Lifetime Value Part 11: How to calculate LTV

Last year, I published a series of posts on the importance of knowing your users’ or players’ lifetime value, the key components and how to impact them and techniques to increase the accuracy of your customer lifetime value (LTV) predictions. I intentionally did not publish a formula for calculating LTV—while it is always a factor of retention, monetization and virality—as it is different by product and there are many alternative ways to get to an accurate customer lifetime value. Prompted by an infographic that I came across (see below) I did want to go into some details of the mechanics of calculating LTV.

The first step is to obtain your key variable metrics as averages across all users. The ones I prefer are ARPDAU (average revenue per daily active user), day 1 retention (how many people who use or install your website, app or game come back the next day), day 30 retention and k-score (how many free/organic users does a user bring in).

The second step is to estimate your “constants,” as Kissmetrics refers to them in the infographic. The key constants you need to understand and estimate are average customer lifespan (and days played in that lifetime) and customer retention rate. Lifespan is how long the customer will remain a customer, which is derived and estimated from your retention data. For a game, you may want to start by assuming six months and then adjust that number as you acquire better data on your users. The second metric, customer retention rate (also often referred to as churn), is the percentage of customers that, given a certain period of time, will return when compared to an equal period of time. As the infographic shows, there are other constants you may want to incorporate into your model (and I have discussed some of these in previous posts) but the core of an accurate LTV prediction for a tech company is understanding your user’s lifespan and churn rate.

Once you have your data, both the variable and constant metrics, it is time to estimate your LTV. As I have written before, always keep in mind that your calculation will be a prediction and actually represents a range of expected values of a new user. Also, to generate actionable data, it is more important to understand LTV of users from different ad sources and cohorts. That said, once you have the data either aggregated or by source/cohort, you then build a function to calculate your customers’ lifetime value. The infographic shows several formulas based on the metrics their example (Starbucks) considers useful. If you have focused more on the metrics that I mention above, an oversimplification would be ARPDAU times number of session in the lifetime of a player (which will incorporate churn rate) times their k-score.

Key takeaways

  1. Step one: obtain metrics for your variable data (average of players), such as ARPDAU (average revenue per daily unique user), day 1 retention, day 30 retention, etc.
  2. Step two: determine the constant metrics, most important being customer lifetime and churn rate.
  3. At its simplest, your customer lifetime value will be ARPDAU times number of session in the lifetime of a player times their k-score.

++ Click Image to Enlarge ++
How To Calculate Customer Lifetime Value
Source: How To Calculate Lifetime Value

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5 thoughts on “Lifetime Value Part 11: How to calculate LTV

  1. Yannick Bourquin says:

    Hi,

    My apologies, but your computations for LTV have a major flaw. You cannot multiply averages between each other unless you assume that the variables are totally independant.

    In this case, when you multiply the average value per week of a customer by the average lifespan, you assume that the “weekly value” is independant of the lifespan which is dubious. And for some reason, you never mention this assumption in your explanation.

    I work as a data analyst in social gaming. In our case, multiplying the lifetime by the ARPDAU underestimate the LTV by 60% because of the positive correlation between lifetime and ARPDAU.

    Like

    • I see your point and my calculation was probably an over-simplification. Payment behavior will depend on where users are in their lifecycle. It does depend to a degree on your mix of users but I definitely cannot disagree.

      Like

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