Customer lifetime value (CLV) is the expected net profit associated with a customer. It can be calculated in multiple ways and with various degrees of sophistication. There are two main areas where knowing your CLV will help you make decisions.
For customer acquisition, you should know the future value attributed to a new customer. Your acquisition strategies will vary depending on how much you expect to earn from a new customer. Compare your CLV to your customer acquisition cost (CAC) to regulate your acquisition spend. If you spend more money on acquiring a customer than you earn from them, you will lose money in the long run.
For customer retention, you’ll want to know the future value attributed to an already existing customer. Your retention efforts will depend on how much you expect to earn from an existing customer. Send out email reminders? Coupon codes? You can also evaluate the health of your existing customer base with a prediction of how much more they’ll spend in addition to what they already bought.
Marketers commonly use backward looking calculations, such as total sales as a proxy for a customer’s CLV. However, predictive lifetime models such as the one used at Compass are much more accurate in calculating a customer’s future value.
At Compass we calculate Customer lifetime revenue (CLR) as the sum of your customers’ already realized sales as well as their future expected sales and thereby cover their whole lifetime’s spending. To calculate future expected sales, we learn about your users’ spending behavior from your store’s order history. We then base our predictive model on this knowledge. We’ll further include your profit margin to calculate your net profit from each of these customers, which results in their CLV. Finally we average your customers to estimate your company’s overall CLV.
Since our predictive model is based on your past order history, we won’t be able to show your CLV if your store has less than 100 transactions. Over time, the increase of tracked transactions will equate to a more accurate prediction of CLV.