Name: Big Data Market Segmentation: New Theories And Methods For Data-driven Customer Segmentation (en Inglés)
Price: 186671.00 COP
Availability: InStock
Author: Chen, Zhihao ; Guo, Feng ; Qian, Feng
ISBN: 9781516984060

portada Big Data Market Segmentation: New Theories And Methods For Data-driven Customer Segmentation (en Inglés)

Formato

Libro Físico

Autor

Feng Guo

Zhihao Chen

Feng Qian

Editorial

Createspace Independent Publishing Platform

Idioma

Inglés

N° páginas

208

Encuadernación

Tapa Blanda

Dimensiones

27.9 x 21.6 x 1.4 cm

Peso

0.68 kg.

ISBN13

9781516984060

Categorías

Tecnología de la información: cuestiones generales

Big Data Market Segmentation: New Theories And Methods For Data-driven Customer Segmentation (en Inglés)

Feng Guo (Autor) · Zhihao Chen (Autor) · Feng Qian (Autor) · Createspace Independent Publishing Platform · Tapa Blanda

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Reseña del libro "Big Data Market Segmentation: New Theories And Methods For Data-driven Customer Segmentation (en Inglés)"

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.