LTV: Money matters, but not if you’re tracking the wrong events

Lifetime value (LTV) is the metric you can’t afford to get wrong — ever. At a high level, it tells you how much a user is worth. It’s the data point that can serve many purposes. It can tell you if your app will be (or stay) a commercial success. It can help you forecast growth for your business. It can be a litmus test for user loyalty and alert you to when (and why) your app is losing favor with your target audiences. LTV provides to all this and much more. But at its core, LTV informs your marketing budget.

The problem is: there’s no one-size-fits-all formula to determine LTV. In fact, there are many different ways to calculate it, depending on your app category and objectives. To further complicate matters, LTV, which is predictive and based on probables and possibles, is also mathematically complex. It requires serious brain power if marketers want to foolproof their forecasting model against change. Getting LTV right is also the only way to ensure you make money, not burn it. For all these reasons getting LTV right is a must. Knowing allows you to allocate budget and pay what users are really worth, ensuring your campaigns have a healthy return on investment (ROI) and turn a profit.

The ratio of LTV to customer acquisition cost (or CAC) “tells us everything” about a business’s health as it scales; CAC is out, LTV is in. LTV > CAC is so important, it’s become a mobile marketer’s go-to mantra. It’s “perhaps the most significant measure to benchmark” and conveniently one of the “easiest to figure out”...except when it isn’t. But for all of the writing out there on LTV and how to calculate it, no one’s talking about the thing that keeps you from getting accurate numbers.

Why calculating LTV is difficult

Calculating LTV is harder than it looks — we know that. Yes, there’s an equation for it (LTV = ARPU [average revenue per user] x 1/Churn), but this is an oversimplification rather than a one-size-fits-all solution. There are multiple ways to calculate LTV, and they depend on key events that happen within your app — if your app’s method of monetization isn’t user-generated revenue, your method of tracking LTV will be different from, say, a subscription-based app. LTV is also a shifting, dynamic metric. As churn rate rises or falls, so does LTV — making it difficult to predict. It’s also a challenge to attribute value to individual users; they must be grouped into cohorts that accurately reflect a particular type of behavior in order for a marketer to apply their learnings to an upcoming ad campaign. But those aren’t the only reasons why your actual LTV calculations are difficult to pin down.

When you’re in your dashboard, you’re currently able to see either “installs” or “re-attributions.” Installs are users who installed your app and were never reattributed. Users who were re-attributed to a new engagement make the “re-attributions.” So if a user is part of a retargeting campaign, is served an ad and is reattributed to a new source, they’ll move over to re-attributions, along with all of their in-app activity. No matter how many sources they engage with, you’ll never see the effect this has on the LTV of their first source. If a user has been reattributed and you’d like to see their earlier sources, you’re forced to query a report and drag in user IDs, or set up callbacks in order to blend the LTV of the original source with any later re-attribution sources.

This means that it’s tough to get a sense of which clients from a particular acquisition source re-engage and are reattributed away from their original source (and conversely, those who never are). For users who are reattributed, it’s not possible to get the full picture of the LTV of their original, first source. What app marketers need is a tool to help them understand their users’ engagement and re-engagement levels and the LTV of those first sources over the user’s actual lifetime.

Methods for projecting LTV

There are two main methods for projecting LTV, either based on modeling after certain events (suitable for when your data set is limited), or predictively, which is based on preliminary data captured at certain time points. Whichever way you go, you can leverage your LTV by identifying which marketing channels are giving you the best results, and invest your time and money in these channels.

Let’s imagine that you have a free to play gaming app. Unlike freemium or premium apps, your LTV varies rather than being fixed to certain points, and is therefore probably one of your core metrics. You’re looking partly to upsell and boost the LTV of existing users, and partly to acquire the users that are most likely to engage with your content.

The easiest and most intuitive way of working this out is by calculating the average LTV from your users over a longer period of time and watching that metric as a single, straightforward KPI. If it moves upwards, good, and if downwards, bad. Easy.

On our web series “The Lowdown,” Mike Paxman, Adjust Global Product Communications Manager, takes a look at how you can customize your LTV measurements in three steps to get value for money from all your users. As Mike points out, some users “may not open their wallets and bring you revenue,” but that doesn’t matter. There are other ways marketers can extract tremendous value from users based on how they “interact and engage with your app.”