Use case #9 happn subscription performance

Context

Happn revolutionizes real-life connections with its unique dating philosophy

Happn is the real-life dating app. Recognised globally, it is one of the most downloaded dating apps for singles who value genuine connections. At the heart of happn’s philosophy is the belief that the context of an encounter adds a vital dimension to the connection. What sets happn apart in the crowded dating app market is its unwavering commitment to fostering trustful, respectful relationships. happn isn’t just about facilitating new encounters; it’s about crafting a unique experience where individuals discover each other within the context of their local environments, thereby adding an additional layer of connection. This approach resonates with singles, offering them a unique way to discover and engage with singles in their area. To date, happn has garnered a user base exceeding 142 million people worldwide. Its popularity spans various regions, with notable user concentrations in Western Europe, South America (particularly in Argentina and Brazil), as well as in Turkey and India.

Part of its strength lies in the adept management of its operations, with a dedicated team of 13 data experts overseeing the intricate web of user interactions and business metrics. Their strategy involves leveraging a suite of tools, including Tableau Software and Datama Compare licenses, to automate and streamline the analysis process.

Datama was implemented for the first time to replace an existing logic in an Excel spreadsheet that happened to be too complex, hard to maintain and update, and limited in the insights that were generated. The team discovered Datama as a Tableau dashboard extension and realized that it was easy to implement and matched the need they had to automate the recurring analysis of metric variation.

Approach:

Market Equation:

Understanding revenue dynamics through subscription renewal analysis

The original use case is focused on explaining variations of revenue generated by subscription renewal on the app.
This revenue obviously depends on the volume of expirations, the renewal rate of those expirations and the average value of the renewals.
The resulting market equation is relatively straightforward and can be written as follows:

Dataset:

Which data to analyze subscription dynamics

The dataset generated to analyze this use case obviously includes the metrics part of the above market equation but also the necessary dimensions to explain the variation. happn teams knew by experience that the duration of the subscription, the type of discount that triggered the first subscription and the type of subscription were the most important. We end up with the following dataset (It’s important to note that the dataset presented below, contains randomized and anonymized information for confidentiality reasons).

This dataset contains randomized and anonymized information for confidentiality reasons

Takeaways:

Insights from Datama: Understanding subscription trends

In the following example, the revenue decreased between the two compared periods due to a decrease in two steps: the number of expirations, which is explained by a decrease in performance on the dimension “duration of the subscription” of the specific segment “2 months”, and then a mixed effect on the second step renewal/expiration where the decrease is due to a lower proportion of the segment “2 months” compared to the others.