The Center for Strategy Research, Inc. Vol 3 Issue 4   April 2007


Welcome!

One of our favorite New Yorker cartoons shows a man talking to a woman at a cocktail party and saying:

“According to my Zip Code, I prefer non-spicy foods, enjoy tennis more than golf, subscribe to at least one news-oriented periodical, own between thirty and thirty-five ties, never buy lemon-scented products, and have a power tool in my basement, but none of that is true.”

It always serves to remind us of the limitations of demographic data alone, in predicting reality… the subject of this month’s edition of Research With a Twist!

As always, please click here to send us your thoughts and comments.


Julie Brown
President

Mark Palmerino
Executive Vice President



Go Ask Alice (if you really want to know)

You may remember the classic article written back in the seventies, in which ad exec John O’Toole demonstrated that psychedelic rock icon Grace Slick, and conservative poster child, Tricia Nixon Cox, were demographically identical.

Both were female, about the same age, college-educated, upper-income, and lived in urban households with three people, one of whom was a child. The punch line, of course, was that socially, behaviorally and politically, these two women could hardly have been more different.

Segmentation, the often-used practice of classifying customers or prospects into two or more groups based on measurable demographics (age, income, etc.) and/or trackable behavior (car ownership, frequency of movies rented, etc.) relies on this same type of logic. And while the Slick/Nixon comparison is clearly an aberration, it nicely illustrates the point that demographic and behavioral data alone do not tell the entire story.

Consider this example. Suppose you work at a bank, and your research shows that check bouncers tend to be younger and lower income than the general customer population. It may be true, but by ignoring the motivation behind bounced checks, all “bouncers” are grouped into one category (usually thought of as, “people without enough money”).

An in-depth look at check bouncers, however — one that takes advantage of open-ended questioning — will likely reveal much more hidden beneath the surface. Some possible findings:

  • Some check bouncers are confused by the online banking options and think they’ve covered checks when in fact they haven’t.
  • Some check bouncers are aggressively playing the float and occasionally, they miscalculate.
  • Some check bouncers travel constantly and lose track of, or don’t have time to deal with, their finances.
  • Some check bouncers are, in fact, short on cash.

You get the idea. One behavior, many underlying motivations.

Why does it matter? Good question (thank you for asking). It matters because each of these “motivational subgroups” may be a candidate for a different kind of product, service or communication from the bank.

For example, the “confused online banking” group would likely benefit from improvements to the web site and better messaging regarding its use. The “frequent traveler” segment, on the other hand, might present an opportunity for selling more overdraft protection services.

Whatever the specifics, without digging below the surface to find the cause (i.e. motivation), we’re simply grouping customers and prospects based on the symptom (i.e. check bouncing).

In practice, of course, motivational information is harder to uncover. In order to use this in concert with demographic segmentation, you need a deeper understanding of what people’s needs really are.

And, unlike readily available and objective demographic information, you can’t simply cull through your database as a means of dividing people into “motivational buckets.” You must talk with them on an individual basis, and you need to use a research process that gives them enough room and flexibility to express their needs.

But it’s worth the effort. Adding this additional, motivational understanding permits you to do (at least) two important things:

  1. Improve product and message development. As described in the check bouncing example above, once we’ve done the work to identify the most common motivations among a target audience, this information may be used to modify the products and services sold and the ways their benefits are communicated.

    We don’t need to know before the fact which specific customers will be attracted to which particular offerings (and it’s a good thing, since we couldn’t know this without talking to each of them). However, by making products and services available that cater to the various motivational buckets we’ve identified, customers will self-select into the groups that meet their needs.

  2. Improve the effectiveness of matching product/service offerings to individual customers. A strictly demographic approach to selling relies on age, income, and other simple variables to target offers to specific customers. It’s helpful, but often an oversimplification of more complex issues at work.

    A needs-based, motivational approach overlaid on top of this, however, can yield much better results. With just a handful of well-crafted, research-based screening questions, a salesperson can quickly place prospects into the appropriate motivational subgroup (see Mixology below for more on this). This, in turn, allows the salesperson to offer products and services that are more closely matched to the prospect’s needs and interests.

In conclusion, it’s important to recognize that demographic segmentation is not wrong, of course. It’s simply incomplete, in that it provides a two-dimensional view of what is, in reality, a multi-dimensional world.

By developing an understanding of the motivations of your customers and prospects — and overlaying these on top of standard segmentations — you have an opportunity for developing much more effective and targeted products, services and communications.

As Ms. Slick might observe, it sure beats chasing rabbits.

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Mixology (Putting research into practice)

Believe it or not, placing prospects into motivational subgroups can usually be done with just a few (four or five) well-crafted questions. The trick, of course, is first conducting research that identifies which four or five questions to ask!

Here’s how we do it…

We begin with a series of 30-minute, open-ended interviews with an appropriate cross-section of prospective customers. Our objective is to understand motivations; to learn more about how respondents view the world; and to map these to demographics, but only when appropriate and applicable.

If the research were related to finance, for example, we might ask people to talk about how often they watch their investments, and why; which things make them think about their investments, and why; or what motivates them to spend time on financial management. In the course of the interview, we might identify as many as 150 variables, each of which reflects some aspect of the respondent’s needs.

After coding each variable, we do a factor analysis — a data manipulation technique in which we statistically identify commonalities. The commonalities are where the subgroup definitions lie, and once these are identified, we craft questions which uncover which subgroups a respondent would fall into.

Armed with these questions, an investment advisor (in this example) could determine up front, which products and services to discuss and recommend to a prospective customer.

 

Go Ask Alice (if you really want to know)

Mixology (Putting research into practice)

Twist and Shout

About Us


Interested in seeing how CSR segments markets based on needs, not demographics (who can blame you)?

Follow this link to read a brief case study (750 words) from the retained executive search industry, and to see a specific example of our needs-based segmentation at work.



“To invent, you need a good imagination and a pile of junk.”

— Thomas Alva Edison



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About Us
The Center for Strategy Research, Inc. (CSR) is a research firm. We combine open-ended questioning with our proprietary technology to create quantifiable data.

 

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