Fooled by Average Customer Revenue

Let’s say you are running a business. You have just on-boarded an (expensive) new agency for acquiring customers. After a month, you want to decide if you should continue using them. You do this by comparing the average revenue of customers brought in by them agency, to average revenue of customers from your existing channels. How many customers from that agency do you need, to make an informed decision? [Read More]

Mutual Information – Example with categorical variables

Mutual information and its cousin, the Uncertainty coefficient (Theil’s U) are useful tools from Information Theory for discovering dependencies between variables that are not necessary described by a linear relationship.

Plenty of good material already exists on the subject: see Section 1.6 in “Pattern Recognition and Machine Learning” by Bishop, freely available as PDF online. What I hope to contribute here are some trivial examples, to help build intuition, based on categorical/multinomial variables.

[Read More]