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- W2336630152 abstract "Customer segmentation is a critical business analysis tool that allows organizations to build customer profiles and plan marketing efforts to satisfy the varying demands of different segments. The objective of the present study is to empirically explore the concept of probabilistic-D clustering for segment profiling in a business-to-business (B2B) market. A SAS® macro that can be used on any data set for application of this technique is developed and reported. To the best of our knowledge, probabilistic-D technique has never been empirically tested in a business environment. It was compared with the widely used k- means clustering technique. Findings indicate a better explanation of customer segment profiles using probabilistic-D clustering because k-means seems to force a high percent of observations in segments where cluster membership probabilities are less than 50% as calculated by probabilistic-D clustering macro. These observations are likely to be non-responsive (or less responsive) to the marketing efforts directed towards respective k-means segments. Using a probabilistic-D clustering approach these observations can be targeted differently to improve the effectiveness of marketing communication and promotions." @default.
- W2336630152 created "2016-06-24" @default.
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- W2336630152 date "2011-01-01" @default.
- W2336630152 modified "2023-09-24" @default.
- W2336630152 title "Comparison of Probabilistic-D and k-Means Clustering in Segment Profiles for B2B Markets" @default.
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