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Case Study
Fort Washington, PA TRC delivers creative research solutions and actionable results via a choice in methods, analytic acumen, and start-to-finish senior-level attention. » See all resourcesshared by TRC. Want to share your content on GreenBook.org? Market Segmentation: One Method, Four ExamplesRajan Sambandam, TRC Effective market segmentation requires an understanding of the market and the skilled art of finding the appropriate segments. TRC gives four examples of this method's application with results. Introduction
Very often, companies shape their market segmentation using the results of market research and analysis. Market segmentation research is not designed to shape the market. Rather, it reveals underlying divisions in the market and characteristics of the market segments that can be used for effective and profitable marketing. At the very least, segmentation research places the steps companies take on a firm factual foundation. Often, it also uncovers characteristics of the market that are not obvious and identifies ways of dividing and approaching the market that will be particularly effective. If these ways are not evident to competitors, the marketing impact of segmentation research can be even more beneficial. At a more tactical level, market segmentation can make the choices a company faces in developing products, services, and marketing messages easier. Often, market segmentation shows that many conceivable combinations of interest in product features, combinations of service needs, or combinations of attitudes are actually very rare in the marketplace. As a result, there is no need for the company to be prepared to deal with these combinations. Effective Segmentation In these circumstances, what counts is a segmentation scheme that the firm can implement to create real marketing advantages. Which scheme is best depends not just on which provides the best description of the market, but also on the company’s strengths and marketing goals:
Thus, the best segmentation analysis is the one that is most useful. Deciding what Data Inputs to Use: Prior to carrying out a segmentation study, a firm should carefully consider what data inputs to use to ensure that the different segments identified can be targeted for actual marketing. If segments cannot be targeted, the most descriptive segmentation scheme may not be very useful. SOM: In our segmentation projects, we have used a neural network based method that allows a computer to “learn” the structure of the market. The specific type of network used is called a "Self-Organizing Map" or SOM. SOMs have important advantages over other more traditional segmentation techniques:
The following brief case studies illustrate some uses our clients have made of market segmentation research using SOMs. Of course, findings are disguised where necessary to protect the proprietary interests of our clients. Case 1: Personal Auto Insurance Buyers
Attitudinal, behavioral, and demographic data were gathered using a mail panel survey of 2000 U.S. households that own auto insurance. Geodemographic and credit information supplemented the survey responses. Segments Identified: The study identified five segments, each making up 17% to 22% of the market.
Thus, attitudes toward distribution and service needs were key factors differentiating the segments. The segments also differed in other attitudes and in their potential profitability, as measured by total auto insurance premiums, other insurance products owned, and loyalty to their insurers. Marketing Outcomes: The study showed which segments the client should target for distribution without agents. It also showed how to define the segments in actual target marketing. As it turned out, the analysis showed strong relationships between segment membership and information available in the databases insurers use in underwriting and direct marketing targeting The study also provided guidance on which marketing messages to use with each segment. Thus, this research provided a major input into our client’s decision about how to proceed with this potential new venture. Case 2: Large Corporations as a Market for Risk Management In this context, our client wanted to accomplish three goals:
For this study, risk managers at about 400 of the 1500 largest U.S. corporations were interviewed. Their answers were supplemented by Dun & Bradstreet data and data on our clients’ relationships with them. (About 40% of the companies were our clients’ customers, to one extent or another.) Segments Identified: The Self-Organizing Map technique identified four segments:
Comparing these segment characteristics to our client's experiences with respondents' firms that fell into the different segments, we found that "innovators" were most likely to buy a broad range of service and did show strong customer retention. Firms in other segments also tended to act as the segmentation suggested they would. Thus, our client’s actual experience confirmed the segmentation findings. Marketing Outcomes: These results contributed to new product/service packages our client developed. Targeting these companies was relatively easy, because like other commercial insurers our client was in touch with risk managers at virtually all of these large corporations. Given this situation, our client asked us to develop a series of questions that could be asked to assign firms to the segments. To read the rest of this case study in pdf format, click here. This article was written by Rajan Sambandam of TRC, a full-service market research provider located in Fort Washington, PA. [Oct 26, 2009] Other Resources By TRCBetter Questions For Segmentation: Use of MAX-DIFF | White Paper Rajan Sambandam, TRC
Database Scoring with Object Based Segmentation | White Paper Rajan Sambandam, TRC
Asymmetry in Product Features: Use of the Kano Method | White Paper Rajan Sambandam, TRC
Conjoint Analysis versus Self-Explicated Method: A Comparison | White Paper Rajan Sambandam, TRC
Product Configurator | White Paper Rajan Sambandam, TRC
How to Measure the Value of a Brand | White Paper Rajan Sambandam, TRC
Asymmetry Analysis | White Paper Rajan Sambandam, TRC
Deriving Value from Research: the Use of Conjoint Analysis for Product Development | White Paper Rajan Sambandam, TRC
Cluster Analysis Gets Complicated | White Paper Rajan Sambandam, TRC
Identifying Feature Importance: A Comparison of Methods | White Paper TRC
Monadic Price Testing vs. Price Laddering | White Paper TRC
New Product Development: Stages and Methods | White Paper Rajan Sambandam, TRC
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