The Rise of First-Party Data, How It's Rewriting Retail's Business Model

While chatting with several retail business owners recently, Aotter noticed a common phenomenon: everyone is anxious about rising ad costs, yet few truly understand why ads are getting more expensive, and what that price increase actually represents. In fact, this isn't simply a supply-and-demand issue — the real reason is far more complex; it's a massive reshuffling of the entire digital ecosystem.

Over the past decade or so, the logic of digital advertising was simple: track users with third-party cookies, build user profiles, and sell them to advertisers. This system made Google's and Meta's ad networks, ad-tech vendors, and media agencies all rich, and kept advertising costs relatively cheap — after all, the platforms behind the scenes could obtain a full picture of a user's online journey through relatively simple means, whether via DMP data management platforms or various smart ad-serving platforms, without ever needing to worry about how the data was acquired. But today, this system — this ecosystem — is collapsing. The essence of rising ad prices is that the amount of usable user data has shrunk. When data becomes scarcer, advertisers and brands have to spend more money to buy access to the users who can still be tracked. But that's only the surface phenomenon; the real change is that the underlying logic of the entire retail and advertising ecosystem is being rewritten.

Even before 2020, Apple had already set its Safari browser to block third-party tracking by default. Then in 2021, Apple officially launched its ATT (App Tracking Transparency) policy, forcing mobile apps to obtain explicit consent before tracking users. The result? That year, only 25% to 40% of users agreed to be tracked. Google had originally planned to phase out third-party cookies by the end of 2024, but in September 2025 it announced a pause on that plan, opting instead to roll out a new user privacy control system. In other words, Google hasn't really abolished cookies — it has instead given users a choice— but this has actually made usable tracking data even scarcer — which is essentially the web-world version of the ATT policy.

However, for the digital advertising ecosystem, another shift in user habits is quietly becoming mainstream. Ten years ago, if you wanted to buy something, you'd open Amazon, eBay, or Walmart's website and search for the product you wanted — the e-commerce platform was your "entry point." But now? AI agents are becoming the new entry point. Users open ChatGPT, Claude, or other AI tools and simply ask, "Recommend me a good coffee machine," and the AI will give a direct recommendation based on the user's preferences, budget, and use case. Users don't even need to open an e-commerce platform to get purchase advice. What does this mean for traditional e-commerce platforms? It means traffic is being intercepted by AI systems: users no longer rely on search engines or e-commerce site search functions, but instead rely on AI recommendations. This is a fundamental shift.

In the past, the goal of SEO was clear: get your website to appear on the first page of search results, and the goal of related advertising was equally clear — get you ranked near the top, or simply hijack ordinary keywords to appear ahead of others. But as AI becomes the primary information gateway, traditional SEO strategies are starting to fail. AI doesn't recommend products based on keyword matching, but based on contextual understanding and users' deeper needs. No matter how well-optimized a website is, if it lacks sufficient first-party data to back it up, an AI system won't be able to accurately judge whether that website can truly meet the user's needs. In other words, SEO is shifting from "keyword optimization" to "data-credibility optimization" — a website's value in the eyes of AI no longer depends on how many backlinks or how much keyword density it has — but on whether it can provide real, verifiable user satisfaction data.

In the era of AI recommendations, the most valuable asset isn't ad space, and it isn't traffic — it's real user behavioral data. Imagine a scenario: a user buys a coffee machine on Amazon, leaves a review, clicks on related products, and even puts coffee beans in the cart without checking out. All of this data is recorded by Amazon. The next time this user opens ChatGPT and asks, "Which coffee bean brand should I buy?" — if ChatGPT can access Amazon's first-party data (of course, within the bounds allowed by privacy agreements), it can give a much more precise recommendation.

This is why Amazon's advertising business grew from $4.9 billion in 2023 to a projected $6.8 billion in 2025, making it the world's third-largest advertising platform. Amazon isn't just selling ad space — it's selling precision. The first-party data it holds gives it a level of accuracy that no other advertising platform can match.

We can distill this logic into a simple framework: first-party data determines the precision of AI recommendations, precision determines user conversion rates, conversion rates determine advertising ROI, and ROI ultimately determines the price advertisers are willing to pay. Every link in this chain reinforces the others. Retailers that hold more first-party data can provide AI systems with more training signals, making AI recommendations more precise. The more precise the recommendations, the higher the user conversion rate. The higher the conversion rate, the more willing advertisers are to advertise on that platform, and the higher the price they're willing to pay.

Conversely, if a retailer lacks sufficient first-party data, it cannot train a precise AI recommendation system. Without precise recommendations, user conversion rates stay low. With low conversion rates, advertisers are unwilling to advertise, or are only willing to pay very low prices. Such retailers will eventually be marginalized. This is why Aotter says: if you missed the trend of rising ad prices, that's fine, but you must get serious about data management and transforming your retail business model. Because rising ad prices are only the surface — the real change is the intensifying competition over data.

Let's look at how the global retail giants are doing this. Amazon treated first-party data as a core asset from the very beginning. It records not only users' purchase history, but also browsing behavior, search keywords, cart contents, and even return records. This data is used to train recommendation algorithms, and is also sold to advertisers as precise audience targeting. The result is that Amazon's advertising business has become its fastest-growing business, with an annual growth rate of 23%.

