E-commerce
September 17, 2026
Are you wondering how to adapt your ad targeting to the end of third-party cookies? The answer lies in one sentence: you must switch to methods that respect privacy while remaining effective, such as first-party data, second-party sharing, or contextual targeting.
The topic is complex because the solutions are multiple and their results vary depending on the advertisers. The trap would be to remain stuck on old practices or to wait for a step backward that will not happen. Technologies are evolving, and merchants who experiment now will stay one step ahead.
So how do you adapt your ad targeting to the end of cookies? On the agenda:
Why do third-party cookies pose a privacy issue?
What are the six main methods of cookie-less targeting?
How to leverage your first-party data for better targeting?
What role does artificial intelligence play in this new targeting?
How does Qstomy help collect data without cookies?
Here we go.
Summary
Why do third-party cookies pose a privacy concern?
Third-party cookies are small pieces of code placed in a visitor's browser by a domain other than the one they are visiting. They are used to identify prospects, collect data, and use it for advertising targeting. For years, this system worked: consumers saw relevant ads and advertisers got good returns on investment.
The problem is that these cookies aggregate and share a large amount of private information, often without the users' knowledge. Investigations have revealed that sensitive data was being exposed to major brands, and that by cross-referencing it with other sources, it became possible to identify specific individuals. This situation prompted privacy advocates to call for changes, and browsers began to react.
Firefox blocked tracking cookies as early as 2019. Google, which owns both the Chrome browser and one of the largest advertising networks, announced its intention to do the same, before backing down in the face of industry protests. The advertising infrastructures built over nearly twenty years could not be replaced in a few months.
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Why did Google give up on deprecating Chrome cookies?
Google had planned to block third-party cookies in Chrome in 2024 and force all advertisers to adopt alternative methods. But the company backtracked. There are multiple reasons: advertising industry players raised the alarm, explaining that twenty years of ad tech infrastructure could not be rebuilt in six months.
Customers of its ad exchange, its tech partners, and antitrust regulators also exerted pressure. Some feared that the end of cookies would give Google a monopoly on high-performance advertising. As a result, cookies remained in Chrome, but ad tech providers continued to innovate.
For a Shopify merchant, this situation means that the transition will be gradual but inevitable. You should not wait for the complete disappearance of cookies to take action. Alternative solutions already exist and are gaining maturity. Testing them now allows you to see what works for your store before regulatory pressure intensifies.
What is second-party data sharing?
Second-party data sharing is a collaboration between an advertiser and a publisher who share their respective first-party data. Concretely, a merchant can partner with a media site to cross-reference their customer data with that of the publisher, in order to improve advertising targeting while respecting user privacy.
This approach is similar to what tools like Shopify Audiences offer. It allows you to expand your audience beyond your own contacts, without relying on third-party cookies. Shared data is often aggregated, which reduces the risk of personal information leaks.
For a merchant, this means your customer data becomes a valuable asset. The more quality first-party data you collect, the more interesting partnerships you can offer. This is an additional reason to set up a collection strategy now, before competition intensifies in this area.
How to use your first-party data to target your customers?
First-party data is the information you collect directly from your visitors and customers: purchase history, stated preferences, browsing behavior on your site. This data is valuable because it is reliable, up-to-date, and collected with the user's consent.
You can use it for retargeting: showing ads to people who have already visited your store or purchased a product. You can also create lookalike audiences based on your best customers, or personalize your campaigns according to the interests declared by your visitors.
The challenge is to collect this data in a smooth and non-intrusive way. A chatbot on your store can ask questions about the visitor's preferences, budget, or needs, without making it feel like an interrogation. This information enriches your first-party database and feeds your future advertising campaigns.
What are unified ID solutions?
Unified identifier solutions produce encrypted identifiers that can be shared between different advertising systems. These identifiers are often derived from an email address, allowing a user to be recognized across multiple platforms without relying on third-party cookies.
The Trade Desk, one of the leading advertising platforms, offers its own unified solution. Other players are developing similar approaches. The idea is to create an ecosystem where advertisers can reach the same people across different channels, while respecting user privacy choices.
For a Shopify merchant, this means taking care in collecting email addresses. Each captured email becomes a potential identifier for your advertising campaigns. This is one more reason to set up effective collection mechanisms on your store, such as sign-up pop-ups or offers in exchange for an email.
How do data clean rooms work?
Data clean rooms are secure environments where analysts can cross-reference data without exposing it directly. The data remains in a protected space, and only aggregated results are shared. This allows for measuring the effectiveness of a campaign or enriching an audience without violating privacy.
Concretely, an advertiser and a publisher can send their data to a clean room, where it is matched anonymously. The insights obtained are then used to optimize campaigns, without the raw data being visible to the other party.
This approach is particularly useful for merchants who work with multiple partners and want to maintain control over their data. It allows for collaboration with complete confidence, while complying with data protection regulations. It is a technical solution, but one that is becoming accessible even to small organizations via SaaS platforms.
