Objectives

Segmentation enables an organization to classify their prospects into naturally occurring groups that share similar characteristics. Marketing programs can then be better designed with offerings and messages that meet these segments shared needs.

Results from customer segmentation will enable you to know how to build these programs more effectively. The following screen shot shows some summary results from one of these segmentation models.

Analysis and Discriminant Variables

The following analyzes and discriminators are used to build unique segments:

• Customer survey or historical data is used to identify segmentation variables (needs, wants, values, preferences, etc.) related to the product/service being offered.
• Cluster analysis is applied to the data to build clusters based on the relative importance of these variables; the most important customer attributes for driving sales are also identified in this process.
• Attributes include Demographics (age, income), Psychographics (lifestyle, attitudes, interests), Behavior (spending patterns, product affinity, media preference), and Geography (distance, drive times, competitor locations).
• Discriminant analysis is run to define the combinations of these attributes that best define the clusters.
• The ideal number of clusters/segments and the best attribute descriptions are then determined.
• Segments are measured in terms of profit potential and prioritized.
• Marketing plan (messaging, media, product features, etc.) is fashioned based on what was learned and what would have the greatest appeal to the prospects and customers in each segment.

Benefits

The benefits that can be realized from utilizing this solution:

• Understand what motivates customers and to direct marketing programs to more effectively generate sales.
• Offer customer a better fit between what is offered and what they value the most.