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Case Study: Badi

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Barcelona-based badi launched a two-sided marketplace that connects people searching for flatmates according to age, tastes and interests. The room rental platform caters to both sides of the equation: people with a room to rent, and people looking to move into a new flatshare. The startup has built out a major presence in Spain and is present in major cities such as Paris, London and Rome. The brand has already hit 12 million rental requests since launch, and is on a mission to become the world’s largest and most innovative real estate marketplace, kicking-off 2019 with Series B!

The Challenge

Finding a mobile attribution solution to analyze and improve the user journey

Badi was born from necessity.

Finding the right place to live can be one of the most stressful processes. Carlos Pierre, saw a big need for matching roommates and apartments simultaneously, and consequently created an app to meet this demand.

Not long after its 2015 launch, badi has experienced a rapid growth, and needed to build a larger, more sophisticated data infrastructure with an attribution partner. Receiving good quality raw data in real-time was critical because badi optimized its marketing strategy with in-app events rather than installs.

We analyze the marketing campaigns at the ad level -- not in terms of acquisition, but in terms of the user journey. It’s not just enough for users to be acquired, use the app, and bounce. There are a number of steps to convert and without information on what’s happening with each user, we’d be working blindfolded.

Guillem Pons

Chief Data Officer,

badi

At the time, badi used a solution that didn’t allow user-level insight, preventing the company from analyzing behavior and improving the user journey.

The Solution

Migrating to Adjust

Badi made Adjust its new MMP. “The main reason we chose Adjust was to have user-level data”, Guillem Pons, Chief Data Officer at badi, says. “Without user level data, we couldn’t cross it with the rest of what we had.”

With Adjust, badi managed to fulfill its main criteria:

  • Freedom to receive data into its own backend, without limitation on how much could be provided (both in terms of length of data stored, and the level of granularity necessary)
  • User-level data
  • The full user journey, tying mobile and desktop data together
  • Privacy-compliance

At first, integrating Adjust on such a large scale was a big change to how badi’s data team worked, but the migration proved to be well worth it. “Adjust data was the first reason we changed our infrastructure into a big data system. We were getting so many data points at the time,” says Pons. Badi now makes full use of Adjust’s user-level data, processing 4,000 data points every second.

When we realized we’d be able to track everything without limits and could pass back all the data we needed, we were really happy about Adjust’s solution.

Guillem Pons

Chief Data Officer,

badi

Adjust’s migration process is carefully tailored over five steps. Learn more about how we migrate new clients.

User-level data allowed badi to align marketing and product for maximum growth

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.

Guillem Pons

Chief Data Officer,

badi

The Result

Reducing cost per acquisition and improving conversion rate

Having this data has helped solve several issues. For instance, badi has been able to get a much better idea of its funnel, improving conversions and performing cross-device tracking.

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