Abstract
Forecasting future behavior of customers has significant importance in businesses. Consequently, data mining and prediction tools are increasingly utilized by firms to predict customer behavior and to devise effective marketing programs. When dealing with multiple time series data, we encounter with the problem that how to use those time series to forecast the behavior of all customers more accurately. In this study we proposed a methodology to create customer segments based on past data, create Segment-Wise forecasts and then discover the future behavior of each segment. The proposed methodology utilizes existing data mining and prediction tools including time series clustering and forecasting, but combines them in a unique way that results in higher level models in terms of accuracy than baseline model. The proposed methodology has substantial application in marketing for any firm in any domain where there is a need to forecast future behavior of different customer group in an effective manner.
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References
Kumar, V., Reinartz, W.: Customer Relationship Management: Concept, Strategy, and Tools. Springer, Heidelberg (2018)
Chiang, W.-Y.: Applying data mining for online CRM marketing strategy: an empirical case of coffee shop industry in Taiwan. Br. Food J. 120(3), 665–675 (2018)
Yildirim, P., Birant, D., Alpyildiz, T.: Data mining and machine learning in textile industry. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 8(1), e1228 (2018)
Lessmann, S., et al.: Targeting customers for profit: an ensemble learning framework to support marketing decision making (2018)
Duan, Y., Cao, G., Edwards, J.S.: Understanding the impact of business analytics on innovation. Eur. J. Oper. Res. 281, 673–686 (2018)
Grover, V., et al.: Creating strategic business value from big data analytics: a research framework. J. Manag. Inf. Syst. 35(2), 388–423 (2018)
Hughes, A.: Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program, 4th edn. McGraw-Hill Companies, Incorporated, USA (2011)
Box, G.E., et al.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)
Brockwell, P.J., Davis, R.A., Calder, M.V.: Introduction to Time Series and Forecasting. Springer, Heidelberg (2002)
Khajvand, M., Tarokh, M.J.: Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Proc. Comput. Sci. 3, 1327–1332 (2011)
Hosseini, M., Shabani, M.: New approach to customer segmentation based on changes in customer value. J. Mark. Anal. 3(3), 110–121 (2015)
Parvaneh, A., Abbasimehr, H., Tarokh, M.J.: Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value. J. Optim. Ind. Eng. 5(11), 25–31 (2012)
Parvaneh, A., Tarokh, M., Abbasimehr, H.: Combining data mining and group decision making in retailer segmentation based on LRFMP variables. Int. J. Ind. Eng. Prod. Res. 25(3), 197–206 (2014)
Hu, Y.-H., Yeh, T.-W.: Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowl.-Based Syst. 61, 76–88 (2014)
You, Z., et al.: A decision-making framework for precision marketing. Expert Syst. Appl. 42(7), 3357–3367 (2015)
Abirami, M., Pattabiraman, V.: Data mining approach for intelligent customer behavior analysis for a retail store, pp. 283–291. Springer, Cham (2016)
Serhat, P., Altan, K., Erhan, E.P.: LRFMP model for customer segmentation in the grocery retail industry: a case study. Mark. Intell. Plann. 35(4), 544–559 (2017)
Doğan, O., Ayçin, E., Bulut, Z.A.: Customer segmentation by using RFM model and clustering methods: a case study in retail industry. Int. J. Contemp. Econ. Adm. Sci. 8(1), 1–19 (2018)
Akhondzadeh-Noughabi, E., Albadvi, A.: Mining the dominant patterns of customer shifts between segments by using top-k and distinguishing sequential rules. Manag. Decis. 53(9), 1976–2003 (2015)
Song, M., et al.: Statistics-based CRM approach via time series segmenting RFM on large scale data. Knowl.-Based Syst. 132, 21–29 (2017)
Dursun, A., Caber, M.: Using data mining techniques for profiling profitable hotel customers: an application of RFM analysis. Tour. Manag. Perspect. 18, 153–160 (2016)
Le, D.D., Gross, G., Berizzi, A.: Probabilistic modeling of multisite wind farm production for scenario-based applications. IEEE Trans. Sustain. Energy 6(3), 748–758 (2015)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques: Concepts and Techniques. Elsevier Science, Amsterdam (2011)
Witten, I.H., et al.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
Tan, P.-N.: Introduction to Data Mining. Pearson Education India (2006)
Aghabozorgi, S., Shirkhorshidi, A.S., Wah, T.Y.: Time-series clustering – a decade review. Inf. Syst. 53, 16–38 (2015)
Montero, P., Vilar, J.A.: TSclust: an R package for time series clustering. J. Stat. Softw. 62(1), 1–43 (2014)
Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Sig. Process. 26(1), 43–49 (1978)
Anantasech, P., Ratanamahatana, C.A.: Enhanced weighted dynamic time warping for time series classification. In: Third International Congress on Information and Communication Technology, pp. 655–664. Springer (2019)
Mueen, A., et al.: Speeding up dynamic time warping distance for sparse time series data. Knowl. Inf. Syst. 54(1), 237–263 (2018)
Chouakria, A.D., Nagabhushan, P.N.: Adaptive dissimilarity index for measuring time series proximity. Adv. Data Anal. Classif. 1(1), 5–21 (2007)
Batista, G.E., et al.: CID: an efficient complexity-invariant distance for time series. Data Min. Knowl. Discov. 28(3), 634–669 (2014)
Cen, Z., Wang, J.: Forecasting neural network model with novel CID learning rate and EEMD algorithms on energy market. Neurocomputing. 317, 168–178 (2018)
Percival, D.B., Walden, A.T.: Wavelet Methods for Time Series Analysis. Cambridge University Press, Cambridge (2006)
Ramos, P., Santos, N., Rebelo, R.: Performance of state space and ARIMA models for consumer retail sales forecasting. Robot. Comput.-Integr. Manuf. 34, 151–163 (2015)
Martínez, F., et al.: Dealing with seasonality by narrowing the training set in time series forecasting with kNN. Expert Syst. Appl. 103, 38–48 (2018)
Hyndman, R., et al.: Forecast: forecasting functions for time series and linear models. In: R Package Version 8.4 (2018)
Desgraupes, B.: Clustering indices, vol. 1, p. 34. University of Paris Ouest-Lab Modal’X (2013)
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Abbasimehr, H., Shabani, M. (2020). Forecasting of Customer Behavior Using Time Series Analysis. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_15
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DOI: https://doi.org/10.1007/978-3-030-37309-2_15
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