Walmart launched the Walmart Connect platform, integrating its massive offline in-store data with its online e-commerce data. It has even installed smart shelf screens in physical stores that can display personalized ads based on a user's online purchase history. This kind of omnichannel data integration allows Walmart to offer services that traditional ad platforms simply cannot. Carrefour launched the Carrefour Links platform, focusing on leveraging its massive membership data. It provides advertisers with precise audience insight reports to help them better understand consumer behavior. What do these three companies have in common? They all treat first-party data as a core competitive advantage, not a byproduct. They are all shifting from "selling products" to "selling data services."

While helping several retailers with digital transformation, Aotter has seen some successful cases, and has also run into some pitfalls. Let me share a few practical takeaways. Many retailers still treat their e-commerce platform as the final destination, hoping users will purchase directly on their website. But in the AI era, your e-commerce platform is just a data collection point, not the point of sale. A user might see your recommendation via an AI assistant, then see your ad on social media, and finally complete the purchase on someone else's platform. But as long as that user's behavioral data is captured by you, you can use that data to improve your recommendation system and attract more similar users. In other words, you need to shift from the mindset of "I want to sell things on my own website" to "I want to collect user data, and then influence user decisions across every touchpoint."

This is no simple task. You need to integrate all your data sources. A user's behavior on your website, on your app, their in-store purchase records, and information in your membership system — all of this data needs to be integrated into a unified user profile, which requires strong technical infrastructure and data governance capabilities. Second, you must ensure data privacy and security. In today's world of increasingly strict privacy regulations like GDPR and CCPA, you must ensure users' data is properly protected. This is not just a legal requirement, but also the foundation of user trust. If users don't trust you, they won't authorize you to use their data. Third, you need to build a data management system capable of continuous learning and improvement. The value of first-party data doesn't lie in how much data you currently have, but in how quickly you can learn from that data and apply those learnings to your business. This requires a technical architecture that supports rapid iteration.

The traditional advertising model is: I have an ad slot, I price it based on factors like placement, timing, and audience, and then I sell it to advertisers. The new model is: I have a user base, I can predict their purchase intent with 95% accuracy, and I price based on conversion performance. This is a fundamental shift. The former is supply-based pricing; the latter is performance-based pricing. The former has limited profit margins; the latter can reach profit margins of 70% to 90%. To achieve this transformation, you need to be able to demonstrate your precision to advertisers. This means you need to build a complete attribution model that can clearly track the causal relationship between ad delivery and user conversion. This requires a combination of data science capability and ad-tech capability.

There are several reasons we emphasize that now is a critical moment. First, AI adoption is spreading faster than anyone expected — ChatGPT reached 1 million users in just 2 months, and Claude is also growing rapidly — which means user habits are changing much faster than we imagined. If you're not preparing for the AI era now, you'll soon be left behind. Second, competition over first-party data is intensifying. Large platforms (Amazon, Walmart, etc.) have already begun fully leveraging their first-party data. If small and mid-sized retailers don't start building their own first-party data infrastructure now, they'll find themselves completely unable to compete in the future. Third, the trend of rising ad prices will continue. As third-party cookies are gradually phased out and privacy protections strengthen, usable user data will become increasingly scarce. This means advertising costs will keep rising. If you don't change your business model now, your advertising costs will get higher year after year, eventually eroding your profits.

But conversely, if you start building first-party data infrastructure now, you can gain a competitive advantage in this new era. You'll be able to provide more precise recommendations, attract more users, and achieve a higher advertising ROI. That way, even as ad costs rise, your overall profit won't decline — it may even increase.

What Aotter wants to say in closing is this: the future of retail doesn't belong to whoever has the most physical stores, nor to whoever has the cheapest goods — it belongs to whoever can best leverage first-party data, and this applies not just to online e-commerce, but to brick-and-mortar stores as well. In an era where AI recommendations are becoming mainstream, e-commerce entry points are losing their dominance, SEO structures are changing, and user touchpoint pathways are evolving, first-party data is a retailer's lifeline. Master first-party data, and you can train a more precise AI recommendation system; with precise recommendations, you can attract more users and achieve higher conversion rates; with high conversion rates, you can demand higher prices from advertisers and earn higher profits — the ROAS metric of the old era is gradually becoming less of a true indicator, since third-party tracking attribution is only an estimate, and human motivation is easily over-optimistically estimated under the old ecosystem's standards.

If you missed the trend of rising ad prices and didn't adjust your advertising strategy in time, that's okay — but you absolutely must get serious about data management and transforming your retail business model. Because that is the fundamental factor determining whether you can survive in the AI era. Only when the roots are deep enough will the fruit be beautiful. First-party data is your root.

Sources:

[1] Apple ATT Policy Impact - Singular [2] Google Cookie Deprecation Timeline - Cookiebot [3] Amazon Advertising Revenue Growth - Statista [4] Retail Media Network Overview - AnyMind Group [5] RMN Deep Analysis - Effect Studio [6] Global Retail Media Market - WARC