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An Improved Method for the Quantitative Assessment of Customer Priorities

Listed author(s):
  • Srinivasan, V.

    (Stanford University)

  • Wyner, Gordon A.

    (Millward Brown Inc)

Registered author(s):

    Companies constantly seek to enhance customer satisfaction by improving product or service features. Two methods are commonly used to assess customer priorities for product or service features from individual customers: ratings and constant-sum allocation. A common problem with the ratings approach is that it does not explicitly capture priorities; it is easy for the respondent to say that every feature is important. The traditional constant-sum approach overcomes this limitation, but with a large number of (ten or more) features, it becomes difficult for the respondent to divide a constant sum among all of them. ASEMAP (pronounced Ace-Map, Adaptive Self-Explication of Multi-Attribute Preferences) is a new web-based interactive method for assessing customer priorities. It consists of the respondent first grouping the features into two or more categories of importance (e.g., more important, less important). The respondent then ranks the features in each of the categories from the most important to least important thereby resulting in an overall rank order of the features. In order to estimate quantitative values for the priorities, the computer-based approach breaks down the feature importance question into a sequence of constant-sum paired comparison questions. The paired comparisons are chosen adaptively for each respondent to maximize the information elicited from each paired comparison question. The respondent needs to be questioned only on a small subset of all possible paired comparisons. Importances for the features are estimated from the constant-sum paired comparisons by log-linear multiple regression. The empirical context was that of assessing research priorities among fifteen topics from managers of Marketing Science Institute's member companies. The ASEMAP method provided a statistically significant and substantially better validity than the traditional constant sum method.

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    Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 2028.

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    Date of creation: Jun 2009
    Handle: RePEc:ecl:stabus:2028
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