A Bloomberg Businessweek article this week takes note of how managers are increasingly slicing and dicing data in order to identify opportunities for improving profitability. For instance, instead of looking at consumer survey results for all respondents together, you might pull out the results for all respondents who report a household income greater than $100,000 and say they are grandmothers interested in giving your product to a girl under the age of 5.
To do such segmentation, you’ll need to think out in advance what criteria you might be using. If you didn’t ask respondents for household income, grandparent status, or age and gender of intended gift recipients, you won’t be able to segment the results for the example I used.
A tag line from consulting firm dunnhumbyUSA is, “Average customers don’t exist. Success lies in knowing individuals.” Based on that advice in the past, grocery retailer Kroger, probably the best known dunnhumbyUSA client, segments the data gathered from Kroger frequent shopper programs in order to customize promotions and rewards.
The tag line serves well to remind us to personalize our interactions with customers and avoid preconceived stereotypes. Still, for most retailers, it’s helpful to place customers into groups rather than require ourselves to think of each shopper as wholly unique. Consumer Generated Ads (CGAs) are great, but few retailers currently would aim for having a separate media ad for each potential customer. And staff training about servicing customers is possible only if we place people into groups so we can talk about them.
The division probably best supported by consumer psychology research is into promotion-focused—consisting of shoppers looking to enhance their current situation—and prevention-focused—consisting of shopper wanting to avoid losses. Another popular grouping is into Mission Shoppers—who burst into your aisles looking for a specific item or for advice on solving a specific problem—and Possibilities Shoppers—who stroll the store considering what they might buy now or maybe during a future visit.
Whatever categories you use should reflect genuine differences important in you improving your profitability. How to determine that? A statistical technique called “cluster analysis” and its cousin, named “discriminant analysis,” can accomplish this when used by experts working in collaboration with you. Applied to a matrix of customer response measures—such as item selection, point-of-sale data, and questionnaire answers—cluster analysis and discriminant analysis can help you identify the best groupings.
Click below for more:
Use Cluster Analysis on Customer Data
Phrase Consumer Survey Questions Carefully
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