Should I still use SEMrush and Ahrefs to track competitors, or are those tools outdated now?

They're still useful but no longer sufficient. Tools like SEMrush and Ahrefs measure traditional search visibility, which is a shrinking slice of how B2B buyers actually research vendors today. A competitor with a thin backlink profile can still outrank you inside ChatGPT or Perplexity if their content is structured for AI extraction. You need to layer AI platform research on top of your existing tools, not replace one with the other.

How do I find out if my competitors are showing up in ChatGPT when people search for services like mine in Miami?

Build a set of 20 to 40 prompts that mirror real buyer questions, such as 'best marketing automation consultants in Miami' or 'top martech firms near downtown Miami.' Run those prompts through ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews with your location set to your target market. Document which competitor names appear, how they're framed, and which sources the AI cites. That citation list tells you exactly where your rivals are earning their AI visibility.

What's the difference between a competitor's AI visibility and their regular search ranking?

Traditional search rankings measure how well a site performs in Google's blue-link results, driven largely by domain authority and backlinks. AI visibility measures how often and how favorably a brand gets named when someone asks an AI platform a conversational question. A company with weak SEO can still dominate AI responses if their brand signals are dense across the open web and their content is easy for models to extract and cite.

What are the most important things to track when I'm analyzing how a competitor shows up in AI search results?

The article recommends scoring competitors on four dimensions: mention frequency (how often they appear across your full prompt set), citation sources (which specific websites are driving the AI to recommend them), framing (whether the AI presents them positively, neutrally, or dismissively), and the types of queries where they surface. The citation sources are especially valuable because they reveal exactly which publications, directories, or platforms you need to target to close the visibility gap.

Competitive Research in the AI Search Era

You used to know exactly where your competitors stood. A quick Google search, a peek at their backlinks, a glance at their paid keywords — done. You had a clear picture of who was eating your lunch and why.

That picture is now incomplete. When a prospect asks ChatGPT for the best martech consultancy in their city, the answer doesn't come from page one of Google. It comes from a synthesized response pulled across dozens of sources — and your competitor might be the only name mentioned.

If you're not researching how rivals show up inside AI platforms, you're studying yesterday's battlefield. Here's how to do competitive research in the AI search era — specifically for marketing technology businesses.

Why Traditional Competitor Analysis Falls Short Now

SEMrush, Ahrefs, and SimilarWeb still matter. But they measure a shrinking slice of buyer behavior.

Today, a growing share of B2B research starts inside ChatGPT, Perplexity, Claude, and Google's AI Overviews. Buyers ask conversational questions — "who's the best HubSpot implementation partner in my market?" — and act on the names the model surfaces. They rarely click through to a SERP at all.

That means your competitor's domain authority is no longer the leading indicator. Their AI visibility is. A rival with a thin backlink profile can still get cited above you in ChatGPT if their content is structured for extraction and their brand signals are dense across the open web.

Marketing technology buyers are especially susceptible to this shift — they're early adopters, they use AI tools daily, and they trust what the model tells them.

How to Research Competitors in ChatGPT and Other AI Platforms

Start with the questions your buyers actually ask. Then run those questions through each major AI platform and document what comes back.

Step 1: Build Your Prompt Set

Write 20 to 40 prompts that mirror real buyer intent. For a martech firm, that looks like:

"Best marketing automation consultants in [your city]"
"Who can help my SaaS company implement Salesforce in my region?"
"Top AEO agencies serving local businesses"
"How do I choose a martech partner?"
"Marketing technology firms near downtown [your market]"

Mix branded queries, category queries, and problem-first queries. The problem-first ones — "my CRM and marketing platform aren't talking, who can fix this?" — are where most buyers actually live.

Step 2: Run the Prompts Across Platforms

Test each prompt in ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Run them logged in and logged out. Run them on mobile and desktop. Run them with location set to your target market.

Record three things for every response: which competitors get named, the context in which they're framed (positively, neutrally, dismissively), and which sources the AI cites.

Step 3: Map Citation Sources

This is the part most marketers skip. The sources cited by Perplexity and Google AI Overviews tell you exactly which sites are influencing the model's output.

If your competitor gets recommended because of a Clutch profile, a local business feature, and a guest post on a martech blog — now you know where to invest. AI search competitive intelligence lives in the citation graph, not the link graph.

