate a claim, and the model gives an uncertain or contradictory answer, that plants doubt. So being consistently and accurately represented across the model's knowledge matters, not only at the very top.

To measure where you stand, the ai search visibility metrics kpis guide covers what to track and how.

How do buyers verify or push back on what ChatGPT tells them about vendors?

Sophisticated buyers don't swallow ChatGPT's answers whole, and that behavior shapes how you should think about the whole system.

The common pattern: the buyer gets a shortlist from ChatGPT, then cross-references it against G2 category pages, asks a peer on Slack or LinkedIn who they use, glances at a vendor's LinkedIn follower count as a rough size signal, and reads 10 to 15 reviews. ChatGPT is the first filter, not the last word.

Buyers also probe the model for uncertainty. "How confident are you about this" and "is this information current" are routine now. ChatGPT's own habit of hedging ("as of my knowledge cutoff" or "you should verify this directly") trains buyers to treat it as a starting point.

Some buyers ask the model to argue against a choice they already like: "give me the best arguments against choosing [Vendor]." That's a verification move, built to surface risks before a decision.

For vendors, the takeaway is that your representation has to hold up across many phrasings and question types. If you show up well on "best [category] tools" but the model goes quiet when asked about your integrations or your security certifications, that gap is exactly what a careful buyer will notice.

What content helps a vendor get recommended by ChatGPT?

AI visibility content differs from traditional SEO in emphasis, though not entirely in mechanics. Traditional SEO chases rankings for individual pages against target keywords. AI visibility builds a body of factual, specific, consistent information about your brand that a model can absorb during training and a RAG system can retrieve on the fly.

Here's what actually moves the needle:

Factual product documentation in plain language. Integration lists, supported file formats, compliance certifications, pricing tiers (or at least ranges), API capabilities. This is the specific information models attach to brand names. "Enterprise-grade security" contributes nothing. "SOC 2 Type II certified, HIPAA compliant, AES-256 encryption at rest" is a fact a model can repeat.

Independent coverage that names your brand and category together. Press mentions, analyst reports, guest posts, podcast appearances where the host says "[Vendor] is a [category] tool that does X." This association density tells the model you're a real player in the category.

Review volume and specificity on G2, Capterra, and category-specific sites. Nudge customers to name specific use cases, integrations, and outcomes. "Great product" helps less than "we use it for SOW automation and the Salesforce sync saves us about 3 hours a week."

Answered questions on forums. Reddit, Quora, and Stack Overflow content is well-represented in training data. When someone asks about your category on Reddit and your brand comes up naturally in a real reply, that's signal.

Honest comparison content. A "[Vendor] vs [Competitor]: an honest look" page that admits your weaknesses performs better in AI systems than a one-sided puff piece, because it matches the nuance of independent reviews.

For a practical toolkit, ai seo tools and ai visibility tool both cover how to audit and build this presence. Spawned's AI visibility audit gives you a structured read on where you stand across the major models before you commit to a content plan.

How is this different from how buyers used Google during vendor research?

The differences are real, and they're easy to overstate. Let's be precise.

With Google, the buyer types a query, sees 10 blue links, clicks a few, reads, and forms a view. Each source is attributed, so the buyer can judge it. They know a G2 page is a review site. They know a vendor's own page is self-serving. Source literacy stays intact.

With ChatGPT, the answer arrives pre-synthesized. Base responses often show no sources at all. The buyer gets a confident summary that may have blended vendor marketing copy, a stale forum post, and review text as if they carried equal weight. Source literacy gets much harder to apply.

That changes a few things. Being confidently named by ChatGPT is worth more than a Google rank-one spot, because there's no visible alternative sitting right next to you. And errors or stale facts are harder for buyers to catch. A model that never learned about your 2024 product pivot will describe your old positioning, and the buyer has no obvious tell that the information is out of date.

The pace differs too. A Google session might mean 20 clicks over an hour. A ChatGPT session covering the same ground can hit equivalent breadth in 10 minutes, with more follow-up questions and less source-checking. Buyers reach a conclusion faster, so your window to influence them is shorter.

What stays the same: buyers are still skeptical, still talk to peers, still read reviews. ChatGPT is a new node in the research graph, not a replacement for the graph. Treating it as the only thing that matters is as wrong as ignoring it.

For the bigger picture on how ai search is reshaping commercial research, that's worth reading alongside this piece.

Can small or newer vendors get recommended by ChatGPT, or is it dominated by established brands?

