Decision-Making With Incomplete Data – Applying McKinsey’s “Day One Hypothesis”

When you are running a company as the CEO or when running sales as the VP of Sales, you frequently deal with incomplete data and missing information. But you need to solve problems, identify root causes (root cause analysis) and ultimately good make decisions that produce business results. So how can you make good decisions with incomplete data?

McKinsey has a good framework called “Day One Hypothesis“.  Basically, at McKinsey you meet with a client and after the first day of gathering information you make an initial hypothesis on what the solution may be.  It is based on collecting some information but yet you are still forming a hypothesis based on incomplete information with some uncertainty.

The Managerial Decision Making” from Charered Management Institute writes that this hypothesis is “a combination of good problem solving skills, pattern matching, and intuition.”

If you are a VP of Sales and you need to troubleshoot and identify root causes of why the sales number was not hit, you can use my 5P Sales Analysis Framework. But you still need to form a few hypotheses along the way.

A good CEO or VP of Sales at a company will bring good problem solving skills as well as pattern matching (based on many years of experience) and intuition to form many such hypothesis to start the process of identifying a solution to every problem of revenue growth.

What else and what are some other ideas on forming a quick hypothesis to solve revenue growth problems?