According to Databox, around 80% of marketers prefer looking at short term goals, like eCPI, because it's easy to measure, all the competitors are doing it, and it saves money. However, as a subscription app, planning for the long term is critical, especially for campaigns on ad networks. 

Let’s say you pay $0.60 for an install, a relatively low cost in the US. If the user who installed your app churns on day 1 - 25% of users will likely do so according to Business of Apps - that $0.60 is a sunk cost.

Elina Dakhis, Senior Strategic Partnership Manager at ironSource, with a focus on Apps Beyond Games, shares her insights on why LTV is important for your subscription-based app and tips to master it.

Why you shouldn’t spend all of your resources optimizing towards short term goals 

Before diving into how to measure success in the long term, let’s first dive into why you shouldn’t devote all of your attention to short term goals and achieving low CPIs. 

Higher bids bring in revenue generating users

It’s possible to have great install rates but flat revenue - there isn’t always a clear correlation. Often, the problem lies in optimizing towards driving cheap installs, or low quality installs that drive very little value. Meanwhile, more expensive installs lead to users that will spend time in your app, engaging with your premium content, generating more revenue, and eventually converting to subscribers. 

Diversity of bids means diversity of users 

Location, device platform, and network all have an impact on the price of the cost per install. For example, CPI differs by country depending on how big the audience is, how much they spend inside apps, etc. To reach a diverse set of users across different geos, devices, and networks, it’s important to remain open to a range of costs. Just because a bid is low, doesn’t mean those users aren't valuable, and vice versa. 

So, you shouldn’t be narrowing in on achieving low CPIs - high CPIs are actually quite valuable. That said, to determine what’s best for your strategy, it’s crucial to look at long term goals. We suggest calculating your LTV.

Longer term goals help you determine user acquisition costs 

Blindly paying for low CPIs without looking at long term metrics, such as LTV, means you could be missing out on an opportunity to spend more to acquire high-quality users and increase profit. If you know how your users behave in your app in the long term, you can predict how much revenue you'll generate from your users, and you can make more calculated decisions for your UA budget.

How to build the LTV model for campaigns on networks

To get a clearer picture into the effectiveness of your campaigns, it’s important to look at user behavior after they install the app and into the long term. Note that you should build dedicated LTV models for the different channels you’re running with - social, ad network, etc. Here’s how to measure LTV for your ad network campaigns taking into account multiple revenue streams:

1. Plot the ARPU curve taking into account all revenue generators

ARPU, or the average revenue per user, is determined by calculating the accumulated revenue generated by a segment of users on a specific day after install. To determine ARPU, first, sum all of the revenue generators - the amount subscribers pay, the revenue from in-app purchases, and the revenue from ads. Then, divide that by the number of installs. For example, if a segment of 1,000 users generates $6,000 over 6 months, the Month 6 ARPU would be $6. If those 1,000 users generate $12,000 over 12 months, the Month 12 ARPU is $12. 

When building the ARPU curve for subscription apps, it’s important to take into account all of your revenue generators - subscriptions, in-app purchases and ads. For some apps, you can stop at choosing a relevant ARPU goal, 12 months for example, to determine the value of your users. For most, however, you’ll need to construct an LTV model from the right trendline.

2. Choose the right trendline for each revenue generator to build your LTV model

Place a trendline over the average revenue per user (ARPU) curve to build the LTV model. Doing so automatically fills in the revenue predictions from the last day of calculated data to the end of the users’ lifetime in your app.  

When building the LTV curve for a hybrid model with subscriptions, ads, and/or in-app purchases, keep the behavior of these components in mind. A logarithmic trendline usually works better for the LTV curve for apps that don’t monetize with subscriptions. We’ve found that a power curve fits over the ARPU the most accurately for subscription apps. This is because subscription apps tend to offer some kind of utility that stands the test of time. Once you’ve built the ARPU curve for each revenue stream, stack them on top of each other to get a more accurate prediction. Below is a more detailed example.

The graph above is the LTV model for the first 180 days of a Social Utility App - their monetization model is based on subscriptions and ads. As you can see, we plotted the ARPU curves (solid lines) based on data we already had for subscriptions and ads separately. From there, we placed power curves (dotted line) to predict the future revenue - keep in mind that the end of the LTV curve does not indicate a user’s last day in the app. Based on the graph, we can assume that the LTV for the average user will be $0.80 for weekly subscribers, $0.25 for monthly subscribers, and $0.15 for ads. 

Now’s the time to start measuring the granular metrics to optimize the precision of your LTV model. There’s more to creating a winning LTV model than just choosing the right trendline.

3. Enrich the model with more data

There’s a lot of uncertainty behind building an accurate revenue prediction, and it’s important to be comfortable with this. Typically, apps have many more non-subscribers than subscribers and subscription rates are constantly changing. IAPs offer a glimpse into the level of user engagement, but often don’t paint the whole picture of how users behave in your app. 

It’s important to look at other engagement events outside of just how much a user is paying each week, month, or year or their engagement with IAPs and ads when building the LTV model. In fact, you should be tracking as many metrics as possible, as early as possible. You can include any type of in-app engagement, such as opening the app a certain number of times, editing a few photos, etc. This granular understanding of your app’s overall performance will help you determine exactly where you stand, allowing you to streamline your strategy towards investing in the right users.

If you start including other metrics into your LTV model and you see different behaviors for different user groups, you should consider building different models to reflect different revenue streams - subscription, IAP, ads - rather than combining them into one.

4. Build a different model for each subscription time frame

Many apps offer weekly, monthly, and annual subscriptions, and these users are going to behave differently and bring in revenue at different rates - it’s not one size fits all. 

Rather than converting annual subscriptions to the monthly equivalent, it’s best to build an LTV model for weekly vs. monthly vs. yearly subscriptions. From there, if you’re including an engagement metric outside of revenue, you can apply a different rate to each model (since, for example, churn will be different for monthly users compared to weekly users). This way you’ll improve the accuracy of your LTV model and have a better idea of how specific users are interacting with your app according to different subscription models.

What now?

Once your LTV model is ready, the next step is adjusting your KPIs based on the information to ensure you’re making the best decisions for your UA strategy. Choose a reasonable margin you’d like to maintain and determine the shortest KPI possible where you can still accurately predict long-term user behavior in your app. Often, it’s the average time it takes a user to subscribe. Your work doesn’t end here - continue to adjust the data so the LTV model remains as updated as possible and takes into account fluctuations in user behavior, such as during holiday seasons, unexpected pandemics, political unrest, etc. 

Measuring short term goals are important, but long term goals are just as, if not more, important to calculating overall success and the effectiveness of your campaigns. Start measuring your LTV model using the above steps and be sure to take into account multiple revenue streams. 

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