Prediction and profitability in market segmentation typing tools-Reference-Cited by-同舟云学术

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Prediction and profitability in market segmentation typing tools

Published:2022-01-07 Issue:4 Volume:10 Page:360-389
ISSN:2050-3318
Container-title:Journal of Marketing Analytics
language:en
Short-container-title:J Market Anal

Author:

Vriens Marco^ORCID,Bosch Nathan,Vidden Chad,Talwar Jason

Publisher

Springer Science and Business Media LLC

Subject

Marketing,Statistics, Probability and Uncertainty,Strategy and Management,Economics, Econometrics and Finance (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1057/s41270-021-00145-4.pdf

Reference80 articles.

1. Ahn, G., Y.-J. Park, and S. Hur. 2021. A membership probability-based under-sampling algorithm for imbalanced data. Journal of Classification 38 (2): 2–15.

2. Allaway, A.W., G. D’Souza, D. Berkowitz, and K. Kim. 2014. Dynamic segmentation of loyalty program behavior. Journal of Marketing Analytics 2 (1): 18–32.

3. Allenby, G., G. Fennell, A. Bemmaor, V. Bhargava, F. Christen, J. Dawley, P. Dickson, Y. Edwards, M. Garrat, J. Ginter, A. Sawyer, R. Staelin, and S. Yanmg. 2002. Market segmentation research: Beyond within and across group differences. Marketing Letters 13 (3): 233–243.

4. Andersen, L., and C. Ho 2011. Life is iterative, so is segmentation. Ipsos Point-of-View white paper.

5. Balakrishnan, P.V., S. Kumar, and P. Han. 2011. Dual objective segmentation to improve targetability: An evolutionary algorithm approach. Decision Sciences 42 (4): 831–856.

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