How to build a chat_card ad that actually works (2026)

Google Ads is evolving fast in 2026, and one of the most compelling new ad formats is the chat_card ad. Where traditional Responsive Search Ads (RSAs) rely on static headlines and descriptions, the chat_card brings a fully conversational experience directly into the search results page (SERP). Instead of passively viewing a message, the user is actively invited to respond. The result? Higher engagement, better self-qualification before the click, and richer signals for Smart Bidding. But building a chat_card ad that actually works requires a deliberate, structured approach. This article walks you through what a chat_card is, how to build one step by step, which mistakes to avoid, and how AdBrains AI technology takes this format to a structurally higher level.

What is a chat_card ad and why does it matter?

Standard text ad vs. chat_card ad

Comparison on the most decisive performance indicators

Standard RSA

• Static headline + description
• No direct interaction possible
• User leaves Google to get an answer
• Average CTR: 3-5%
• No personalised flow
• Limited signals for Smart Bidding

chat_card AdRecommended in 2026

• Conversational setup with multiple answer options
• Direct interaction within the SERP
• User self-qualifies before the click
• Average CTR: 6-10% for well-structured flows
• Personalised flow to the most relevant landing page
• Richer first-party signals for Smart Bidding

A chat_card is an interactive ad format within Google Ads that behaves like a short conversation. The ad displays an opening question, after which the user can click one of several answer options. Based on that answer, the user is directed to a specific landing page tailored to their stated intent. This stands in sharp contrast to a standard RSA, where every visitor lands on the same destination page regardless of their specific need or question.

The format aligns closely with how people search in 2026: conversationally, context-driven, and with a clear expectation of immediately relevant answers. With the rise of AI-generated search results and AI Overviews, Google has fundamentally reshaped the SERP. The chat_card responds to this shift by giving advertisers the ability to conduct a mini-dialogue before the user even reaches the website. The payoff is measurable: advertisers running well-structured chat_cards consistently see higher CTR, lower bounce rates on landing pages, and improved conversion rates on incoming clicks.

For both e-commerce and lead generation, the format offers distinct advantages. An online learning platform like ToetsJeKennis.nl can use the opening question to identify which type of exam preparation the visitor is looking for, with each answer option linking to the most relevant course page. A lead gen business like Clima-Active.nl can ask whether a visitor is interested in air conditioning, a heat pump, or a combination, then tailor the quote request page accordingly. In both cases, the potential customer arrives at the landing page with a significantly higher purchase intent.

The anatomy of an effective chat_card

A chat_card consists of several layers, each playing a specific role in the conversation. To get the most out of the format, every layer must be designed deliberately. The five core components are:

• Opening question (trigger message): The first text the user sees. It must connect directly to the search query and prompt a concrete response. Always write from the user's need, not from the product.
• Answer options (reply chips): A maximum of three choices covering the most common intents. Too many options create decision fatigue and lower interaction rates. Keep labels short, scannable, and clearly distinct from one another.
• Follow-up message (context message): After selecting an answer, the user sees a brief confirmatory message that validates their choice and announces the next step. This builds trust and lowers the threshold for clicking through.
• Dynamic destination URL per answer: Each answer routes the user to a different, specifically tailored landing page. This single element has the greatest impact on post-click conversion rate.
• Closing message with call-to-action: A short, clear wrap-up that invites the user to take the next step. Avoid vague phrases like "Click here"; use active, outcome-oriented language like "See your solution" or "Request a free quote".

The power of the chat_card lies in the combination of all these elements. A strong opening question paired with weak answer options will not perform. Perfect destination URL mapping means nothing if the follow-up message fails to build confidence. Every component must work together.

Step by step: how to build a chat_card that converts

Building an effective chat_card starts not in Google Ads, but in understanding the search intent of your audience. Follow the steps below for the best results:

1. Analyse your highest-value search terms: Which queries drive the most conversions? Use search term mining to uncover the intents hiding behind those searches. This forms the foundation of your opening question and answer options.
2. Define two to three core segments: Group the identified intents into two or three clear categories. For E-4motion.com, a webshop for new electric folding bikes, that could be: "For daily commuting", "For recreational rides", and "As a gift or for business use".
3. Write the opening question from the user's pain or desire: A question like "What do you use your electric bike for most?" outperforms "Which model are you looking for?"
4. Link each answer option to a unique landing page: Make sure the landing page reflects the chosen option visually and in copy. Use the same terminology as in the answer option itself.
5. Write the follow-up message and CTA: Keep the follow-up message to two sentences maximum. The CTA should be concrete and action-oriented.
6. Test and optimise based on interaction data: Analyse which answer options are chosen most often and which lead to the highest conversion rate. Adjust the chat_card based on these insights. This is an iterative process.

For HACCP-cursus.com, an online food safety certification provider, this approach could look like this: the opening question is "Which industry are you looking for an HACCP certification in?", with answer options "Hospitality", "Production and manufacturing", and "Home cooks and private individuals". Each option links to a dedicated course page built for that specific audience, resulting in a significantly lower bounce rate and more enrolments per click compared to a generic RSA campaign.

chat_card conversion funnel: from impression to lead or purchase

Benchmark flow rates per stage (industry average 2026)

Impressions

Ad shown in the SERP

Interactions

User clicks or answers a question within the chat_card

Clicks to site

User continues to the landing page after the conversation

Conversions

Purchase or lead form completed

Conversion rate on qualified clicks: average 33%

The funnel above shows how a well-structured chat_card optimises the flow from impressions to conversions. More than half of users actively interact with the ad, and the clicks that subsequently reach the website are of significantly higher quality than average. That translates into a conversion rate on qualified clicks that sits well above the industry average for standard RSA campaigns.

