McKinsey Starts with a Hypothesis. Should You?

In their book The McKinsey Mind, Ethan Rasel and Paul Friga breakdown the methodologies used by one of the world's most well-known consulting firms. One of the basic building blocks of the McKinsey method is to begin with a hypothesis. Consultants are trained to use a hypothesis to focus their research and analysis early in the engagement. 

Initially I had a hard time coming to terms with this idea. It seemed to me that if I were to begin much of my data gathering and analysis with the hypothesis in mind objectivity would be thrown out the window. I questioned how we can truly arrive at the best solution for clients if we demonstrate bias in the beginning by formulating a hypothesis too early. I have thought about this a lot in the last few weeks, and I'm beginning to see the value in McKinsey's push to arrive at a hypothesis before much of the work is done.

The most often levied criticism against the consulting profession is that its practitioners fly in, diagnose all the problems with the client company, then fly back out without spending much, if any, time on execution. But if the consultant begins with the hypothesis, and the client understands the hypothesis and its implications something very different happens. If the hypothesis is proven true the client has a very clear and definitive course of action to take. The fact that much of the data gathering and analysis was based on a mutually understood hypothesis means that there will be a lot of objective information to guide the client's post diagnostic actions.

Consider the example of a small specialty manufacturing company. They've been losing market share for three years and hire some outside help to determine why. In talking with the management team the consultant narrows the possibilities down to pricing or lack of design+build capability. Of these two the consultant formulates a hypothesis that the client is losing market share because design+build services are not offered to customers. From here a very efficient scope of work is constructed to gather the data and analyze the results to prove or disprove the hypothesis.

If the hypothesis is proven true the client knows that in order to maintain and gain back market share an investment in design services must be made, and this new capability must be communicated to the marketplace. These are tactical projects well within the capabilities of the management team.

If the hypothesis is proven untrue the client will immediately look at pricing, and may or may not engage the consultant for help. Data will still be gathered and analyzed since it would be unwise to assume the disproving of one hypothesis is proof of an alternative. However, if the initial narrowing of hypotheses was valid there is a very good chance the client will find that pricing is indeed the issue. Data that eventually proved the hypothesis will also guide the client's decisions in changing pricing to retain and regain market share.

Formulating a hypothesis early in the engagement has the benefits of informing data gathering and analysis, and putting together a more concrete implementation plan once the hypothesis is proven or disproven. To make this work I think consultants should build the following into their engagement model.

  • Intentionally structure the engagement into two phases 1) hypothesis formulating and 2) hypotheses proving.

  • Build a sense of urgency around phase 1, hypothesis formulating. Through interviews and due diligence good consultants can whittle down the possibilities and arrive at a consensus with the client about what the most likely hypothesis should be. A sense of urgency means the value added work of data gathering and analysis can begin sooner before the client's budget is wasted educating the consultant on every conceivable problem or inefficiency contributing to less than optimal performance.

  • Educate clients that hypotheses proven false can be just as valuable as hypotheses proven true. The goal is to understand what is going on and what to do about it. Eliminating expensive and time wasting options is extremely important. Had someone taken the time to disprove the hypothesis that online content should drive mass media distribution the AOL Time Warner debacle could have been avoided.

  • Hone hypothesis formulating skills and tool sets. McKinsey does this by curating and managing their accumulated intellectual capital. Knowledge management is essential to getting better at the art and science of hypothesis formulation. Taking the time to write up case abstracts, standardize tool sets and make incremental improvements in processes make us better at delivering value early via thoughtful, informed hypotheses formulation.