What to Look For: The Four Visibility Signals

When analyzing competitors on AI platforms, score them on four dimensions.

1. Mention Frequency

How often does the competitor appear across your prompt set? A rival named in the majority of your prompts has dominant share of model voice. One named in only a handful does not.

2. Mention Context

Are they described as a generalist, a specialist, an enterprise option, a boutique? AI platforms assign frames to brands based on what the open web says about them. If your competitor is consistently framed as "the HubSpot specialist for local startups," that's the position they own.

3. Source Authority

Look at which domains the AI cites when it mentions them. G2, Capterra, Reddit, niche industry blogs, and local publications — each carries different weight in different models.

4. Geographic Specificity

Does the model associate them with your city, your broader region, or with no location at all? Geographic anchoring matters in markets where buyers explicitly want local partners who understand the regional customer base and business environment.

The Local Layer Most Marketers Miss

Local martech markets have quirks that AI platforms pick up on — and you should too.

Seasonal business rhythms influence buyer behavior in visible ways. Procurement cycles compress and stretch with local patterns. If your competitor publishes market-specific content tied to those rhythms, AI models learn that association.

Multilingual markets matter as well. In regions with significant non-English-speaking buyer populations, a meaningful share of queries happen in other languages or in mixed-language prompts. Competitors with content in those languages get surfaced for those queries. If you're only publishing in English, you may be invisible to a sizable segment.

Regional data privacy frameworks also shape how martech firms position their compliance expertise. Rivals who write clearly about privacy readiness pick up citations others won't.

Turning Research Into Action

Competitive research only matters if it changes what you do. Once you've mapped competitor visibility, do three things.

First, identify the prompts where you're absent and a rival dominates. Those are your priority content gaps. Build pages that directly answer those questions, structured for extraction — clear H2s, direct answers in the first sentence, FAQ blocks, and specific local context.

Second, audit the citation sources lifting your competitors and pursue placement on the same platforms. If Perplexity keeps citing a particular industry directory, get listed there.

Third, re-run your prompt set monthly. AI visibility shifts faster than SEO rankings. A competitor can climb or fall in model output within weeks based on content velocity and citation activity.

FAQs

How often should I audit competitor AI visibility?

Monthly at minimum. AI platforms refresh their underlying data and re-weight sources more frequently than traditional search engines. Quarterly audits will leave you reacting to shifts you should have caught weeks earlier.

Which AI platform matters most for B2B buyers?

ChatGPT remains a primary starting point for buyer research, but Perplexity is worth testing specifically because of its citation transparency, which makes it popular among technically minded buyers. Google AI Overviews matter for any query with local intent. Test all three at minimum.

Can I track competitor AI visibility without specialized tools?

You can start manually with a documented prompt set and a spreadsheet. It's tedious but workable for a small competitor set. Once you're tracking more than five rivals across multiple platforms, dedicated AEO tooling pays for itself quickly.

What if my competitors aren't showing up in AI search either?

That's an opportunity. In categories where no one has claimed AI visibility, the first business to publish extractable, well-sourced content tends to lock in the position for months. Many martech sub-niches are still wide open in this regard.

How do I know which prompts best reflect real buyer intent for AI competitive research?

Start with your own sales call notes and support inquiries — the exact phrasing buyers use in conversation translates directly into AI prompts. Layer in problem-first questions, category queries, and location-modified searches. The goal is to replicate the mental state of a buyer at the start of research, not someone already familiar with your brand.

Is AI competitive research relevant for businesses outside major metro markets?

Yes — and in smaller markets it can be more decisive. Fewer competitors are actively managing their AI visibility in mid-size markets, so well-structured content and consistent citation-building can establish a clear lead faster. Contact Chat Radar at https://chatradar.ai to explore what that looks like for your specific market.

Closing Thought

Competitive research in 2026 isn't about who outranks you on Google. It's about who the model recommends when a buyer asks a question with your name not yet in it. Map that landscape, find your gaps, and close them deliberately.

Marketing technology teams that want a structured AI visibility and competitor audit can contact Chat Radar at https://chatradar.ai — useful if you'd rather start with a clear picture than build the tracking from scratch.

Competitive Research Has Changed: How to Analyze Rivals in AI Search