I think we basically agree; you may just be taking issue with my very explicit representation of the formula. All that's really happening here is just a weighted average of daily churn rates. I really think you need to average over a period of time to deal with normal volatility.
While the number '30' is in the formulation is not intended to mean that this metric can only be measured for a month. Rather it's just there to normalize the metric to always be comparable to the monthly rate. It would be very reasonable to take this metric for a month and for every week in the month and see if any of the weeks are substantially higher or lower.
I would be cautious with using this, or anything based on daily churn, to be used in a formula for predictions. If you want to predict what customers will do over x days it is far better to measure what customers have done over x days. What you lose in currency you more than make up for in having taken a direct measurement. Though I would happily use this metric to play in a model with computed weights.
Thanks again, Steven
If you calculate at the wrong time you will severely skew the numbers.
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Its my opinion that if you have daily churn numbers and you want to be the most accurate, simple formula is no longer viable. You should model daily churn against daily sales and create a revenue model. (takes about 2 minutes in excel, less if you have the data already in a spreadsheet).
- If you want a simple formula, you should use the first two formulas you described in the article and just see how the numbers "feel".
In my opinion, anything beyond that introduces unnecessary levels of complexity that may actually make your modeling less valuable.
- In any event, the article is great and really got me to think about churn again. Well done, and I really liked your thoughts.