Quick and Dirty Metric for Determining Probability of Customer Being Active

One of the toughest problems facing organizations with many customers and large numbers of transactions is determining when a customer has left the company for good. Unlike contractual businesses where the customer is locked in for a given period of time, most companies have to make educated guesses about whether or not a customer is worth spending their marketing dollars pursuing.  While there are complex data analysis models that can be more than 80% accurate in predicting when a customer will buy next, many times a quick and dirty calculation can give your marketing executive a better indication of who to contact in the future.

We sometimes use the following metric to give an initial suggestion of whether or not a customer is still active in a given period. To clarify, period here can mean any observable time length - days, weeks, months, even years can work depending on your type of business and how long your typical customer stays engaged:

Probability (Active) = (T/N)ⁿ

where:

T = last period when the customer made a purchase

N = total periods in your observation time

n = total number of purchases during observation time

To give you an example of how it’s used, let me take the following example out of the book Managing Customers for Profit: Strategies to Increase Profits and Build Loyalty by V. Kumar (highly recommended) to illustrate the power of its simplicity. Let’s say I have two customers with the following purchasing pattern over a twelve month time horizon (X’s indicate when a customer made a purchase):

The probability for each customer to purchase in time period 12 is calculated as follows:

Customer 1 = (7/12)⁴ = 11.58%

Customer 2 = (8/12)² = 44.44%

The initial results may seem counter-intuitive - why does Customer 1 have a lower chance of purchasing again when he/she has 4 purchases in the observation time while Customer 2 only has 2 purchases? Well, as you can see from the table, Customer 2 appears to buy every 4 months and is poised for another purchase in month 12 while Customer 1 was purchasing every 2 months, but hasn’t made a buy in over 5 months so is unlikely to buy again in month 12.  Nice, huh?

While the above metric doesn’t take into account all important information, it does provide a fast and easy way for marketers to get a better indication of whether a customer should be put into the active marketing group or the re-activation group.

Tags: Customer Retention, Marketing Metrics

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This entry was posted on Tuesday, July 15th, 2008 at 8:07 pm and is filed under Customer Retention, Marketing Metrics. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.