How Verv Fitness App Grew 1500% in 3 Months Becoming a Multimillion-Dollar App

How Verv Fitness App Grew 1500% in 3 Months Becoming a Multimillion-Dollar App

VERV has been our partner for over 2 years, and today we’d like to share with you the case of implementing and bringing their newest VERV app to the market with

Jumping ahead of ourselves, we’re proud to say that the transition to working with BI and predictive Revenue and ROAS allowed the VERV app to increase its Revenue up to 800k+ from December 2019 to May 2020.

————————— brought us great value by gathering transparent, reliable data in one place, instead of running tens of analytical systems simultaneously. The very quick assessment it delivered for the effectiveness of our ad accounts & managers, saved us a ton of time and money.“


How the whole thing started:

This is what our processes looked before we’ve been introduced to the BI and ‘predictions’:

  • The target payer LTVs, calculated and validated by the analyst, were set before the marketing department. They represented the average LTV values broken down by tiers (countries with similar LTV).
  • Almost all VERV products sold both trial subscriptions with different durations (weekly, monthly, annual) and trial-free subscriptions.

That way, in order to purchase traffic, the UA manager would independently calculate the conditional CAC d1 which equals to the activation cost of any subscription, which took into account the following: 

  • the intended trial conversion rate for trial subscriptions,
  • distribution within the configuration (the number of users that got trial-free purchases and their LTV);
  • as for the purchase, the UA managers also considered the 1-day cancellation rate. If it exceeded the N value, then the campaign wasn’t likely to pay off.

There are two fundamental difficulties with this approach:

  1. the probability of error
  2. hasty decision-making.

Manual LTV calculation, the need to consider numerous factors for the UA team (geo, 1-day subscriptions and purchases distribution, cancellation rate) – all this significantly increases the likelihood of wrong decisions that directly affect business outcomes.

Speed: there is a clear time lag before the analyst/UA manager takes notice of the fact that the actual LTV or target CAC values ​​are out of the average value. Such situations are inevitable and can lead both to lost profits (UAs were forced to turn off campaigns, while calculating LTV at low values) but also to purchases that result in negative Revenue.


“The team helped us make the right call on whether to scale or turn off certain ad campaigns in our ad manager, while using both LTV and ROI metrics, and we were always guided by smart predictions. We highly recommend their BI system for effective growth! “


As a result of the integration with, the team could enjoy the following benefits: 

On average, the time for harvesting purchase data and its analysis went down by 30%.

Redistribution of time cannot be underestimated, either. The UA team stopped spending precious time on building reports, manually summarizing data, as well as uploading data from various analytical systems. The team could devote their freed-up time to a deeper analysis of ad campaigns and their analytics in various sections (period, geo, campaign, ad set, ad, paywall and configuration).

  • Real-time purchase data contributed to making timely decisions for scaling/disabling campaigns. And the automation of all eRevenue and eROAS calculations minimized the possibility of false calculations.
  •  Using allowed the VERV team to test multiple paywall prices and find the optimal configuration that maximized their ROAS. VERV has accumulated historical data on renewed subscriptions from different periods for other apps in the Health&Fitness category. From the very launch of the VERV app, we’ve been using these values in building the relevant model up to the point where the new application managed to collect enough data. This approach made it possible to understand the return on investments of the application for the required period from the very start of the traffic purchase.
  • Also, for the convenience of testing configurations, the VERV team used our Monetization dashboard, which simplified the LTV calculation for a configuration with these or other relevant prices.

Finally, and most importantly, all of the above-mentioned factors positively impacted the financial performance of the company and allowed the VERV app to increase its Revenue up to 800k+ in the space of 3 months of active purchases. is a full-fledged marketing analytics platform which predicts 1-month/ 2-month/ 6-month/ 1-year/ 2-year acquired user revenue. If your team is looking for ways to automate LTV reports and recalculation, and your goal is to make prompt and accurate marketing decisions, please fill out an application on our website or contact us at We are always happy to tell you more about our platform and address all your questions.

Written by Maria Kurnosova
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