There's a real incumbency bias in AI-generated vendor lists, and it's worth being honest about. Models trained on historical web data reflect the brands that dominated that data. Salesforce has a decade of being named in every CRM article, guide, and forum thread, so it shows up in CRM recommendations reliably. A startup that launched 18 months ago has a sliver of that footprint.

The bias isn't total, though. Newer vendors break through in a few ways.

Niche specificity. Ask "what are the best contract management tools for independent legal practices" and a niche vendor with strong coverage in legal forums and publications can beat a generalist incumbent on that narrow query.

Recent coverage for RAG-enabled systems. For ChatGPT queries that browse the web, and for Perplexity, recency matters. A vendor with fresh press, a Product Hunt launch, or a surge of new reviews can surface in retrieval-augmented answers even when training data is thin.

Category creation. A vendor that genuinely originated or defined a subcategory often owns that subcategory in AI answers, even while small, because they wrote the content that named the category.

So the advice for smaller vendors is simple: don't fight for the broad generic query. Get mentioned specifically, accurately, and often in the narrow subcategory and use-case context where you actually win deals. That's where you can build enough signal to appear.

And get your facts right in the places models draw from. A small vendor with accurate, detailed G2 listings and a clear documentation site has more AI visibility than a small vendor hiding behind a slick homepage.

What are the risks for buyers who rely on ChatGPT for vendor research?

This matters for vendors too, because it shapes how much buyers trust AI answers and how they read them. The main risks are hallucination, staleness, and training bias.

Hallucination is when the model states something false with total confidence. In vendor research that looks like ChatGPT naming a feature your product doesn't have, citing a customer that never used you, or quoting a pricing tier that doesn't exist. Stanford's Human-Centered AI Institute has documented hallucination rates in commercial LLM applications ranging from 3% to over 20% depending on query type and model [6]. For specific product facts, the rates sit toward the higher end.

Staleness is when a fact that was accurate at training time is now wrong. A vendor that switched from usage-based to seat-based pricing, or got acquired, or killed a major integration, gets described in outdated terms. The model has no way to know what it doesn't know.

Training bias reflects whatever was overrepresented in the corpus. If most CRM coverage in the training data focused on enterprise use cases, the model may quietly underrecommend tools built for SMBs, not because they're worse, but because the coverage never captured them.

For vendors, these risks are an opening. Publish clear, current, specific information across third-party sources and you're less likely to be misrepresented. You lower the odds of a bad hallucination by making accurate information easier for the model to learn in the first place.

How should marketing teams measure whether they're winning in AI vendor research?

Measuring AI visibility is harder than measuring search rank, and anyone who tells you it's easy is selling something too simple. There are practical proxy metrics that work.

The most direct method is prompt testing. Query ChatGPT, Claude, Gemini, and Perplexity with the top 20 to 30 vendor research prompts in your category. Record where your brand appears, what gets said, and how you're positioned against competitors. Run it weekly or monthly and track the change over time. It's manual at small scale and becomes a tooling problem at large scale.

Brand mention rate. Across a fixed set of category and use-case queries, what share include your brand name? A brand that appears in 12 of 25 queries is better positioned than one appearing in 3. Track it over time and against rivals.

Sentiment and positioning accuracy. When you're mentioned, is the description right? Are your differentiators named? Are your known weaknesses stated fairly? Errors here are content gaps you can close.

Lead source self-report. Add "how did you first hear about us" to your demo form with "AI assistant (ChatGPT, etc.)" as an option. The data is noisy because people forget, but the trend line over 6 to 12 months tells you something real.

Dark funnel signals. Prospects who already know your category positioning, your integrations, and your rough pricing before the first call, without having visited your site, are probably ChatGPT-informed. Train your sales team to ask.

For a fuller methodology, the ai search visibility metrics kpis guide and brandrank.ai visibility insights analysis both go deeper. Spawned's platform automates the prompt-testing layer if you're running this across multiple categories or models.

Sources

1. Forrester Research, "Death of a (B2B) Salesman" report summary
2. Gartner, B2B Buying Journey research
3. Search Engine Journal, AI citation and brand visibility patterns research overview
4. BrightEdge, AI Search Trends and Citation Behavior Report 2024
5. Demand Gen Report, B2B Buyer Behavior Survey 2024
6. Stanford Human-Centered AI Institute, AI Index Report
7. MIT Sloan Management Review, AI in B2B Sales and Marketing
8. G2, State of Software Buying Report
9. Perplexity AI, product documentation on retrieval-augmented generation