Re:Your average revenue per customer is meaningless
by raviparikh on Tuesday April 01, 2014 @05:31PM
(#7511857)
One of the things to take away is that it's often counter-productive to take one summary statistic like "mean" or "median" and try to just optimize that. The correct strategy is likely to be a bit more nuanced and depend on your specific situation.
For example, maybe you have a few different pricing tiers, and instead of optimizing for ARPU, you optimize for the number of people in a given tier that you can up-sell into a higher pricing tier. Now you're optimizing for something meaningful. Note that even if you did a good job in accomplishing this goal, your ARPU might actually go down, if you simultaneously saw a lot of growth in your lower pricing tiers from new signups.
Re:Your average revenue per customer is meaningless
by Retric on Tuesday April 01, 2014 @05:31PM
(#7511863)
There are a lot of ways of looking at this data. But if it's usage based I would suggest ranking things by revenue to see what types of customers are the most valuable.
10% comes from the top 0.1% who spend at least X$,
10% comes from 0.1%-0.9% who spend at least Y$
etc.
The value is to know what types of users you should focus on just be careful as you need a lot of data to make some of this stuff meaningful.
On the technical side, when trying to boil down scads of metric datapoints into a single number, the statistics i usually end up using are the median and the 95th centile (or some higher centile). The median gives some sort of rough idea of where the middle of the main mass distribution is, ignoring the extremes, and the high centile gives an indication of where the top edge of the main mass is, ignoring freak outliers.
Would those be any use for revenue per customer?
Would we be better off with the median and some number that tries to capture the shape of the power law? Something that says "for every dollar you go up in revenue, the number of users drops by X%", or something like that?