Hey CJ, just found your cool blog! Very well explained, and an associated issue with these average metrics is that it doesn't really say much about customer segmentation and outlier values as you allude to.
Have you read Sam Savage's work on the Flaw of Averages? I highly recommend it. One of the most famous examples is the case of the statistician who drowns while fording a river that he calculates is, on average, three feet deep. Plans based on assumptions about average conditions usually go wrong!
ARPU is best used when determining the unit economics of your model.
ARPU can also include non-revenue data. For example, the ARPU can include a value for increased traffic leading to future revenue of user flow in a network. There is LTV there.
Hey CJ, just found your cool blog! Very well explained, and an associated issue with these average metrics is that it doesn't really say much about customer segmentation and outlier values as you allude to.
Have you read Sam Savage's work on the Flaw of Averages? I highly recommend it. One of the most famous examples is the case of the statistician who drowns while fording a river that he calculates is, on average, three feet deep. Plans based on assumptions about average conditions usually go wrong!
Thanks so much for the kind words and recommendation. I’ll check it out tonight.
ARPU is best used when determining the unit economics of your model.
ARPU can also include non-revenue data. For example, the ARPU can include a value for increased traffic leading to future revenue of user flow in a network. There is LTV there.