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

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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?

Example #1: What is going on with our disconnects?

Disconnects0%

25%

50%

75%

100%

Disconnects by Segment

Tough TimesUp and ComersStable FamiliesHappily Retired

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What does this data mean for our business?Is this good? Is this bad?

Disconnects Active base0%

25%

50%

75%

100%

Disconnects by Segment

Tough TimesUp and ComersStable FamiliesHappily 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

Simplified Subscription Customer Turnover Model

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Tenure to Disconnect 10 years 1 year

Churn Rate, Annual 10% 100%

Active Subscribers 10,000 3,000

Average Customer Tenure 5 years 6 months

77% 23%

Customer Type A: Stay & Play B: Churn & Burn

1,000 3,000Connects, Annual

25% 75%

The differences between the connects/disconnects and customer base are systemic and caused by the natural heterogeneity of the consumer base.

Stable Subscriber Base:Connects = Disconnects in each segment

Example #1: What is going on with our disconnects?

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Because the composition of disconnects and stable active customer base are inherently different, no conclusion about segment growth can be made from this comparison.

Disconnects Active base0%

25%

50%

75%

100%

Disconnects by Segment

Tough TimesUp and ComersStable FamiliesHappily 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

Example #2: What is the best segment for churn reduction?

SegmentCount of

SubscribersDisconnects,

JuneJune 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

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

In the first year, a 10% churn reduction among the Type B Subscribers offers the most growth.

Effects of Churn Reduction – Long Term

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

200

400

600

800

1,000

1,200Cumulative Impact of 10% Churn Reduction

A: Stay & Play B: Churn & BurnYears

Type A: Stay & PlayYear Base Connects

(annual)Disconnects,

9% a yearGrowth (annual)

1 10,000 1,000 900 1002 10,100 1,000 909 913 10,191 1,000 917 834 10,274 1,000 925 755 10,349 1,000 931 696 10,418 1,000 938 627 10,480 1,000 943 578 10,537 1,000 948 529 10,589 1,000 953 47

10 10,636 1,000 957 43

Type B: Churn & BurnYear Base Connects

(annual)Disconnects, 90% a year

Growth (annual)

1 3,000 3,000 2,700 3002 3,300 3,000 2,970 303 3,330 3,000 2,997 34 3,333 3,000 3,000 05 3,333 3,000 3,000 06 3,333 3,000 3,000 07 3,333 3,000 3,000 08 3,333 3,000 3,000 09 3,333 3,000 3,000 0

10 3,333 3,000 3,000 0

The impact of the Type B churn improvement will provide a boost in subscribers in the first year, however, the improvement in churn in Type A subscribers will be more sustained and act as an engine for the long term growth.

Example #3: How well are our campaigns working?

Decision: Run a targeted marketing campaign to improve loyaltyMeasure success: Track response and survival rates

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Segment 1 Segment 2 Segment 3 Segment 4

2.1%

1.4%1.1%

0.8%

Campaign Response Rate, %

Segment 1 Segment 2 Segment 3 Segment 4

95.6%

97.5%98.4%

99.1%

45 Day Survival Rate, %

Most likely to disconnect Least likely to disconnect

• Which segment is responding the best?

• Which segment has the best retention rates?

• How do we reconcile these contradicting results?

• How do we tell if we made an impact on customer retention rates?

Measuring against a matched control group

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Segment 1 Segment 2 Segment 3 Segment 4

95.6%

97.5%

98.4%

99.1%

96.2%96.7%

98.7%99.2%

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.

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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