Common mistakes in chat_card advertising

Despite the power of the format, the same mistakes appear repeatedly. The most common ones to avoid are:

• Too many answer options: More than three choices increase cognitive load and reduce interaction rates. Prioritise sharpness over comprehensiveness.
• Generic opening questions: Questions like "What are you looking for?" are too vague. The question must connect directly to the search query that triggered the ad.
• Sending all answers to the same landing page: This is the most common mistake and renders the chat_card pointless. The value lies precisely in the personalised destination URL per answer.
• Ignoring mobile-first design: The vast majority of searches where chat_cards appear happen on mobile devices. Ensure answer options are short enough to read clearly on a small screen.
• Weak follow-up message: A generic "Thanks for your response" misses the opportunity to build trust and push the user toward the click.
• No connection to negative keywords: Chat_cards showing on irrelevant queries generate high interaction costs without return. Keep the campaign tightly segmented with relevant negative keywords.

How AdBrains AI makes chat_card ads structurally better

At AdBrains, our proprietary AI technology is specifically designed to optimise chat_card ads at every level, from initial setup to daily fine-tuning. Where a manually managed campaign relies on periodic reviews by an account manager, our AI runs continuously in the background and intervenes the moment data calls for it.

The foundation is our multi-agent verification system: four independent AI agents evaluate every optimisation decision before it is executed. For a chat_card campaign, this means that changes to answer options, destination URLs, or bidding strategies are always validated before going live. That prevents costly errors that are easy to make in manual management, such as an incorrect URL mapping that wastes hundreds of euros in budget.

Our automated search term mining analyses every search term triggering the chat_card campaign on a daily basis. Terms that do not match the ad's intent are automatically added as negative keywords. This ensures the advertising budget is allocated exclusively to queries with a high probability of generating a valuable interaction. For Clima-Active.nl, this means generic informational searches are filtered out and the budget flows entirely toward users who are actively considering requesting a quote.

AdBrains' RSA improvement system analyses the Ad Strength of every chat_card variant and automatically rewrites the opening question or follow-up message when performance falls below expectations. The AI looks not only at CTR but also at the ratio between interaction rate and conversion rate per answer option, optimising the entire chat_card flow continuously based on real user data.

Through our server-side signal enrichment via a proprietary sGTM infrastructure, we enrich the conversion signals that Google's Smart Bidding algorithm receives with first-party data. This means the tCPA and tROAS bidding strategies for chat_card campaigns are fed with richer, more accurate signals than standard conversion tracking provides. The result is a Smart Bidding algorithm that learns faster and is better equipped to predict which users will convert after a chat_card interaction.

Average CTR improvement per chat_card element (vs. standard RSA)

Percentage increase when the element is correctly applied

Relevant opening question

38%

Maximum 3 answer options

29%

Personalised destination URL per answer

44%

Clear CTA in closing message

31%

Mobile-first layout

22%

The chart above highlights which elements of a chat_card have the greatest impact on CTR. Personalised destination URLs per answer deliver the highest relative improvement, but it is the combination of all five elements that makes a chat_card truly work. AdBrains monitors and optimises each of these elements continuously through automated systems, keeping your campaign on its best-performing version at all times.

chat_card ads vs. other formats: when to choose what?

The table shows that the chat_card excels particularly in situations where an advertiser offers multiple products or services and wants to segment users based on their specific intent. For single propositions or strict budget discipline, a tightly structured RSA campaign is often more effective. The art is to use both formats strategically alongside each other, leveraging the data from the chat_card to improve other campaigns as well.

Frequently asked questions about chat_card ads

What is the difference between a chat_card and a standard Responsive Search Ad?

A Responsive Search Ad (RSA) is a static ad where Google automatically tests combinations of headlines and descriptions. The user sees one ad and clicks through, or not. A chat_card adds a conversational layer: the user is asked to select an answer, after which the ad adapts to that choice. This makes a chat_card fundamentally different: it is not just an ad, but a mini-qualification tool that helps users self-segment before they reach the website. Engagement is higher, clicks are qualitatively better, and the conversion rate on the destination URL is measurably improved.

Which industries benefit most from chat_card ads?

Chat_cards perform best in industries with multiple clearly distinguishable product categories or audience segments. Think of online learning platforms like ToetsJeKennis.nl (different study levels and subject areas), installation companies like Clima-Active.nl (air conditioning vs. heat pump vs. combinations), webshops with a diverse range, or lead gen businesses offering multiple service packages. Industries with a single, straightforward product or service benefit less from the format.

How long does a chat_card need to gather enough data to optimise?

Just like Smart Bidding campaigns, a chat_card needs a learning phase. Allow at least two to three weeks before enough interaction data is available to draw statistically meaningful conclusions. Make sure the starting budget is realistic and generates sufficient volume. With too few impressions and interactions, it is impossible to determine which answer option performs best. AdBrains' Keyword Incubator helps launch new chat_card campaigns safely and scale only when the data justifies it.

Can a chat_card be combined with Performance Max?

Yes, and doing so is actually recommended as part of a broader campaign strategy. A chat_card in a Search campaign gives you detailed intent data at the keyword level, while Performance Max (PMax) delivers reach across all Google channels. The intent data collected via the chat_card, such as which answer options are chosen most often and which destination URLs have the highest conversion rate, can be used to improve your PMax asset groups and audience signals. The two formats reinforce each other, as long as you use the data deliberately.