The fintech app Orange Money was launched in an extremely dynamic & fast-paced growing market, with competitors as Revolut, Monese, LeoPay or Neat that aim at developing an innovative, user-centered product, seen as a disruptive alternative to the financial system.

Therefore, we deal with a product with a relatively low degree of openness to adoption, given that the features and usability are hard to grasp by the majority of potential users.

For the app to gain traction, Orange had to figure out how to enhance the app utility adoption rate - engage potential users and make them fully aware about the specifics of this app.
General Information:
Strategy: The Challenge: make the Orange Money app gain traction in 2 ways: first, we aimed to increase the user base and spike downloads, second - and basically the more important - we wanted to make users increase the usage of the app (add money, perform transactions, etc.)

For the first phase we targeted the tech-savvy, curious and well-aware of the digital ecosystem, namely ”the curious ones” and added another layer determined by the usage appetence.

But since we had to also tackle the potentially high uninstall rate of such target (given the market high competition and target degree of unpredictability), we further focused on how to increase the usage of the app by targeting the potential ”innovators” among the curious.

Transforming ”the curious” into ”innovators” was a matter of segmentation and granular micro-targeting techniques, in order to understand the propensity of our target and thus influence their behaviour after they downloaded the app. In short, use multiple sources of data and define a set of strategic actions:

- We defined Firebase events for each of the actions users can take in the app:
e.g. transferring money into the account, pay bills through the app, transferring money towards another account linked to the app

- Consequently, we imported Firebase events into Google Ads and DoubleClick Manager Account, to measure campaign performance and the frequency of the Events occurrence in the users behavior

- We created specific audiences (in Google Ads and DoubleClick Manager) based on data from the imported events (e.g. current university students with high openness rate but low usage). Consequently, we developed specific messages tailored for those audiences.
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