What is cohort targeting?
Cohort targeting involves grouping users into sets sharing similar characteristics and then delivering advertisements to these groups rather than to individuals. This approach protects privacy because advertisers do not see individuals, but statistical groups.
For example, a cohort could consist of people interested in gardening, aged 30 to 45, living in suburban areas. A gardening equipment retailer can target this cohort without knowing the identity of its members.
This method is less precise than individual targeting, but it is more respectful of privacy. Results vary by advertiser, but well-constructed cohorts can offer good performance. Artificial intelligence plays a key role in forming these groups by identifying relevant signals in behavioral data.
How does contextual targeting replace cookies?
Contextual targeting analyzes the content of a web page, an app, a video, or an email to deliver relevant ads, without using personal data. If a visitor is reading an article about the best running shoes, they will see ads for running gear.
This method existed before cookies, but for a long time, it was considered less effective. Artificial intelligence has changed the game: it allows content to be analyzed with much greater precision, by understanding the meaning of text, images, and even videos.
For a merchant, contextual targeting offers a reliable and sustainable alternative. It does not rely on personal data, so it is not affected by privacy regulations. It works across all browsers and platforms, making it a solid foundation for your advertising strategy.
What role does artificial intelligence play in cookieless targeting?
Artificial intelligence is at the heart of new targeting methods. It allows for the analysis of massive volumes of data to identify patterns, build relevant cohorts, and optimize campaigns in real time. Without AI, contextual and cohort targeting would be much less effective.
Machine learning algorithms can predict which users are most likely to convert, based on behavioral signals rather than personal data. They can also adjust advertising bids depending on the likelihood of conversion, thereby improving return on investment.
For a Shopify merchant, AI is an accessible performance driver. Tools like intelligent chatbots can collect first-party data, qualify prospects, and recommend products, while feeding your advertising campaigns with high-quality data. AI does not replace strategy, but it makes it more precise.
Why is it necessary to experiment with the new methods right now?
Cookie-free targeting technologies are still young, and their results vary depending on the advertisers. Some platforms specialize in identifying visitors from email interactions, while others develop secure programmatic networks. The landscape is evolving rapidly.
Waiting for cookies to completely disappear before taking action would be a mistake. Merchants who experiment now can identify the methods that work for their industry, build solid first-party audiences, and refine their campaigns before the competition.
Experimenting does not mean testing everything at the same time. Start with one or two methods, measure the results, and then adjust. Contextual targeting is a good starting point because it is simple to set up. Second-party sharing and unified IDs can come next, depending on your needs and partnerships.
How does Qstomy help collect data without cookies?
Qstomy, the Shopify AI agent, helps merchants collect first-party data in a natural and non-intrusive way. Through natural language conversations, the chatbot asks about the visitor's preferences, budget, and needs, turning every interaction into actionable data.
This data powers your cookieless targeting strategy: it enriches your first-party audiences, improves the personalization of your campaigns, and strengthens your second-party partnerships. Qstomy also handles package tracking, product recommendations, and post-purchase support, increasing customer satisfaction and repurchase likelihood.
By centralizing interactions within your store, Qstomy helps you build a solid proprietary database, independent of third-party cookies. It is an investment that prepares your store for future developments in advertising targeting, while improving the day-to-day customer experience.
What checklist to prepare your shop for cookieless targeting?
Here is a concise checklist to prepare your Shopify store for the cookieless targeting era:
Collection: set up mechanisms to capture emails and preferences from the very first visit.
Data: structure your first-party data to make it usable by your advertising tools.
Partnerships: explore second-party collaborations with publishers or complementary brands.
Tests: experiment with contextual targeting and cohorts on a pilot campaign.
Measurement: track your performance indicators to compare the effectiveness of new methods.
In brief
Cookieless targeting relies on first-party data, second-party sharing, unified identifiers, data clean rooms, cohorts, and contextual targeting. AI enhances these methods, and merchants who experiment early will maintain a competitive advantage.
FAQ
Is contextual targeting really effective? Yes, especially with AI that analyzes content in depth. Results vary, but this method is reliable and sustainable.
Should I stop using third-party cookies? Not necessarily, but prepare alternatives. The transition will be gradual, and cookieless methods are gaining maturity.
How does Qstomy help concretely? Qstomy collects first-party data through natural conversations, enriches your audiences, and improves the customer experience, without depending on cookies.
To go further: Training an e-commerce chatbot with Shopify: using the right data without creating bad answers - Qstomy, Zero-party data with a chatbot: collecting useful preferences without making it feel like an interrogation - Qstomy, What e-commerce marketing strategy with zero ad budget? - Qstomy, How to increase sales for an e-commerce store? - Qstomy, Is Shopify Inbox enough for customer support of a growing store? - Qstomy, Out of stock on a single size: helping the customer choose between waiting, an alternative, and stock alerts - Qstomy, AI Chatbot to qualify B2B leads on Shopify without slowing down the sale - Qstomy.
Enzo
September 17, 2026
