This year Ogilvy & Mather is launching a unit within its agency that focuses on cross-cultural marketing as opposed to multicultural marketing.  This is an important shift in how to think about multiple markets and segmentation, and consistent with what we at Versta have been seeing in our research for quite some time.

For decades, marketing has focused on demographic segmentation.  Research in particular has focused on understanding the unique needs and specific messages that appeal to women, for example, or to Hispanics, African-Americans, Asian-Americans, or gay or lesbian consumers.  For many companies, this has fragmented their marketing and messaging even when trying to offer the same goods and services that offer the same consumer benefits.

From the perspective of marketing research, one problem has been that analysts tend to rely on “statistically significant” differences to guide them when presenting findings rather than thinking more broadly about substantively important differences.  In our view, statistically significant differences should only be highlighted if they matter within the larger context of the questions being asked and how the data might be used.

Suppose that your consumer study shows that women are significantly more likely than men to focus on customer service as a reason to purchase, whereas men are significantly more likely to focus on the warranty.  Does this gender difference matter?  It depends, and no test of statistical significance can answer that question.  If you find that 93% of women are focused on service versus just 87% of men, it may be statistically significant, but who cares?  You better focus on service no matter what gender your customers are.  Likewise, if you find that 8% of men care a lot about warranties versus 3% of women, does that mean you should begin targeting men with messages about warranties?  Probably not.

While it is critical to segment your customers and understand important differences among them, market research that focuses narrowly on reporting statistically significant differences is doing a disservice.  It may be leading you towards marketing fragmentation rather than truly effective segmentation.

Wondering whether differences among subgroups in your data really matter, or whether an approach focused on commonalities is more appropriate?  Versta Research can help.  We do not supply reports that look like banner tabs, but rather reports that focus broadly and deeply on what the data mean and how it answers your key business questions.

—Joe Hopper, Ph.D.

]]> http://www.verstaresearch.com/blog/research-trends-in-cross-cultural-marketing/feed/ 0 http://www.verstaresearch.com/blog/when-to-kick-out-a-survey-respondent/ http://www.verstaresearch.com/blog/when-to-kick-out-a-survey-respondent/#comments Thu, 04 Mar 2010 15:16:09 +0000 Joe Hopper http://www.verstaresearch.com/blog/?p=409 Nearly every survey begins with screening questions to ensure that only the people you are trying to reach are included in the survey.  For example, if you are conducting a survey of women, you need to ask about gender and kick out the men.  And because every question costs money, you want to qualify respondents quickly and terminate those who do not belong.

Here is a helpful hint: Do not actually terminate respondents until after you have asked all screening questions.  Suppose you’re trying to reach women over age 30 who own dogs.  You’ll need three screening questions: gender, age, and dog ownership.  First you ask gender.  It’s tempting to have men immediately discontinue, and then after asking age have all those under 30 discontinue, and so on.  Do not do this.  Ask gender, age, and dog ownership of everyone.  You don’t need men in your survey, but knowing their age and whether they own dogs may be useful to you.  Likewise, you don’t need young women, but knowing whether they own dogs may be useful.  We see two common scenarios in which this information becomes extremely useful:

1.  You complete the study, present the findings, and somebody asks, “What if we expand our market to men?”  Having complete screening data lets you estimate how many men there over age 30 who own dogs, which is an important piece of exploring whether to expand the market.

2.  You decide to expand your target group while still in the field, which means relaxing the requirements of who qualifies for the survey.  “Let’s open up our survey to allow women who are age 25 and over,” your client suddenly says.  Having complete screening data tells you in advance how many more women will qualify for the survey and helps you estimate in advance the potential cost and benefit of relaxing this criterion.

Programming a survey to do this is easy, but unfortunately most people don’t.  Give your programmer clear instructions.  The additional cost of asking all screening questions to all respondents who want to take the survey is minimal, and the benefits are potentially huge.  It’s a smarter way to screen respondents.

Need additional help?  Versta Research would be happy to review your survey and assist in any way we can.

—Joe Hopper, Ph.D.

While the Census provides basic tabulations and counts on its website, you can also query the data yourself to answer specific, customized questions that you are unable to find otherwise.  In fact, using this data is so easy that Versta no longer uses the Census’ rather difficult and ever-changing “fact finder” features.  We have downloaded the data along with a codebook, and use a statistics program to run specific queries and counts of anything we need.

Want to know how many single mothers there are between the ages of 40 and 45, with four children living at home?  We can tell you.  We recently had a client who was having a hard time finding the percentage of people in the U.S. age 40 and over.  With the data from the census at our fingertips, it took just a few minutes to get this number, and ship off the answer to our client.

Versta Research Report Using Census Data

The data (called the Public Use Microdata Sample, or PUMS) is available to anyone, for free.  You’ll need a decent database, spreadsheet, or statistics program work to with it (we use SPSS).  Or, to get the exact information you are looking for, contact Versta Research and we can help.

Here is a partial list of attributes covered in the data:

• Age
• Ancestry
• Disability status
• Educational attainment
• Fertility
• Hispanic origin
• Hours worked
• Income
• Industry
• Location
• Marital status
• Mobility status
• Occupation
• Race
• Sex
• Family, subfamily, and household relationships
• Mortgage status and selected monthly owner costs
• Presence and age of own children
• Property value

–Joe Hopper, Ph.D.