Helping Point -- a debit card with credit benefits -- reach all times high MAU and decrease CAC by ~68%

The client for this project was a Point Card, a startup offering debit card with credit card benefits like cash-back and rewards on purchases for US consumers.

The company’s main goal was to grow fast and the main challenge was high CAC that ate almost all the marketing budget. This project was one of the favorites, imagine advertising a debit card that gives you cash-back and rewards when you make purchases. That's a cool product 😎

We were chosen for this project after working directly with the CEO for a couple of months, we quickly saw results, aligned on what is the ultimate goal and how it can be met. In addition to that, our previous experience scaling businesses and my extensive knowledge of the mobile tracking ecosystem, before and after iOS14.


Test, Learn, Retarget, Scale


Mobile App Tracking Setup

The first challenge was to find out the real CAC, we believed that the real CAC was 4x the reported number due to tracking issues. So, we started by setting up our tracking stack in partnership with the data team. We also built reporting dashboards that reflected the information we should focus on as a growth team.

We configured our Mobile Measurement Partner "MMP", a tool that helps attribute, collect, and organize app data. MMP also helps manage all tracking codes in one Software Development Kit "SDK". You can think of MMP as the Google Tag Manager for mobile apps. We also built a custom web attribution algorithm to reflect the real weight of each channel on our overall growth vs spend.

Then we integrated our backend with our paid channels – such as Facebook – and mapped the most important events such as app install, signup, subscription…etc.


Test & Learn

After setting up our mobile app tracking, we were ready to test and generate learnings. We started with paid media, Facebook specifically. We had enough data to start.

We adapted a creative production framework that helped us test different value propositions, messages, designs...etc to understand user's behaviour. This quickly unlocked so many learnings.

It helped us understand where and how we should allocate out budgets for efficiency. More importantly, we got the right data signals to train the targeting algorithm and decrease our CAC by ~68%.

Retarget

In this step, we used the data signals we have to retarget users dropping from our signup funnel. This contributed into our CAC reduction.

It also helped us focus on optimizing our funnel for maximum conversion rate. We were able to give the product team much useful learnings that helped them shape our signup funnel.

Scale

We started experimenting with different paid social and paid search platforms including TikTok, Twitter, and others.

We also doubled down on referral campaigns, partnered with influencers and took over Twitter with a giveaway campaign.


Outcome: Sustainable Growth

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