Improved Multi-index Customer Segmentation Model Research
AUTHORS
Wolfgang Bellotti,University of Plymouth, Plymouth, UK
Daniela N. Davies,University of Liverpool, Liverpool, UK
Y. H. Wang,University of Liverpool, Liverpool, UK
ABSTRACT
Customer segmentation helps the company's strategy formulation and competitiveness enhancement. To better meet customer needs and preferences, companies must recognize the differences of customers and formulate precise marketing strategies. This article focuses on the current customer segmentation background and combines Data mining tools, proposed a multi-index customer segmentation model. Considering the micro and macro perspectives, the traditional indicators are refined, and new segmentation indicators are added. The indicators are weighted by the entropy method. To reduce the time complexity of clustering, factor analysis is used to reduce the data dimension. Finally, the improved K-means clustering algorithm is used to optimize the determination of the K value and the selection of the initial center point to determine the customer segmentation results. The empirical research results on the segmentation of a retailer's membership data show that the improved algorithm is superior to the classic customer segmentation method in terms of clustering compactness and feature division capabilities. With this, it can help companies to improve the level of customer relationship management and the quality of decision-making.
KEYWORDS
Data mining, K-means, Customer segmentation model, RFMPQ model
REFERENCES
[1] M. Kowalczyk and P. Buxmann, “An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study,” Decision Support Systems, vol.80, no.10, pp.1-13, (2015)
[2] P. Sarantopoulos, A. Theotokis, K. Pramatari, “Shopping missions: An analytical method for the identification of shopper need states,” Journal of Business Research, vol.69, no.3, pp.1043-1052, (2016)
[3] A. Dursun and M. Caber, “Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis,” Tourism Management Perspectives, vol.18, no.3, pp.153-160, (2016)
[4] G. J. Krishna and V. Ravi, “Evolutionary computing applied to customer relationship management: A survey,” Engineering Applications of Artificial Intelligence, vol.56, no.1, pp.30-59, (2016)
[5] Y. S. Cho and S. C. Moon, ‘Weighted mining frequent itemsets using FP-tree based on RFM for personalized u-commerce recommendation system,” Lecture Notes in Electrical Engineering, vol.274, no.1, pp.441-450, (2014)
[6] C. H. Park, Y. H. Park, and D. A. Schweidel, “A multi-category customer base analysis,” International Journal of Research in Marketing, vol.31, no.3, pp.266-279, (2014)
[7] J. Kwac, J. Flora, and R. Rajagopal, “Lifestyle segmentation based on energy consumption data,” IEEE Transactions on Smart Grid, vol.9, vol.4, pp.2409-2418, (2018)
[8] C. Raab and S. Tanford, “Segmenting customers by participation: An innovative path to service excellence,” International Journal of Contemporary Hospitality Management, vol.29, no.5, pp.1468-1485, (2017)
[9] M. Bejaei, K. Wiseman, and K. M. Cheng, “Developing logistic regression models using purchase attributes and demographics to predict the probability of purchases of regular and special tyeggs,” British Poultry Science, vol.56, no.4, ppp.425-435, (2015)
[10] P. W. Murray, B. Agard, and M. A. Barajas, “Market segmentation through data mining: A method to extract behaviors from a noisy data set,” Computers and Industrial Engineering, vol.4, no.17, pp.233-252, (2017)
[11] M. Tleis and R. Callieris, “Segmenting the organic food market in Lebanon: An application of k-means cluster analysis,” British Food Journal, vol.119, no.7, pp.1423-1441, (2017)
[12] S. Peker, A. Kocyigit, and P. E. Eren, “LRFMP model for customer segmentation in the grocery retail industry: A case study,” Marketing Intelligence and Planning, vol.35, no.4, pp.544-559, (2017)
[13] A. Lotko, P. A. Korneta, and M. A. Lotko, “Using neural networks in modeling customer loyalty in passenger cars maintenance and repair services,” Applied Sciences, vol.8, no.5, pp.713-729, (2018)
[14] D. L. Huerta- Mu oz, R. Z. Ríos-Mercado, and R. Ruiz, “An iterated greedy heuristic for a market segmentation problem with multiple attributes,” European Journal of Operational Research, vol.261, no.1, pp.75-87, (2017)