Our next interviewee is Patrik Punco, Marketing Analyst at German media company, NOZ Medien. Patrik is presenting a lighting talk ‘Subscription Analytics with focus on Churn Pattern Recognition in a German News Company’ at EARL London.
Ruth Thomson, Mango’s Practice Lead for Strategic Advice chatted to Patrik about the business need for his project, what value it created for the business and any learnings and recommendations he had for other businesses interested in using predictive analytics and machine learning to reduce churn.
Firstly and most importantly, thanks Patrik for this interview. We’d love to know what was the business need or opportunity that prompted your project?
There is a structural change happening in media companies in Germany and also globally.
There is a decline in sales of print products with an increase in digital products. As with many media companies, our print products still provide significant income and we want to reduce the decline of print customer numbers with churn prevention strategies.
If we can identify which customers are likely to churn and why, we can put in place targeted customer loyalty activities to both improve customer experience and reduce churn. We saw an opportunity to use predictive modelling and machine learning to achieve these goals.
What were the key elements of your project?
Most importantly, we were mindful of our customers privacy and made sure customers were fully informed about how their data would be used and had the appropriate permissions in place.
We started with a strong understanding of our business and our current churn reduction strategies. This understanding informed the 115 variables we identified as important in the customer lifecycle. We combined data from our SAP system with data from external sources such as delivery data. Next we tested different models to find the one which delivered the best results.
As a result, we were able to choose the 1% of customers most likely to churn to include in our customer loyalty activities.
We ran A/B tests to measure the impact of our work.
What was the impact? How did you measure value?
Using our A/B tests, we were able to quantify the reduction in churn and the reduction has been significant and financially valuable to the company.
Overall the project has been a success so much so that we are extending and building on the work.
What would you say were the critical elements that made the project a success?
R ecosystem helped us not only to implement predictive modelling but also to work much faster and efficiently. For example using data.table and Rcpp package reduced the aggregation of customer tenures runtime from over 30 minutes to less than 1 second.
Our Data Mining Methodology: We had a complex set of data to prepare for this project, it wasn’t simple or easy. I think the methodology we used was critical to the success of this project. We focused on maximisation of churn lift values that we obtain from the model compared to the overall performance of the print segment. On this basis, we were able to build the A/B testing strategies.
Understanding the Business: A key element of success is the understanding of our business. What type of business are we? Who are our customers? What are the policies that need to be factored in? Without that there is the risk that the project would not have been structured appropriately and not deliver the expected value. An example of what I mean is the exact understanding of the churn policy followed by the company which had a direct impact on definition and encoding of the response variable.
What other businesses could benefit from this type of use case?
Subscription businesses in all sectors would benefit from using advanced analytics to reduce churn. But the applicability of this use case is even wider than that, any business that has a churn reduction strategy could make it more effective with advanced analytics.
Thank you Patrik!
To hear Patrik’s talk in September and others like his, get your EARL London tickets now – early bird ticket sales end 31 July.