whether the customer ported their number or not, customer plan, and outbound calling behaviors over the preceding 90 days.
A carefully controlled experiment was run over a period of 3 months, and the results were tabulated and analyzed. The results were excellent: Customers identified as churners by 11Ants Customer Churn Analyzer were a game-changing 1275 percent more likely to be churners than customers chosen at random. This can also be expressed as an increase in lift of 12.75 at 5 percent (the 5% of the total population identified as most likely to churn by the model). At 10 percent, lift was 7.28. Other benefits included the various insights that 11Ants Customer Churn Analyzer provided, for instance, validating things that staff had intuitively felt, such as time on network’s strong relationship with churn, and highlighting areas where product enhancement would be beneficial.
Armed with the information of which customers were most at risk of defecting, 2degrees could now focus retention efforts on those identified as most at risk, thereby getting substantially higher return on investment on retention marketing expenditure. The bottom line is significantly better results for fewer dollars spent.
2degrees head of customers, Matt Hobbs, provides a perspective on why this is not just important to 2degrees but also to their customers: “Churn prediction is a valuable tool for customer marketing and we are excited about the capabilities 11Ants Analytics provides to identify customers who display indications of churning behavior. This is beneficial to both 2degrees and to our customers.”
• To customers go the benefits of identification (if you are not likely to churn, you are not being constantly annoyed by messages asking you to stay) and appropriateness (customers receive offers that actually are appropriate to their usage—minutes for someone who likes to talk, texts for someone who likes to text, etc.).
• To 2degrees go the benefits of targeting (by identifying a smaller group of at-risk customers, retention offers can be richer because of the reduction in the number of people who may receive it but not need it); and appropriateness.
By aligning these benefits for both 2degrees and the customer, the outcomes 2degrees are experiencing are vastly improved.
1. What does 2degrees do? Why is it important for
2degrees to accurately identify churn?
2. What were the challenges, the proposed solution, and the obtained results?
3. How can data mining help in identifying customer churn? How do some companies do it without using data mining tools and techniques?