More and more retailers are seeking to offer a fluid and engaging mobile experience, by developing applications that replicate the functionality of their e-commerce site, while adding a touch of simplicity and loyalty.

But when it comes toanalyzing user behavior, things get complicated. We often try to apply the same data model between App and Web, with the laudable ambition of obtaining a unified data model. However, this approach conceals a number of limitations. Is it really possible to track a mobile application in the same way as a website?

⚠️ Identical behaviors… but different data

Let’s take a concrete example: on an e-commerce application, a user arrives on a product page, selects a size and then adds it to the shopping cart. On mobile, opening the size selector can trigger the appearance of a slider, considered a new page view. On the website, on the other hand, this same action is limited to a simple click on a selector, with no additional page loading.

👉 Result: for the same user intent, the application sends two page views, whereas the website only sends one.

Same behavior, two different data feedbacks. Indicators then become misleading: an App user will seem more engaged than a Web user, simply because of a difference in implementation.

🧠 The mirage of the single model

The idea of a common App + Web data model remains attractive: it facilitates global analysis and governance. But it overlooks the fundamental differences between these two environments: technical structure, user interaction, latency, navigation management and so on.

A truly relevant model must be consistent in content, but adapted in form – taking into account the specific features of each platform.

🧰 Stronger technical constraints on the App side

On the Web, updating a tag or trigger in a TMS (Tag Management System) is a snap. But on a mobile application, tracking relies entirely on the development teams. And this reliance is twofold:

  • an iOS team,
  • an Android team, each with its own rhythm and constraints.

This means guaranteeing data model consistency at every level. The same variable can easily diverge if the nomenclature is not strictly respected:

  • product_stock (Web) → “Fully Available
  • product_stock (iOS App) → “FULLY AVAILABLE”
  • product_stock (Android) → “fully_available”

Three variants, three possible interpretations, and a guaranteed analytical puzzle.

📘 The key: document, harmonize, anticipate

The solution lies in clear, structured documentation of the data model, including precise definitions, expected values and examples of use.

This documentation then serves as a recipe guide for the App developers, enabling them to quickly understand the expectations of the Data side, and avoid discrepancies or oversights due to lack of time or prioritization.

A well-documented data model then becomes a powerful lever for collaboration: it facilitates exchanges, limits errors and preserves technical knowledge over time.

💡 In conclusion

The unification of App + Web tracking is an ambitious goal, but it cannot be achieved at the expense of data quality. Rather than forcing perfect symmetry, it’s better to accept the structural differences between App and Web, and build an adapted, coherent and documented model.

Only then will companies be able to effectively analyze user behavior, and harness the power of their digital ecosystem without bias.

The Optimal Ways team has been supporting retail and e-commerce players for several years in implementing hybrid App + Web tracking models, with a pragmatic approach: 📊 data reliability, 🤝 cross-team collaboration, and 🧩 analytical coherence at the service of performance.