Slide8_2010Fall_MGMT324

Graphical view of linear ma models utility product w

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Graphical View of Linear MA Models Utility Product = W 1 Attr 1 + W 2 Attr 2 w 1 Utility Attr 1 Attr 2 Attr 1 Utility = U 0 slope = – w 1 / w 2 B C A For simplicity, there are only two relevant attributes Utility = U 1 Utility = U 2 Utility = U 3 Indifference curves Where U 0 < U 1 < U 2 < U 3
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Perception is Vital Consumer attributes Engineering Attributes Example 1: How Boston residents see the US Attr j for j=1,..,m in the MA model = Consumer’s subjective perception of j th attribute level
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Perception is Vital Example 2: Amtrak vs. Flight San Diego-Los Angeles Amtrak: 2-1/4 hours each way, $26 round trip United Airlines: 2-1/2 hours, $110 to $204 Charlotte-Raleigh Amtrak: 3-1/2 hours each way, $38 round trip United Airlines: 2-1/2 hours, $158 to $372 New York-Albany Amtrak: 2-1/2 hours each way, $65 round trip United Airlines: 3-1/2 hours, $68 to $428 Chicago-Milwaukee Amtrak: 1-1/2 hours each way, $38 round trip United Express: 2-1/2 hours, $65 to $372 Even when it’s quicker and cheaper to travel by train, many fly
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Benefit Segmentation Benefit as a segmentation variable Why useful? Consumers are heterogeneous in their relative importance weights Where W j for j=1,..,m: Relative importance weight of j th attribute More relevant for the segmentation purpose than others Other segmentation variables (Geographic, Demographic/Socio- economic, Behavioral, Psychographic) serve as proxies for “Benefit” Utility Product = W 1 Attr 1 + W 2 Attr 2 +…+ W m Attr m More precisely, use relative importance weights Recall that However, “Benefit” is NOT directly observable Identified using marketing research techniques
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Benefit Segmentation
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  • Fall '08
  • Management, Marketing, Priceline, jth attribute, WebHouse Club, Relative importance weight, nonperishable grocery items

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