Subscriber Lifecycle and Turnover: How to Interpret Your Data and Use It to Reduce Churn

How do we use data to reduce disconnects in subscription business? Standard process to make data-driven decisions: Collect data Interpret the data Make better decisions Measure the results 2 Incorrect interpretations of the data are very common. They lead to poor decisions. Incorrect interpretations of the data are very common. They lead to poor decisions. Anything that can go wrong in this process?

Example #1: What is going on with our disconnects? 3 What does this data mean for our business? Is this good? Is this bad? Common Conclusions: “Tough Times” have higher propensity to disconnect than average subscribers → We are losing more “Tough Times” from the customer base. “Happily Retired” and “Stable Families” are less likely to disconnect → their share is growing

Simplified Subscription Customer Turnover Model 4 10,000 3,000 1,000 3,000

Example #1: What is going on with our disconnects? 5 Common Conclusions: “Tough Times” have higher propensity to disconnect than average subscribers → We are losing more “Tough Times” from the customer base. “Happily Retired” and “Stable Families” are less likely to disconnect → their share is growing

Example #2: What is the best segment for churn reduction? Segment Count of Subscribers Disconnects, June June Churn Rate Segment #1800,0008,8001.1% Segment #2200,00013,0006.5% Segment #3150,0005,2503.5% Segment #4300,0007,2002.4% Total1,450,00034,2502.4% 6 Common Recommendations: Research the drivers of high churn in Segment #2? Create program to reduce churn for Segment #2? Monthly Disconnect Report, June

Effects of Churn Reduction – Short Term 7 What impact would a 10% churn reduction for both group have on the subscriber base?

Effects of Churn Reduction – Long Term 8 Type A: Stay & Play YearBase Connects (annual) Disconnects, 9% a year Growth (annual) 110,0001, ,1001, ,1911, ,2741, ,3491, ,4181, ,4801, ,5371, ,5891, ,6361, Type B: Churn & Burn YearBase Connects (annual) Disconnects, 90% a year Growth (annual) 1 3,000 2, ,3003,0002, ,3303,0002, ,3333, ,3333, ,3333, ,3333, ,3333, ,3333, ,3333,000 0

Example #3: How well are our campaigns working? Decision: Run a targeted marketing campaign to improve loyalty Measure success: Track response and survival rates 9 Most likely to disconnectLeast likely to disconnect

Measuring against a matched control group 10 Most likely to disconnectLeast likely to disconnect Using control groups: 1.Control group must be representative of the treated group (usually achieved by random assignment). 2.The role of control group is to show what would have happened given everything else that is going on in the marketplace. 3.Groups need to be measured in exactly the same way. 4.Control group measures only the type of treatment withheld. There is no need to “rest” either of the groups or keep it clean from any other treatment to obtain quality measurement.

Takeaways Most subscriber businesses have a lot of data. Making sense of the data can be challenging. Incorrect interpretation is very common and leads to poor decisions. Understanding subscriber turnover cycles helps us interpret the data correctly. The turnover model sheds a new light on which segments we should target to grow our business over long term. To refine your targeted segments, use measurement against control when implementing marketing programs. 11