One might have heard that it is important to calculate and track Customer Lifetime Value (CLV). What is more interesting is that during our projects with clients, we have observed every single business having a slightly different definition of what they consider to be CLV and how they calculate it. The most simple and straightforward definition of CLV could be:
"Customer lifetime value is a concept which estimates how much money the customer is going to bring to a company in the long run."
However, this definition is quite broad and does not explain how to calculate it and what it is really used for. In this article you will find out:
First, which are the benefits of tracking CLV? At CIVITTA, we see three main areas where CLV is important:
It is not enough just to know the areas where CLV can be used to improve business as described previously. It is also important to choose the right way to calculate CLV; the most suitable method depends on several factors:
In our experience, we have seen 4 different levels of CLV maturity:
One of the main advantages of knowing CLV is to compare it to customer cost per acquisition (CPA). Only if CLV is higher than acquisitions costs, it is possible to have sustainable growth and the business has potential to be profitable.
This is not a new event for small companies, and most businesses tend to have an idea about how much, on average, is their customer worth — given some specific period as well as evaluate blended acquisition costs (CPA). However, the problem arises when business is growing and starts using more acquisition channels which are priced differently, and different acquisition channels are bringing customers with different value. Consequently, this raises the need to understand CLV and CPA at the lowest level possible (e.g. marketing channels, customer segment, etc.). That is when marketing attribution and more advanced CLV calculation methods come into play.
One other traditional CLV use case is to use it as an input to customer value management. In this case, companies would have a goal of improving CLV by using various actions with users. Such actions could be: price changes, discounts, services improvement or any other campaign.
In the example below, we are presenting how a raw output of advanced algorithms might look like. Each row represents one calculation in time of both historical and predicted values relevant to CLV. As it is calculated per each individual, it allows to constantly track each customer behaviour and react to any unexpected changes.
In the example above, calculations are ran monthly. The calculation, which was completed on 1/1/2018 using the available historical data at the time, shows that this customer has decreased in probability of being “alive”, resulting in expected total lifetime value decrease. Given this information, it would be beneficial to include this customer into the campaign which is designed to revive customers who have high chances of churning.
To sum up, the most important CLV exercise is to apply your customer data in a pragmatic way in order to understand the most critical threats to customer transactions (i.e. revenue contribution). The key is to identify which business problem (or problems) you want to solve using CLV, evaluate your maturity and choose the most appropriate CLV calculation method. As an example, if you want to understand which of two customer segments is more valuable, the second approach based on a rather simple formula is enough. However, if your goal is to dynamically generate and send an email or notification to customers who have high likelihood of churning, then probabilistic methods should be used.
These and much more were discussed by CIVITTA experts on CLV on 13th of May in a free 1.5h webinar. You can watch it here.