Chat conversations as a new customer journey: what changes?

AI chat conversations are fundamentally reshaping the customer journey: people increasingly orient themselves through a continuous dialogue rather than isolated search queries, and then click through to websites and ads with significantly higher intent. A chat conversation in this context is any interaction via an AI assistant (such as ChatGPT, Gemini or Bing Copilot) where the user receives step-by-step answers to questions that were previously asked through Google searches.

Key takeaways

• The orientation and comparison stages of the customer journey are shifting from traditional search to conversational AI sessions.
• Visitors arriving via a chat conversation are typically further in their decision process than average search users.
• Google Ads and Meta Ads need to be restructured to remain visible at the moments when chat users do click through.
• Conversion tracking and server-side tracking become even more important to accurately measure this new, shorter path to conversion.
• AdBrains uses proprietary AI technology to continuously adapt campaigns to the evolving orientation behaviour of potential customers.

How the traditional customer journey worked

The classic customer journey consisted of multiple separate moments. A prospective customer considering an electric folding bicycle would start with a generic search, click on several websites, return to Google, conduct a more specific follow-up query, maybe visit a comparison platform, and only then convert. This entire process could span days or even weeks, involving dozens of touchpoints across multiple devices.

For advertisers, this created a complex customer journey attribution challenge. Which click, which channel or which advertisement ultimately delivered the conversion? Answers were rarely clear-cut, and budgets were often allocated based on educated guesses rather than solid data. For e-commerce players like ToetsJeKennis.nl, a platform for online exams and courses, the typical journey involved a blog post for awareness, a comparison on a review site, a branded search a few days later, and only then a purchase.

What is changing because of AI chat conversations

AI assistants are collapsing multiple steps of the traditional journey into a single session. Someone considering an electric folding bicycle can, within one ChatGPT conversation, ask what the best electric folding bikes of 2026 are, follow up about battery capacity, ask which webshops are trustworthy, and then click through. That single click to E-4motion.com already has an entire research phase behind it, one that bypassed traditional search completely.

For advertisers, this shift has real consequences. Brands that rely exclusively on paid search miss the moment when a user is forming preferences inside a chat interface. Visitors who arrive after a chat conversation are further along in their decision, which changes what a landing page or ad needs to do. And the time-to-conversion can compress sharply: someone may convert on their first website session because all the consideration happened in the chat window.

According to Think with Google (2025), a growing share of consumers use AI tools to gather information before making a final purchase decision, particularly in product categories with higher price points or more comparison options.

Which journey stages change most?

Not all journey stages are equally affected. The orientation and awareness stage undergoes the greatest transformation. Google played the role of gateway for years, but AI chat tools are increasingly taking over that role. The comparison stage follows closely: instead of opening five tabs of review sites, users ask targeted follow-up questions within one chat window.

The conversion stage itself changes more slowly. Actually checking out, filling in a form or confirming a purchase still happens on the advertiser's website. But the lead-up to that conversion has become shorter and more intentional, which means landing pages need to be immediately convincing the moment someone arrives, because the visitor has already done their comparison work.

For lead generation clients like Clima-Active.nl (air conditioning and heat pump installations), this means that an incoming quote request increasingly comes from someone who has already decided on the category and is now selecting a supplier, not someone who is still wondering whether they need an air conditioner at all. That shifts the required message on the landing page from educational to reassuring and action-oriented.

Implications for Google Ads and Meta Ads

There are three adjustments that are already relevant for campaign structure right now.

• Rethink keyword strategy: broad match keywords become more valuable because they capture the longer, more conversational queries that people type after a chat session. Someone who had a ChatGPT conversation about air conditioning units and then turns to Google uses different search terms than a traditional cold searcher.
• Adapt ad copy to higher intent: Responsive Search Ads for accounts like HACCP-cursus.com need to account for the fact that a portion of visitors are already informed. That calls for confirmation-oriented copy ("certified food safety course, receive your certificate the same day") rather than pure awareness messaging.
• Get conversion tracking right: server-side tracking and Enhanced Conversions are no longer optional. They are necessary to correctly register the shortened path and feed Smart Bidding strategies with accurate signals.

For Meta Ads, remarketing audiences become qualitatively different. A visitor who arrived at E-4motion.com after a chat conversation about electric folding bikes has a completely different behavioural profile than someone who clicked a broad interest-based ad. Treating them the same way wastes budget. Audience management therefore requires finer segmentation to reflect this difference.

Measuring in a chat-driven world: the attribution challenge

Attribution was already complex in the traditional multi-touch journey. In a chat-driven world it gets even harder. The chat conversation itself is invisible in Google Analytics or Google Ads, but it does influence visitor behaviour after the click. This creates "attribution gaps": conversions that occur after AI-influenced orientation but are credited to a branded search or a direct session.

