The document discusses how to use data effectively to understand subscriber lifecycle and reduce churn in subscription businesses. It emphasizes the importance of correctly interpreting data to make informed decisions and highlights common misconceptions that can lead to poor outcomes. Recommendations include exploring churn drivers in high-risk segments and applying targeted marketing strategies, all while tracking performance against control groups to measure campaign effectiveness.

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

2. 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
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Incorrect interpretations of the data are very common.
They lead to poor decisions.
Anything that can go wrong in this process?

3. Example #1: What is going on with our
disconnects?
0%
25%
50%
75%
100%
Disconnects
Disconnects by Segment
Tough Times
Up and Comers
Stable Families
Happily Retired
3
What does this data mean for our business?
Is this good? Is this bad?
0%
25%
50%
75%
100%
Disconnects Active base
Disconnects by Segment
Tough Times
Up and Comers
Stable Families
Happily Retired
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

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

5. Example #1: What is going on with our
disconnects?
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0%
25%
50%
75%
100%
Disconnects Active base
Disconnects by Segment
Tough Times
Up and Comers
Stable Families
Happily Retired
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

6. Example #2: What is the best segment for churn
reduction?
Segment
Count of
Subscribers
Disconnects,
June
June Churn
Rate
Segment #1 800,000 8,800 1.1%
Segment #2 200,000 13,000 6.5%
Segment #3 150,000 5,250 3.5%
Segment #4 300,000 7,200 2.4%
Total 1,450,000 34,250 2.4%
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Common Recommendations:
• Research the drivers of high churn in Segment #2?
• Create program to reduce churn for Segment #2?
Monthly Disconnect Report, June

7. Effects of Churn Reduction – Short Term
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What impact would a 10% churn reduction for both group have on
the subscriber base?
Type A: Stay & Play Current
After 10%
Reduction
Churn Rate 10% 9%
Connects, annual 1,000 1,000
Disconnects, annual 1,000 900
Subscriber Growth, Year One 100
Type B: Churn & Burn Current
After 10%
Reduction
Churn Rate 100% 90%
Connects, annual 3,000 3,000
Disconnects, annual 3,000 2,700
Subscriber Growth, Year One 300

8. Effects of Churn Reduction – Long Term
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0
200
400
600
800
1,000
1,200
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Years
Cumulative Impact of 10% Churn Reduction
A: Stay & Play B: Churn & Burn
Type A: Stay & Play
Year Base
Connects
(annual)
Disconnects,
9% a year
Growth
(annual)
1 10,000 1,000 900 100
2 10,100 1,000 909 91
3 10,191 1,000 917 83
4 10,274 1,000 925 75
5 10,349 1,000 931 69
6 10,418 1,000 938 62
7 10,480 1,000 943 57
8 10,537 1,000 948 52
9 10,589 1,000 953 47
10 10,636 1,000 957 43
Type B: Churn & Burn
Year Base
Connects
(annual)
Disconnects,
90% a year
Growth
(annual)
1 3,000 3,000 2,700 300
2 3,300 3,000 2,970 30
3 3,330 3,000 2,997 3
4 3,333 3,000 3,000 0
5 3,333 3,000 3,000 0
6 3,333 3,000 3,000 0
7 3,333 3,000 3,000 0
8 3,333 3,000 3,000 0
9 3,333 3,000 3,000 0
10 3,333 3,000 3,000 0

9. Example #3: How well are our campaigns working?
Decision: Run a targeted marketing campaign to improve loyalty
Measure success: Track response and survival rates
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2.1%
1.4%
1.1%
0.8%
Segment 1 Segment 2 Segment 3 Segment 4
Campaign Response Rate, %
95.6%
97.5%
98.4%
99.1%
Segment 1 Segment 2 Segment 3 Segment 4
45 Day Survival Rate, %
Most likely to disconnect Least likely to disconnect

10. Measuring against a matched control group
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95.6%
97.5%
98.4%
99.1%
96.2%
96.7%
98.7%
99.2%
Segment 1 Segment 2 Segment 3 Segment 4
45 Day Survival Rate, %
Treated Group Control Group
Most likely to disconnect Least 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.

11. 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.
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