A holistic picture of customer lifetime value

Many eCommerce companies calculate Customer Lifetime Value (CLV) as the sum of all historical purchases for each customer. However, CLV, by definition, is a forward-looking statement. By simply considering historical purchases, the underlying assumption is that the customer will not make any further purchases. There has been significant statistical research into creating reliable probabilistic models to calculate Residual Customer Value (RLV) i.e. the dollars your customers are yet to spend.

Here are some ways in which calculating residual lifetime value can be useful for your business -

• Define your customers not only by what they have spent so far, but by how much they are likely to spend going forward
• Identify & understand the traits associated with your best and worst customers
• Improve acquisition decision-making
• Sales / demand forecasting
• Customer service queuing
• Proxy for a company’s value

The most popular of these statistical models is the Pareto/NBD model. Here is the paper that sets the foundation for it. It’s full of math, so here’s the short version -
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The model takes as input a historical purchase log, and calculates basic RFM (recency, frequency, monetary) stats. It then spits out the following data points for each customer -

• PAlive - the probability (between 0-1) that the customer is “alive” i.e. still a customer of your business.
• Expected Transactions - the predicted number of transactions the customer is likely to make in the next X months. X is defined by you and depends on how long you’d like to predict for. As a rule of thumb this should not be greater than the duration for which the model has been trained.
• Average Order Value - the predicted average transaction value of the customer going forward.

What you will need to predict CLV

1. Historical Transaction Log - A list of orders with the customer_id, date and amount of the order. This list should include orders made by customers acquired between 2 years ago and 6 months ago. For example, if today was Jan 1, 2018, the customers to be included would be the ones acquired between Jan 1, 2016 and June 30, 2017. It is okay to include customers older than 2 years (in fact, the more data the better), but not newer than 6 months ago.
2. R Script - The code to calculate the predictions for each customer. The "BTYD" R package is perfect for this purpose. Here is a walkthrough of the code.
3. Export to BI Tool - Ideally, you will want a way to automate the above steps 1-2 times a month. If you are using a BI tool, use API access to set up an automated export/import process.

Need help setting this up? Feel free to reach out here.

Note: This method is suitable for non-subscription businesses only. For subscription businesses, there are a host of other methods that can be used. Another post to follow shortly!