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Jonathan Raveh
12-14-2018
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User Retention
Calculating your app user LTV

User LTV is not only one of the most powerful business KPIs, but also serves two more roles – a great tool to plan for future growth, and a constant reminder of the ever-growing importance of user loyalty and retention as critical success parameters. LTV is both an everyday tool and a pillar for any long-term growth strategy.

 

Main LTV components

To figure out the ideal equation, let’s first understand what parameters actually contribute to an app’s user LTV.

Revenue/Monetization – The monetary aspect of an app, specifically the amount of app revenue driven from users. App revenue can be driven from various revenue models – advertising, subscriptions, in-app purchase, e-commerce, and more. Regardless of business model, revenue calculation takes into account all revenue components in that time frame.

Measuring Revenue: When calculating LTV, one must figure out the revenue that each user generates—the ARPU, average revenue per user.

Since most app users do not use the app every single day, 24 hours don’t constitute as an accurate enough time frame. On the other hand, looking at long time frames can lead to imprecise results. For this reason, most app developers use 30 days as the designated time frame to measure ARPU, which coincides well with the retention measurement.

Retention – The holy grail of app engagement. The retention component basically measures length of engagement (rather that frequency or quality of engagement). User retention curves are tracked by all attribution and analytics services – Google analytics, TUNE, Appsflyer, Mixpanel, Adjust, and others.

Measuring Revenue: Since the revenue factor is measured for a 30-day time frame, so should retention. In any case, ‘revenue’ and ‘retention’ should be measured over identical time frames, not only in length but also in date (not only ‘30 days’, but ‘January 2018’ as well).

Understanding retention is possible by calculating user churn:

Retention is then simply 100% minus churn. That is, if your 30-day retention is 30%, this means your have a 70% churn rate for that period.

Virality – Virality generally refers to the number of users who installed the app due to a referral from an existing app user. Virality is a huge growth metric, and apps with high viral factors tend to be very successful. In terms of user quality, there’s nothing like a recommendation from a friend. On the other hand, it’s quite difficult to measure virality, and analytics services do not currently offer a sufficient solution for tracking it. If you do manage to figure this out, your referral value will be a derivative of your standard users LTV. If not, just use ‘0’ as your referral value.

Calculating your app user LTV:

We’ve figured out how to measure revenue, retention, and virality metrics, so we are ready to calculate user LTV:

Remember, when calculating user LTV:

ARPU = Monetization factor

Churn = Retention factor

Referral Value = Virality factor

Example:

  • An app’s average user generates $1.5 per month
  • After 30 days, the app’s retention rate is 30%
  • On average, 10 users bring in 1 additional user to install the app (so a user brings 0.1 new app users on average). The referral value then multiplies this value by ARPU/Churn.

 

LTV calculation:

If you’re investing money on marketing and UA specifically, a positive ROI (return of investment) is your main goal. In this example, if it costs you less than $2.354 to acquire a new user, things are looking good. If not, you need to optimize one of the LTV components in order to become profitable.

More often than not, mobile marketers will focus on revenue and virality. Improving retention is commonly ignored and left for the product team to challenge. Lack of personalization and insufficient tools force marketers to spend more of their budget on UA rather than focusing their efforts on their existing users to increase app engagement, prolong user lifetime, and subsequently improve user LTV.

Applying meaningful personalization to the app by using real-world data can revolutionize not only the way apps understand their users and interact with them, but also the way their path to becoming commercially successful.