A lot of e-commerce advice sites will suggest you look at customer lifetime value by their average. The average value however will be very misleading and may cause you to make terrible decisions on acquiring customers.
Let's examine why the average lifetime value is highly misleading. Below is a graph of customer lifetime purchase values ordered by customer. It is a familiar L-shaped distribution with long tails.
This distribution becomes more L-shaped as more customers are added. It never becomes the shape of a normal Gaussian distribution that everyone is familiar with, which is a symmetric bell-shaped distribution with 2 sides.
The 80/20 rule
There is a rule of thumb for business that roughly 80% of the revenues are driven by 20% of the customers. (In actuality it is closer to 73-76%). Let's see what kind of implications such an extreme distribution has on the average customer lifetime value.
Let's begin with some round numbers. Say $80 million of lifetime revenue is generated by 20 customers. The rest of the 80 customers only generate $20 million of lifetime revenue. This makes the average lifetime revenue generated to be $4m*20% + $0.25m*80% = $1 million lifetime revenue on average per customer (out of 100 customers and $100m revenue). From the high revenue generating group, the average is $4 million per customer. From the low revenue generating group, the average is $0.25 million per customer.
Now for the sake of simplicity, assume that the Cost of Acquisition of each Customer (CAC) is $1 million, or close to it because you are basing decisions on the average lifetime revenue generated by each customer. Then for 20 customers, you are profitable by $3 million, but for 80 customers you are losing $0.75million, each.
As you scale your business, it is more likely that you will add customers who are unprofitable. The tail of the L-shaped distribution becomes more extreme, and what you thought was the average lifetime revenue of $1 million 6 months ago, is now only $0.5 million on average. You will not know this value is changing because it takes a while to realize that new customer lifetime values are lower than before. This could be disastrous if you kept the $1 million per Customer Acquisition Cost. However, using our AI forecasting software you can change the distribution of lifetime values by going after only the more profitable customers or changing unprofitable customers into profitable ones.
Suppose that you are able to cut 40 out of the 80 unprofitable customers. Then the profit goes from zero in the previous example to +$30million. We can do this for you because we can forecast customer lifetime value for each and every customer and forecast individual future purchase values for each and every customer, early in their lifetime. Contact us for a demo.