informing business strategy with hypothesis testing

the motivation

Using data to help guide business strategy is the bread and butter of any professional data scientist.

Hypothesis testing is especially important, since it allows us to see if an observed correlation is in fact a causal relationship; a particular marketing method genuinely leads to better conversion, certain salespeople are genuinely better at building customer loyalty, a new store layout genuinely produces higher sales, and so forth.

the approach

Given the sheer volume of data that may be at a company's disposal, it is important to structure thinking carefully. One way to do this is by creating an issue tree, which breaks down a single big problem into progressively smaller 'issues' that we can tackle more easily.

Suppose we were consulting for a food trading company, Northwind. If our goal was to think of ways to increase their profit, we could construct the following basic issue tree:

We can already start to think about hypotheses that we could test. The results of these hypothesis tests can then be turned into recommendations for the company's management. Because we used the issue tree, we know automatically that our analysis will be relevant and actionable.