New research from Similarweb tracking consumer search journeys across three industries shows that AI chatbots and traditional search engines are serving different roles in the purchase path, with ChatGPT functioning as a discovery and shortlisting tool while conventional search handles price comparison and final verification. Brands recommended by ChatGPT were 2.5 times more likely to receive a site visit even without a direct link, and 98% of consumers still cross-checked those recommendations through a search engine before purchasing.
- AI chatbots dominate early discovery while traditional search engines remain the preferred channel for price comparison and deal hunting, meaning brands need to optimize for both stages separately.
- A ChatGPT brand recommendation makes consumers 2.5 times more likely to visit that brand’s site, even when no clickable link is included in the response.
- Visitors arriving from AI referrals spend roughly twice as long on-site and view twice as many pages compared to other traffic sources, pointing to stronger commercial intent.
- With 98% of consumers verifying AI recommendations through additional research, organic search visibility remains essential alongside any AI visibility strategy.
- Consistent brand mentions across third-party sites are gaining importance as a signal in AI recommendation systems, shifting some optimization focus away from backlinks alone.
What Changed and Why It Matters
Research from Similarweb, covering thousands of user search journeys across three industries, reveals a clear split in how consumers use AI tools versus traditional search. People turn to chatbots like ChatGPT during the early discovery phase, then switch back to conventional search engines when they need price comparisons, discount codes, or deal hunting. The two channels are not competing for the same moment in the journey.
The influence of AI recommendations on brand consideration is stronger than many marketers expected. Consumers who received a brand recommendation from ChatGPT were 2.5 times more likely to visit that brand compared to competitors, even when no direct link was provided. That figure alone signals a meaningful shift in how brand awareness is built.
This is where Generative Engine Optimization and its role in modern SEO strategy becomes relevant. GEO is framed as a complement to traditional SEO rather than a replacement. The reasoning is grounded in behavior: 98% of consumers still verify AI-recommended brands through a search engine before purchasing, meaning organic visibility remains essential even after an AI mention.
Traffic quality adds another layer to the argument. Visitors arriving from AI referrals spend approximately twice as long on websites and view roughly twice as many pages compared to standard visitors. Higher engagement from a smaller referral pool can still carry significant commercial weight, making AI visibility worth optimizing for now rather than later.
Key Confirmed Details from the Study
The research puts specific numbers behind what many marketers have suspected: AI chatbots carry real weight during the consideration phase of a purchase journey. When ChatGPT recommends a brand, consumers are 2.5 times more likely to visit that brand’s website, even when no direct link is provided and even if they have never visited the site before. That figure points to a meaningful level of trust in AI guidance that goes beyond simple convenience.
The verification behavior is equally striking. Only 2% of consumers act on an AI recommendation without doing further research. The remaining 98% cross-check brands through additional channels before committing to a purchase. This means AI chatbots are functioning primarily as discovery and shortlisting tools rather than direct conversion drivers.
Engagement quality among AI-referred visitors also stands out. Compared to visitors arriving from other sources, this group spends twice as much time on-site and views twice as many pages. For site owners, that pattern suggests stronger intent and a more receptive audience.
The study examined user search journeys across three industries, tracking specifically when and how consumers switch between AI chatbots and traditional search engines at different purchase stages. Understanding those transition points is central to developing effective AI visibility strategies that account for the full path to purchase rather than isolated touchpoints.
Who Is Affected and What the Shifts Mean in Practice
The rise of AI-assisted shopping touches several distinct groups, each facing different pressures. E-commerce brands in electronics, fashion, and home goods now need to account for a consumer journey where discovery happens on an AI platform and price comparison happens somewhere else entirely. That split requires optimization across two stages rather than one continuous funnel.
For SEO and content marketers, the practical change is that brand visibility in AI-driven search increasingly depends on mentions and citations, not just backlinks. Traditional keyword optimization still matters, but layering in generative engine optimization (GEO) strategies is becoming necessary to maintain presence across both channels.
Publishers and review sites are gaining renewed relevance as trust validators. With 45% of consumers reported to search for a brand after receiving an AI recommendation, third-party sources that verify claims are positioned as a critical step in the purchase path, not a peripheral one.
