In my post about opening cases, I talk about how to take an ambiguous problem and build an initial structure / issue trees around it. Usually they look something like this:
Issue trees are a good way to structure cases, but there is an even better way, called a hypothesis tree.
In today’s article we will discuss what a hypothesis tree is, why they’re more useful than issue trees, when to use them, and how to construct them.
What is a Hypothesis Tree?
A hypothesis tree is the set of all MECE hypotheses that can explain a particular problem.
Instead of organizing your analysis around issues or areas such as the customers / competition / company, you directly organize problem solving around hypotheses.
Why are they more useful than Issue Trees?
Hypothesis trees are much more direct than issue trees. Because you’re organizing around hypotheses, every question you ask / area you explore will be much more focused towards uncovering the issues.
However, they suffer from a weakness. If the situation is ambiguous or you don’t understand the industry well you won’t be able to generate a collectively exhaustive set of hypotheses.
In that case, use an issue tree instead.
How do we construct them?
They’re much the same as issue trees, except each branch of the hypothesis tree is a hypothesis. Let’s examine what this would look like for a case where you have to improve sales force productivity:
(credit: Ian Davis, The McKinsey Approach to Problem Solving)
The only thing I would add to the above diagram is the data that you’d need to examine the validity of each hypothesis.
A hypothesis tree is another way to structure your analysis. It’s much more direct than the typical way of structuring, but it can only be used when you understand the situation well enough to come up a collectively exhaustive set of hypotheses.
As a result, it’s a pretty safe bet for profitability, sales, and cost related cases but for the others it’s safer to use an issue tree.
Hope it helps!