Profit calculation for churn prevention data mining models (part 2 of 3)

09.04.2011 Hilmar Buchta

SQL Server 2005 | SQL Server 2008 | SQL Server 2008R2

In part 1 of this mini series of data mining posts I showed how data mining can be used to optimize a churn prevention campaign by selecting the optimal sub sample of customers to receive a voucher. For this optimization I used a data mining model (without going into detail about the model itself). However, developing such a model is not always an easy task and can result in relevant additional costs. This post first looks at an “alternative” method which you might find easier to implement in the first step. In fact, when facing any kind of optimization or prediction problem, data mining should not be the only option you’re taking into account.

With our data mining approach we were able to transform the average churn rate of the customers (used by the trivial model first) into a customer specific churn rate. By sorting the customers by this individual churn rate we could address those customers first, for which is was most likely that they will not return. This made our investment more efficient.

For the next approach, we will ignore this individual churn score and go back to the average churn score of 50% from the example of my last post. But now we will be looking at a customer specific profit. Depending on your data, profit may be replaced by some other value measure for the customer (revenue, customer life time value etc.).

In the same way as in the previous approach, we can simply sort our customers by their value in descending order and address the customers with the highest value first. Again, the warning from my last post also applies here: The customers with the highest value might not respond to the $10 voucher in the same way as customers with a lower value. I will get back to this point later.

In the same way as before we can calculate the costs per customer:

Option Costs
Option 1: Customers gets no voucher [average churn rate] x [customer profit]
Option 2: Customer gets voucher [average churn rate] x (1- [prevention success rate]) x [customer profit]
+ [cost of a single voucher]
[average churn rate] x [over all prevention success rate] x [customer profit]
+ [cost of a single voucher]

For my sample data, the chart (now including the trivial approach, the churn score approach and the new profit based approach) looks like this:


As you can see, for my test data both approaches (green and red line) perform almost in the same way. However, this highly depends on your customer base and I adjusted my data in a way not to prefer one of the two approaches. If there is a low variance in your customer values (almost the same value for every customer) the method based on the value will perform almost as bad as the trivial approach (as it gives no benefit then). If the variance is high, the value based method might easily outperform a purely data mining driven score calculation.

Another good idea would be to combine both methods. Instead of ordering the customers by value or churn score we could sort them by the product [customer values] x [churn score]. In this case both parameters are variables and the calculation looks somewhat like this:

Option Costs
Option 1: Customers gets no voucher [churn score] x [customer profit]
Option 2: Customer gets voucher [churn score] x (1- [prevention success rate]) x [customer profit]
+ [cost of a single voucher]

For my sample data the chart output of all four methods shows that the combination is superior (which should be expected in most real world scenarios):


In my example the optimizations using only profit (value) or only churn score give an improvement by about 5.5% to 6% while the combination (purple line in the chart) gives about 10.2%.

For all of these samples we used the same parameters. How does the whole picture changes, if the parameters are changed (for example the average return rate)? And what may happen if we also adjust the last fixed parameter in this model, the value for the voucher itself? This will be the topic of the third and last part of this series.

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