The practical recommendation is to combine server-side tracking with a solid first-party data strategy. Knowing who visited your website and when makes it possible to better identify returning visitors who have been prepared by a chat session, and retarget them with relevant messaging at exactly the right moment.

How AdBrains AI technology handles the chat-driven customer journey

The shift toward chat conversations as an orientation channel touches every layer of a Google Ads account. AdBrains has built proprietary AI technology that compensates for this shift automatically, without requiring manual analysis.

Our automated search term mining runs daily analyses of all incoming search terms and identifies patterns consistent with post-chat intent. Search terms used after a chat conversation tend to be longer, more specific and more purchase-oriented. Our AI flags these patterns and adjusts bidding and ad copy for that specific cluster. For Clima-Active.nl, this meant automatically identifying a group of search terms typical of visitors who had already oriented themselves via a chat session, then adapting the campaign response to fit.

The Keyword Incubator system continuously tests new long-tail, conversational search terms inside a separate incubator campaign before promoting them to the production campaign. An unknown search pattern can burn through significant budget before anyone knows whether it converts. New keywords that match post-chat search behaviour are held in that controlled environment until enough performance data exists to act on.

Our RSA improvement system analyses Ad Strength scores across all Responsive Search Ads and automatically rewrites copy flagged as POOR. For accounts where the chat-driven journey plays a role, confirmatory messaging gets priority over awareness messaging. Visitors arriving after a chat session already know what they want. For ToetsJeKennis.nl, that translated to ad headlines that lead with USPs and certification rather than generic introductory language.

Server-side signal enrichment via our own sGTM infrastructure adds first-party data to conversion signals. Because the chat conversation itself is invisible to Google, every signal that does reach the platform needs to be as complete and accurate as possible to steer Smart Bidding in the right direction. Our system delivers enriched, deduplicated conversion signals so that Target CPA and Target ROAS strategies are working from correct data, even when part of the journey happened outside the Google ecosystem.

Our multi-agent verification system ensures that every automated optimisation decision passes review by four independent AI agents before it executes. User orientation behaviour is changing fast, and this layer of oversight prevents adjustments that look reasonable in isolation from doing real damage to campaign performance during a transitional period.

Practical steps for advertisers adapting now

A number of concrete steps help make campaign structure more robust for this shift:

• Verify that conversion tracking is complete, including micro-conversions such as form interactions and scroll depth, giving Smart Bidding more signals to work with.
• Implement server-side tracking and activate Enhanced Conversions to accurately measure the shortened conversion path.
• Review current broad match usage: accounts running exclusively on exact match risk missing post-chat search patterns.
• Analyse organic search terms for long-tail patterns indicating higher intent, and use those insights to build new ad groups and RSAs.
• For Performance Max campaigns, build tighter asset groups that address the higher intent of visitors arriving after a chat session.

Frequently asked questions

Can chat conversations be measured in Google Analytics?

Chat conversations via external AI tools like ChatGPT or Gemini are not directly measurable in Google Analytics. What is measurable are the website sessions that follow. By correctly setting up UTM parameters on all paid channels and analysing the behaviour of "direct" traffic, you can identify patterns suggesting post-chat behaviour, such as short sessions with immediate conversion or returning visitors who navigate quickly to a request page.

Does the role of Google Ads change as chat conversations grow?

Google Ads remains relevant, but its function shifts. In a world where orientation increasingly happens inside chat interfaces, a Google ad more often functions as confirmation or endpoint rather than a first introduction. This means ads and landing pages need to address the higher intent of the visitor. Google itself is also developing AI-driven search features (such as AI Overviews) that change the SERP, bringing new visibility opportunities alongside new competitive pressures.

How does this affect CPA and ROAS targets?

Higher intent at the click can, in theory, lead to a better conversion rate, which at a stable CPC level results in a lower CPA. But that effect only occurs if the landing page and offer match the already-informed visitor. If the page is still designed for awareness-stage visitors, the mismatch hurts conversion rate. Target ROAS and Target CPA work best when conversion data is complete and recent, which is exactly why server-side tracking becomes indispensable in this environment.

Does this only apply to large brands, or also to SMB advertisers?

The shifting orientation behaviour affects all advertisers, including small and medium-sized businesses. An SMB in air conditioning installation or an e-learning provider like HACCP-cursus.com faces the same reality that potential customers are first orienting via chat before they search or click on ads. For smaller accounts the impact can actually be greater, because less historical data is available to detect shifts in search behaviour early. Automated search term mining and smart broad match usage are especially valuable in those cases.