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Business Tools Blog

How do we measure Usage Churn?

While measuring MRR Churn is tedious, measuring Usage Churn presents unique challenges … that is, if you consider climbing Mt. Everest a unique challenge.

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In order to understand Usage churn, you need to understand the impact of seasonality, days and unpredictable temporary events on usage.  The following factors must be isolated from performance before you can determine whether there is a trended increase or decrease in usage.

  • Seasonal component represents fluctuations that recur more or less regularly from one year to the next.
  • Day component represents the relative importance of the days within the month. This effect is present when activity varies with the day of the week. For instance, Sunday is typically less active than the other days, and the number of Sundays, Holidays, Mondays, etc. in each month varies from year to year.
  • Unpredictable Temporary component represents errors of measurement and unusual events (e.g., strikes, natural disasters, weather, etc.

These are likely to be different by product and vertical customer segment.  Your largest customers may each have their own components.

After the components are removed from the data, you can calculate churn as any negative change in usage after the normalizing for the components shown above.  

  • Disconnects – Services where a disconnect order was completed.  Should be counted as the last month’s usage $
  • Price Decreases (Re-rates) – Services where a new usage rate is applied.  The re-rate is calculated by multiplying the rate decrease by the current month’s usage.
  • Discount increases – Calculated – current month discount less prior month discount.
  • Trended decrease in usage – Any remaining changes in usage revenue after the above were calculated.  Does not include credits and other financial statement revenue adjustments.
Understanding usage requires comparing daily and monthly usage from customers over time.  There are a lot of moving parts in usage data that makes it very important to have good processes that archive usage data on a daily basis.   This data should be easily accessible by product managers so they can identify and forecast each of the above.
 
 

 

 

 

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