It takes the average reader 3 hours and 30 minutes to read Big Data Market Segmentation by Ying Liu
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Market segmentation is a multicriterion problem. This dissertation addresses the multicriterion nature of market segmentation with a new unified segmentation model that is derived from a multiobjective conceptual framework. The unified model elegantly solves the intrinsic antagonistic problem of market segmentation by generating a set of Pareto optimal solutions that represent different tradeoffs among multiple conflicting objectives. This dissertation develops an innovative implementation named Multicriterion Market Segmentation using Evolutionary Algorithm (MMSEA). Based on multiobjective evolutionary algorithms, MMSEA overcomes many limitations and disadvantages of existing methods by optimizing multiple objectives simultaneously, searching for globally optimal solutions and generating a set of Pareto optimal solutions. It also suggests the interesting solutions based on the geometric characteristics of Pareto front. The method was applied to customer value and benefit segmentation for the cell phone service market (a descriptive segmentation model) and customer response segmentation for a national retailer (two predictive segmentation models). The empirical evaluation shows that the proposed unified market segmentation model and solution techniques provide the decision makers with many insights and enhanced flexibility that are missing in existing market segmentation methods.
Big Data Market Segmentation by Ying Liu is 206 pages long, and a total of 52,736 words.
This makes it 70% the length of the average book. It also has 64% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 4 hours and 48 minutes to read Big Data Market Segmentation aloud.
Big Data Market Segmentation is suitable for students ages 12 and up.
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