Introduction to LTV

This is the second blog post in a series covering churn and customer lifetime value.

kliment Marzlyakov
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Churn and Customer Lifetime Value: Part 2

This is the second blog post in a series covering churn and customer lifetime value. In this article, we are going to focus on lifetime value (LTV). If you would like to know more about churn, check out part 1 of this series.

LTV is the amount of money a customer will bring to you over their entire relationship with your business (i.e. their lifespan). LTV and CAC (customer acquisition costs) are important indicators of the health of your business. LTV can be used to determine a “ceiling” on your marketing costs. An LTV analysis across marketing channels can help you to understand which channels bring you the most valuable clients. Along with that, LTV is a pillar of unit economics.

LTV can be:

  1. Historical—a cumulative sum of sales for a particular customer.
  2. Expected—a sum of future sales for a particular customer, commonly discounted to the current period.
  3. Overall—historical + expected.

Ideally, we would calculate the LTV based on margin, but depending on the circumstances, net sales or sometimes even gross sales may be used.

The general equation for calculating LTV consists of the following components:

  • Period—You should define a period for churn rate and revenue, and it should be the same for both. If your churn rate has a monthly basis, then your average revenue has to be monthly too. Check out our Intro to Churn blog post for more details.
  • ARPU—average revenue per user (customer). This should be calculated per the same period as the churn rate. For example, if we have a monthly churn rate, then we can calculate the ARPU as revenue for the month / users for the month. If you have an individual churn rate for each customer, then you can calculate the ARPU separately for each customer to be more accurate. To do this, you can divide historical customer revenue by the tenure of the customer (tenure = last date - first date, as defined in part 1 of this series). Make sure that you have the same period here. For a monthly churn rate and tenure measured in days, you would have to multiply your average revenue by 30 days. Beware of the cold start problem when you are dealing with a customer-level calculation. In the case of a new customer with a short tenure (e.g. two orders with a few days between them), your denominator will be too small, which can lead to a huge ARPU value. In such cases, you can change it with the average values for such types of customers.
  • MR—margin rate, which is roughly margin / revenue.
  • Life Span—an expected (future) lifespan for your customers (i.e. how many periods you expect them to stay with you). The formula for the geometric distribution mean can help us here. The churn of a customer follows geometric distribution—a customer is “alive” until they churn out. At each point in time, a customer will churn out with a certain probability. The impactful assumption here is that a customer will have the same churn probability over time. If that’s true, we can apply the formula of the mean for geometric distribution, which will be the average lifetime period for a customer if we use churn probability.
  • CR—churn rate for the period.
  • DR—A discount rate is a cost of capital to discount the future cash flows to the current period. It is often ignored in LTV calculations, but to be more accurate, you can add it as a correction to the Life Span.

Why it is valuable to apply the LTV approach to your business:

  • It allows you to view your customers as quantifiable assets.
  • It allows you to track the impact of your actions on LTV over time.
  • It provides an estimation of how much marketing money you can afford to spend.
  • It helps management to focus on long-term relations with customers instead of only attracting customers through cheap channels that pay off in the first month.
  • It allows you to perform customer segmentation based on their expected values—not only on their current values.
  • It can be used to calculate more accurate unit economics.

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