Abstract
Traditionally, most companies use marketing campaigns to recruit new customers or retain old customers. Customer segmentation is an important technique for a marketing campaign to target the right customers. Most previous clustering algorithms have drawbacks, such as being stuck at local minima. To overcome such drawbacks this study attempts to develop a consumer segmentation model using a swarm intelligence based algorithm, called Particle Swarm Optimization (PSO). The swarm intelligent algorithm has the advantage of using fewer parameters to reach a global optimal solution. In general, the value of customer segmentation is judged by the customer’s lifetime value. Based on many previous researches, the RFM (Recency, Frequency, and Monetary) model is the most well-known model used to compute customer lifetime value. This study calculates the RFM model from a data set into value-based information. Based on this value-based information the PSO algorithm is able to cluster consumers to find customers likely to be the most profitable and valuable. To demonstrate the effectiveness of PSO, we present an empirical case study involving a retail automobile marketing campaign. We compare the performance of the PSO customer segmentation algorithm against that of other segmentation algorithms (K-mean and self-organizing map (SOM)) and hybrid algorithms. The study finds the hybrid S-KMeans -PSO (SOM, K-Means and PSO) algorithms can reach the best performance. Finally, this study proposes effective marketing strategies for two segmented profitable and valuable customers.
Similar content being viewed by others
References
Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180–184
Berger JT (2006) Peeling the customer loyalty onion. Wiglaf J. http://www.wiglafjournal.com
Chan CCH (2005) Online auction customer segmentation using a neural network model. Int J Appl Sci Eng 3(2):101–109
Chan CCH (2008) Intelligent value-based customer segmentation method for campaign management: a case study of automobile retailer. Expert Syst Appl 34(4):2754–2762
Chen C-Y, Ye F (2004) Particle swarm optimization algorithm and its application to clustering analysis. In: Proceedings of the 2004 IEEE International Conference on Networking, sensing and Control Taipei, Taiwan, March 21–23, pp 789–794
Davies DL, Bouldin DW (1979) A cluster separation measure. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1 (2), pp 224–227
Hsieh NC (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Syst Appl 27:623–633
Hu TL, Sheu JB (2003) A fuzzy-based customer classification method for demand-responsive logistical distribution operations. Fuzzy Sets Syst 139:431–450
Hwang H, Jung T, Suh E (2004) A LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Syst Appl 26:181–188
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall Inc, USA
Jiao J, Zhang Y (2005) Product portfolio identification based on association rule mining. Comput Aided Des 37:149–172
Jonker JJ, Piersma N, Poel D (2004) Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Syst Appl 27:159–168
Kao YT, Zahara E (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8:849–857
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. IV, pp 1942–1948
Kim YS, Street WN (2004) An intelligent system for customer targeting: a data mining approach. Decis Support Syst 37:215–228
Kim J, Wei S, Ruys H (2003) Segmenting the market of West Australian senior tourists using an artificial neural network. Tou Manag 21(4):25–34
Kim YS, Street WN, Russell GJ, Menczer F (2005) Customer targeting: a neural network approach guided by genetic algorithms. Manage Sci 51(2):264–276
Kuo RJ, An YL, Wang HS, Chung WJ (2006) Integration of self-organizing feature maps neural network and genetic K-means algorithm for market segmentation. Expert Syst Appl 30:313–324
Pang W, Wang KP, Zhou CG, Dong LJ (2004) Fuzzy discrete particle swarm optimization for solving traveling salesman problem. In: Proceedings of the 4th International Conference on Computer and Information Technology. IEEE CS Press
Rouse M (2015) Customer segmentation definition. http://searchcrm.techtarget.com/definition/customer-segmentation
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp 69–73
Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp 101–106
Shin HW, Sohn SY (2004) Segmentation of stock trading customers according to potential value. Expert Syst Appl 27:27–33
Swarmintelligence.org (2015) Particle swarm optimization. http://www.swarmintelligence.org/
Tsai CY, Chiu CC (2004) A purchase-based market segmentation methodology. Expert Syst Appl 27:265–276
Urade HS, Patel R (2011) Study and analysis of particle swarm optimization: a review. In: IJCA Proceedings on 2nd National Conference on Information and Communication Technology NCICT(4), pp 1–5, November 2011
Vellido A, Lisboa PJG, Meehan K (1999) Segmentation of the on-line shopping market using neural networks. Expert Syst Appl 17:303–314
Yao J, Li Y, Tan CL (2000) Option price forecasting using neural networks. Int J Manage Sci 28:455–466
Zhang C, Fang Z (2013) An improved K-means clustering algorithm. J Inf Comput Sci 10(1):193–199
Zhang C, Yang Y, Du Z, Ma C (2015) Particle swarm optimization algorithm based on ontology model to support cloud computing applications, J Ambient Intell Hum Comput. Published online 20 March 2015. doi:10.1007/s12652-015-0262-2, pp 1–6
Acknowledgments
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan for financially supporting this research and Empower company for providing a case study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chan, C.C.H., Hwang, YR. & Wu, HC. Marketing segmentation using the particle swarm optimization algorithm: a case study. J Ambient Intell Human Comput 7, 855–863 (2016). https://doi.org/10.1007/s12652-016-0389-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-016-0389-9