Use CasesPhoto & Video Mojo


Michal Parizek

Michal Parizek

Senior Growth Product Manager

As Mojo doubles ARPU, reliable & flexible reporting is key

Both prongs of Mojo’s growth strategy depend on reliable ARPU monitoring

The growth team at social video editing app Mojo uses average revenue per user (ARPU) as a guiding measure in the optimization of their product, monetization, and user acquisition. 

And it seems to be working. As 2023 drew to a close, the team enjoyed a near 2x year-on-year increase in ARPU — or realized lifetime value (rLTV) as it’s reported on in RevenueCat. 

To achieve this, the team needs a reliable and flexible way to measure key revenue metrics. Mojo has leveraged RevenueCat as its in-app subscriptions platform since the beginning, and RevenueCat continues to act as the single source of truth for Mojo’s growth and leadership teams. 

Centralized access to custom analytics help Mojo keep the pulse of ARPU 

Michal Parizek, Senior Growth Product Manager, is laser-focused on growing ARPU. This means constant experimentation with pricing, packaging, and paywalls to find what does and doesn’t work for the app’s myriad users. 

The Mojo product team makes use of RevenueCat’s Dashboard and Charts to monitor ARPU and apply custom filters to garner insights that drive their business forward. Michal shares some key examples of how RevenueCat enables his team:

  • A source of truth for ARPU. With the ability to cohort data in RevenueCat Charts, Mojo can split the data by different conversion and customer lifetime windows. 
  • Surfacing problems before they become problems. Michal tells us, “Having reliable dashboard reporting also allows us to quickly identify issues or where things might be broken. For example, once I managed to break something and there was no paywall displaying for a certain region. I saw a decline in ARPU in a particular country and we found very quickly what happened and fixed it.”
  • Aiding local price testing. Being able to break down metrics by location allows Michal to discover new opportunities. “If conversion rates are a bit lower in some countries that might indicate that the price may be too high.” These indicators inform which new localized experiments the team should run. 
  • Identifying revenue expansion opportunities. RevenueCat data on what percentage of revenue comes at different time frames after install or onboarding helps Mojo optimize their discount strategy so they don’t cannibalize on revenue by discounting too early. Michal shares an interesting observation, “It was quite surprising! We found out that about 50% of new revenue comes in the first 7 to 14 days, and the rest comes anywhere from three weeks to one year or two years. So it’s kind of interesting that there was actually still quite a lot of revenue to unlock.”

Read Michal’s write-up on how monetization experiments fueled Mojo’s early 2023 growth

“Our co-founders use RevenueCat to monitor high-level metrics: new and recurring revenue, churn, etc. These metrics make their way into our monthly decks for stakeholders and investors.”  Michal Parizek

RevenueCat fills in the gaps left by ATT

App Tracking Transparency (ATT) has been a huge challenge for the ability of apps to assess the effectiveness of user acquisition campaigns. Mojo is no exception. 

The introduction of Apple’s privacy changes has made it more difficult to attribute app installs and user engagement directly to specific marketing activities. However, Mojo, through RevenueCat, found a way to adapt and thrive despite these challenges.

Through cohort analysis and grouping users based on their acquisition date, Mojo can assess the long-term value and behavior of users acquired through different marketing channels. This cohort-based approach allows for a detailed understanding of user engagement and revenue generation over time, compensating for the lack of granular data from traditional attribution models.

Bernard  Bontemps, Head of Growth, emphasized the importance of blended cohort analysis in safeguarding Mojo’s marketing budget where they lack attributed data.

“By using realized LTV at different time frames we can really assess marketing performance. At day 14, for example, because we have a 7-day free trial. And then you can extend from day 14 to six months and isolate, let’s say, the cohort of September and look at the impact of the marketing we’ve done over time. How does that translate over time in terms of revenue?”

Mojo uses this blended cohort analysis alongside whatever attribution data they do have, providing a “source of truth that we can really trust”. 

Conclusion: What’s next for Mojo?

Mojo isn’t stopping here, though. With access to the data they need to profitably grow their business Mojo is excited to keep evolving and expanding their business. 

“Looking ahead, Mojo’s future sparkles with potential. We’re eyeing exciting expansions into Asia and pushing the envelope in mobile video editing, all while navigating the thrilling waves of generative AI. Our journey, built on solid sustainable growth and a knack for innovation, aims to make video editing simpler and more intuitive for everyone.”



Mojo is an app designed to help you make amazing video content for Instagram and TikTok and more. Based out of Paris, the app has been downloaded by over 40 million people worldwide with over 12,000 ratings contributing to a 4.8 star review.

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