B2B companies face a related but distinct challenge. High-consideration purchases are increasingly being surfaced through AI discovery, which can accelerate the early research phase. The risk is that without strong external validation content, brands may appear in AI results but fail to convert when buyers move to verification. Across all these segments, the common thread is that AI and traditional search now function as complementary layers, and strategies built around only one of them are likely to leave gaps.
The 98% verification rate is a useful reminder that AI discovery and organic search are not rivals. A brand that earns an AI mention but lacks credible third-party coverage may win the shortlist and still lose the sale. Both layers need attention, not just the newer one.
Practical Response and Next Steps for Brands
The core strategic shift required here is dual optimization: brands need to pursue AI chatbot visibility for discovery while maintaining traditional search performance for price comparison. These are not interchangeable goals, and collapsing them into a single approach will leave gaps at critical points in the buyer journey.
Building Visibility in AI Engines
Start with a GEO (Generative Engine Optimization) audit to map the buyer questions your category generates, then assess how well your content surfaces in AI-generated responses. Restructure content using answer-first formatting, clear headings, and FAQ sections that make it straightforward for AI systems to extract and cite your information. Strengthening entity signals matters here too. Consistent brand mentions across third-party sites carry real weight in AI recommendation systems, even when no direct URL is provided alongside the mention.
Implementing schema markup for AI and search crawlers helps AI systems parse your content accurately, reducing the chance of misrepresentation in generated answers. Adding verified statistics and expert quotes further improves the credibility signals that AI engines appear to favor.
Protecting Traditional Search Performance
Do not deprioritize conventional SEO for price-comparison keywords and deal pages. Consumer behavior data suggests that shoppers return to traditional search engines at the final decision stage, particularly when comparing prices. Monitoring AI-driven referral traffic through tools like Similarweb’s AI Traffic Tracker gives a clearer picture of which landing pages are actually receiving sessions from AI sources and where adjustments are needed.
Practical Response and Next Steps for Brands
The core strategic shift required here is dual optimization: brands need to pursue AI chatbot visibility for discovery while maintaining traditional search performance for price comparison. These are not interchangeable goals, and collapsing them into a single approach will leave gaps at critical points in the buyer journey.
Building Visibility in AI Engines
Start with a GEO (Generative Engine Optimization) audit to map the buyer questions your category generates, then assess how well your content surfaces in AI-generated responses. Restructure content using answer-first formatting, clear headings, and FAQ sections that make it straightforward for AI systems to extract and cite your information. Strengthening entity signals matters here too. Consistent brand mentions across third-party sites carry real weight in AI recommendation systems, even when no direct URL is provided alongside the mention.
Implementing schema markup for AI and search crawlers helps AI systems parse your content accurately, reducing the chance of misrepresentation in generated answers. Adding verified statistics and expert quotes further improves the credibility signals that AI engines appear to favor.
Protecting Traditional Search Performance
Do not deprioritize conventional SEO for price-comparison keywords and deal pages. Consumer behavior data suggests that shoppers return to traditional search engines at the final decision stage, particularly when comparing prices. Monitoring AI-driven referral traffic through tools like Similarweb’s AI Traffic Tracker gives a clearer picture of which landing pages are actually receiving sessions from AI sources and where adjustments are needed.
Signals To Watch as AI Discovery Matures
As AI-driven discovery becomes a more established channel, the metrics worth tracking are shifting. Brand visibility no longer depends solely on ranked search results. Marketers now need to monitor how often their brand appears in AI-generated responses across platforms like ChatGPT and Google AI Overviews, since citation frequency is becoming a meaningful proxy for discovery-stage competitive positioning.
Consumer verification behavior is equally worth watching. Roughly 45% of users who receive an AI recommendation go on to search for the brand directly, and verification rates across the broader population sit near 98%. Understanding how search intent shapes user behavior after an AI touchpoint can help marketers design better post-recommendation experiences and capture that follow-through traffic more effectively.
On the measurement side, tools that track AI referral traffic and prompt-level attribution are still emerging. How accurately brands can attribute influence from AI discovery will depend heavily on how these capabilities develop. Several specific signals are worth monitoring on an ongoing basis:
- Adoption rate of AI referral and prompt tracking tools across analytics platforms
- Shifts in ChatGPT traffic share if newer AI search tools gain ground
- Whether linkless brand mentions continue to carry weight as ranking signals evolve
- Changes in verification channel preferences among consumers post-recommendation
None of these signals are fully settled yet, which makes regular observation more valuable than any single snapshot analysis.
