{"id":15080,"date":"2021-11-11T17:44:17","date_gmt":"2021-11-11T16:44:17","guid":{"rendered":"https:\/\/appagent.com\/?p=15080"},"modified":"2024-02-26T19:10:50","modified_gmt":"2024-02-26T19:10:50","slug":"growing-subscription-based-mobile-apps-with-ltv-prediction","status":"publish","type":"post","link":"https:\/\/webrixstudio.online\/aa\/blog\/growing-subscription-based-mobile-apps-with-ltv-prediction\/","title":{"rendered":"Growing subscription-based mobile apps with LTV prediction"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

To learn more about industry insights and best practices, sign up for the AppAgent newsletter <\/span><\/i>here<\/span><\/i><\/a>.<\/span><\/i><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Identifying a suitable <\/span>post-install event<\/b> and generating an accurate <\/span>lifetime value (LTV)<\/b> prediction could help you significantly improve <\/span>user acquisition performance<\/b>. In this article,\u00a0 we explain what an LTV prediction is, the critical role the post-install events play in calculating LTV and how we improved the return on ad spend (ROAS) of a language learning app by 300% thanks to an unexpected finding.\u00a0\u00a0<\/span><\/p>

The state of the subscription-based apps market<\/b><\/h2>

Revenue from subscription-based apps<\/a> hit a record during 2020, reaching $13 billion, according to Sensor Tower. However, a whopping growth of 34% year-on-year doesn\u2019t mean it\u2019s easy to make a living from charging recurring fees on non-game apps.<\/span><\/p>

In fact, it\u2019s quite the opposite. Most subscription-based apps face very tough challenges in the post-IDFA (identifier for advertisers) era. Previously, strong monetization of the iOS user base\u2013\u2013which generated nearly 4x of revenue compared to Android in 2020\u2013\u2013showed a downward trend. At the same time, the cost of acquisition among some of our non-gaming clients grew by up to 500%.<\/span><\/p>

It comes as no surprise that publishers are exploring how to fight this negative trend. Besides creative optimization and audience expansion through using a motivation-led creative approach (see AppAgent\u2019s <\/span>Mobile Marketing Creative Series<\/span><\/a>), growth marketers have other major levers at their disposal: optimization events and LTV prediction.<\/span><\/p>

Source: Quantmar<\/span><\/i><\/p>

\"\"\u00a0<\/p>

What is LTV – lifetime value prediction?<\/b><\/h2>

LTV is the projected revenue<\/b> that a customer will generate over their lifetime (you can find detailed explanation in this <\/span>article<\/span><\/a> or <\/span>video<\/span><\/a>).\u00a0<\/span><\/p>

LTV helps marketers to forecast campaign performance early on, make long-term predictions with limited data, and optimize marketing spend while maximizing revenue.<\/span><\/p>

\u00a0<\/span><\/p>

3 basic approaches to LTV prediction are:<\/span><\/p>

1) Behavior-driven\/user-level predictions<\/b><\/p>

Analyzing in-app user behavior\/properties to identify actions or combinations that predict a new users\u2019 value.<\/span><\/p>

Example: User A had 7 long sessions on day 0, with a total of 28 sessions by day 3. He also visited the pricing page and stayed there for over 60 seconds. According to the regression analysis and machine learning-based algorithm, the probability of him making a future purchase is 65%.\u00a0 Using an ARPPU value of $100 USD, his predicted LTV is therefore $65.<\/span><\/i><\/p>

2) ARPDAU retention model<\/b><\/p>

Calculate the number of active days from a modeled retention-curve, by calculating the area under the curve. Then multiply the active days by the average revenue per daily active user (ARPDAU).\u00a0<\/span><\/p>

Example: Day1\/D3\/D7 retention are 50%\/35%\/25%. After fitting these data points to a power curve and calculating its integral until D90, we find that the average number of active days is 5. Knowing that the ARPDAU is 40 cents, the predicted D90 LTV would equal $2 USD.<\/span><\/i><\/p>

3) Ratio-driven<\/b><\/p>

Calculating a coefficient (D90 LTV\/D3 LTV) from historical data, then applying this coefficient to multiply the real D3 LTV to get a D90 LTV prediction for each cohort.<\/span><\/p>

Example: After the first 3 days, ARPU for our cohort is 20 cents. From historical data, we know that D90\/D3 = 3. The predicted D90 LTV would thus be 60 cents (20 cents ARPU*3).<\/span><\/i><\/p>

LTV prediction for subscription monetization<\/b><\/h2>

To illustrate our point, let\u2019s focus on a real case that demonstrates <\/span>how to calculate<\/span><\/a> the LTV for a language learning app that charges users monthly ($30) and yearly ($240).<\/span><\/p>

Such a model has two major components:\u00a0<\/span><\/p>

  1. what is the LTV after subscription; and<\/span><\/li>
  2. what behavior leading to subscription happens before the actual user commitment.<\/span><\/li><\/ol>

    \"\"<\/p>

    Here\u2019s a step-by-step process of how to calculate LTV:<\/span><\/p>

    1. First, decide over which time to calculate LTV. Typically it\u2019s 90 days, 180 days, or one year. You can choose to segment by country, channel, or platform. In our case, we split the LTV model into subscription type and country (the world and the US).<\/span><\/li>
    2. Second, gather retention data for subscribers and extrapolate for months when data is not available. (You can achieve this by fitting a power curve and applying a trendline to existing data.)<\/span><\/li>
    3. Calculate the active months from the retention data by summing up the monthly retention numbers: 100% (Month 0) + 45% (Month 1) + 36% (Month 2)\u00a0 + \u2026 + \u2026 = 330%. Do the same for yearly subscriptions. In our example, that is 130%.<\/span><\/li>
    4. Then calculate the average split between monthly and yearly subscriptions. (70% vs. 23%.)<\/span><\/li>
    5. Finally, you\u2019re ready to calculate the LTV for a random user from the US: Monthly sub % X Active months subscribed X monthly sub price + Yearly sub % X Active years X yearly sub price = 0.7 X 3.3 X 30 + 0.3 X 1.3 X 240 = $163.\u00a0<\/span><\/li><\/ol>

      \u00a0<\/span><\/p>

      In order to be able to quickly see the potential value of acquired users, you must analyze their behavior before they subscribe. To be able to <\/span>perform regression analysis,<\/span><\/a> you must have basic data available, such as:<\/span><\/p>