Global Segmentation – Dealing with Cross-Cultural
Differences in Survey Rating Scale Usage
by
John Colias, Ph.D.
Developing segmentation solutions that are global in scope requires dealing
with cross-cultural differences in scale usage. To a greater or lesser degree,
respondents of different countries or cultures
- Tend to rate high for all questions
- Tend to rate low for all questions
- Bunch responses at end points of the scale.
Given cross-cultural differences in scale usage, marketing research analysts
frequently develop ways to adjust survey responses, so that a particular survey
response value means the same thing regardless of country of origin.
Perhaps the most sophisticated example of this approach is a Hierarchical Bayes
scale usage model developed by Rossi, Gilula and Allenby (2001)1.
This scale usage model estimates mean and standard deviation adjustments for
each individual respondent. Once adjustments are made to respondent attribute
ratings, data across countries may be pooled together and segmentation analysis
proceeds as usual.
Another approach is to avoid scale rating questions altogether. With the Maximum
Difference (MaxDiff) survey task, respondents do not use a rating scale at all,
but rather make choices2. For example, instead of rating the importance of each
attribute on a scale from 1 to 10, they select a most and least important attribute
from among small subsets of the total set of attributes.
For example, in the MaxDiff survey task, the respondent might read, say, four
attribute descriptions and then decide which one is MOST important and which
is LEAST important in making category purchase decisions.
Each respondent would be presented with multiple sets of four attribute descriptions
and would make their choices. The total number of sets and the number of attributes
per set depends on the total number of attributes and attribute complexity.
The selection of which attributes would appear together in each set of four
would be determined by an experimental design. For example, for a total of 20
attributes, we might develop an experimental design with 2 blocks of 15 sets
of 4 attributes. Each respondent would be randomly assigned to a block and would
select most and least important attributes in each of the 15 sets.
The most-least task offers the following key benefits:
- Since the survey task forces respondents to make a discriminating choice
about which statement is the most and which the least influential to them,
there is no possibility to encounter cultural scale bias (e.g., respondents
of a particular culture are high-raters or low-raters for all attributes).
- The most-least survey task is less difficult for the respondent to do than
a full sort and rank, but still produces a full ranking of all statements
for each respondent.
The most and least choices can be analyzed using Latent Class (LC) choice
modeling, which produces distinct segments of customers. Each segment would
have unique attribute importance scores.
Alternatively, most and least choices can be analyzed using Hierarchical Bayes
choice modeling which produces unique attribute importance scores for each individual
respondent. In the Hierarchical Bayes model, segmentation solutions would be
developed through application of clustering algorithms applied to the respondent-level
attribute importance scores.
References
- Rossi, Peter E., Zvi Gilula and Greg M. Allenby (2001) “Overcoming
Scale Usage Heterogeneity: A Bayesian Hierarchical Approach,” Journal
of the American Statistical Association, 96, 20-31.
- Cohen, Steve and Bryan Orme (2004) “What’s Your Preference?”
Marketing Research, 16 (Summer 2004), 32-37.
Copyright © 2005 by Decision Analyst, Inc.
This article may not be copied, published, or used in any way without written
permission of Decision Analyst.
About the Author
John Colias (jcolias@decisionanalyst.com)
is a Senior Vice President and Director of Advanced Analytics at Dallas-Fort
Worth based Decision Analyst. He may be reached at 1-800-262-5974
or 1-817-640-6166.
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