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Marketing Research: It's Just Not That Complicated

An old (but relevant) shareholder letter from Warren Buffet — a “bible” read for some in the investment community — had this warning:

Investors should be skeptical of… models. Constructed by a nerdy-sounding priesthood using esoteric terms such as beta, gamma, sigma and the like, these models tend to look impressive… [But] Our advice: Beware of geeks bearing formulas.
(Warren Buffet, Shareholder Letter, 2008)


Honestly, the same advice goes for us “marketing research analysts bearing formulas”. Sure, I could go on about the benefits of using K-means clustering versus CHAID/CART or the different types of discrete choice models for determining optimal concept scores (and I reserve the right to blog about this in the future!) but that is not the point and doesn’t move the conversation forward.

Here’s what does: Asking the critical question that empowers you to know you’re making the right innovation and research methodology decision. That question is as follows: “What outcome am I trying to reach from this research?” Generally, the methodologies fall under two categories: divergent and convergent.

Divergent research questions are expansive in nature. Questions such as: “What else might my customers expect from my product?”, “What are the unmet needs in the marketplace?”, and “How can we transform our category?” are those that seek to broaden our knowledge. A rule of thumb is that divergent questions generally require qualitative and observational research techniques — in-depth one-on-one interviews, focus groups or ethnographic research — which are longer, open-ended and conversational research techniques.

Convergent research questions generally want to narrow down the set of choices.  Questions such as: “Who is the right target market for my services?”, “What is the right price for my new product?”, or “Which product should I launch?” are examples of convergent questions. Typically, these types of questions require quantitative research tools — sampling hundreds (if not thousands) of customers or consumers to make the right convergent decision.

Of course there are exceptions to the above, but this is a good starting point as you think through your innovation challenges and research/insight needs.  

So our advice is the same as Buffet’s: Beware of (researchers) bearing formulas. Ask the simple question, demand the simple answer.

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Discussion:    Add a Comment | Comments 1-2 of 2 | Latest Comment

July 9, 2010 3:40 PM

Javier,

I think you make a good point, and I tend to be very skeptical of "black boxes" and mathematical solutions devoid of business thinking.

That said, there is equal risk, in my judgment, of oversimplifying. I believe it was HL Menken who said "For every complex problem, there is a solution that is simple, neat, and wrong."

Sound business thinking supported with robust business models, applied appropriately, is an equally strong recipe for breakthrough insights. This, however, is a risky road. Underqualified practitioners, or weak models (or God forbid, both) are major issues faced by the industry today.

Thanks for your post. I enjoy reading the MD point of view.

Peter

June 1, 2011 10:31 AM

I appreciate the clarity this post provides. What I have found is that most businesses and organizations do not understand the differences in the methodologies and try to mix the two.

The light bulb went off for me as I read this and thought back to how clients have mixed the two. Going against better judgement the resulting information tried to be all things and did not satisfy anyone. My experience is that it is tough to collect both types of data in the same study successfully.

Discussion:    Add a Comment | Comments 1-2 of 2 | Latest Comment

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