Badi does most of the heavy lifting in its BI, by leveraging Adjust’s real-time raw data option. Thanks to this capability, badi can track more than just a marketing campaign or user acquisition source. They can also track every single in-app event down to the user-level. This allows badi to receive granular information, such as custom login IDs, the type of searches made by users and the creatives they engage with.
To measure the success of its marketing campaigns, badi shifted the focus from event tracking to behavioral tracking. This allows the company to connect marketing and product strategies.
Badi analyzes the behavior of their users once they have installed the app. With that in mind, badi can test all functionalities and launch marketing campaigns according to the results they have found. Specific events can then define ROI. To analyze this behavior, badi uses clustering methodology: assessing all behavioral data with all user acquisition data, then clustering this user activity by frequency and how far it is from expected behavior.
From this, the behavioral ROI can be defined, showing trends such as most frequent behavior. This methodology also shows how marketing campaigns relate to specific user actions, as well as if they are targeting the right users. For example, to understand retention, badi tracks republished listings at the user-level and listing-level, ensuring they know when users (who had bounced) return to complete the listing. “With Adjust, I’ve also been able to perform in-depth post-campaign analysis, so now we know with absolute certainty if the users we acquired are exactly the ones we paid for,” says Guillem.
For us. it’s very important to have flexible, clean and well populated raw data since we merge Adjust’s data on our backend to track user activity over time. With it, we can shape user profiles and influence our own product development.