Riga Technical University (Latvia)
Riga Technical University (Latvia)
The development of information technology (IT) causes an increase in the amount of data to be created, stored and processed for the needs of various organizations. Segmentation as a one of the marketing tools can help organization to promote sales activities and benefit from it. It is important for marketing practitioners and decision makers to understand concept of predictive modelling and have understanding of how to use big data for segmentation purposes. Marketing and Information Technology are blending due to digitalization, statistics is becoming more important due to rise of big data and data mining opportunities. Boarders of different disciplines are becoming vaguer and interconnection of disciplines can be observed more often. The purpose of the study is to create customer segments based on predictive modelling by using big data available in organization. Data for modelling is used from non-banking lending company based in Latvia AS 4finance. The process of data mining is described and performed in the study using data provided by the company. For data mining process and the development of customer segments the authors selected RapidMiner Studio software and used CRISP-DM data mining methodology. Three types of activities were tested to evaluate economic benefit of created segmentation model on overall 11321 customers. All customers were segmented into two groups based on created predictive model – one group contained customers that were predicted to become inactive and second group with customers that were not predicted to become inactive. All customers were split into three groups containing similar split of predicted outcome. Three different types of activities were performed with all three groups.As a result, common characteristics of segmentation and predictive modelling were identified. The results of empirical study show that it is possible to create customer segments by using sophisticated predictive model. This can be achieved without having to write statistical software codes. The study results also show that organization can benefit from implementation of segmentation based on data mining and predictive modelling in key business areas. Segmentation model created during research show economic benefit for the company. Authors also indicate that this segmentation approach can be replicated in different business areas.
Keywords: segmentation, Big Data, predictive modelling, decision tree, RapidMiner. x
JEL Classification: M31, C45.
Cite as: Verdenhofs, A., & Tambovceva, T. (2019). Evolution of customer segmentation in the era of Big Data. Marketing and Management of Innovations, 1, 238-243. https://doi.org/10.21272/mmi.2019.1-20
This work is licensed under a Creative Commons Attribution 4.0 International License
- Adrian, C., Sidi, F., Abdullah, R., Ishak, I., Affendey, L.S., Jabar, M.A. (2016). Big data analytics implementation for value discovery: A Systematic Literature Review. Journal of Theoretical and Applied Information Technology, 93 (2), pp. 385-393.
- Agarwal, R. and Dhar, V., 2014. Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research.
- Azevedo, A.I.R.L. and Santos, M.F., 2008. KDD, SEMMA and CRISP-DM: a Parallel Overview. IADS-DM.
- Chen, C.P. and Zhang, C.Y., 2014. Data-Intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data. Information Sciences, 275, pp.314-347.
- France, S.L., Ghose, S. (2019). Marketing Analytics: Methods, Practice, Implementation, and Links to Other Fields. Expert Systems with Applications, 119, pp. 456-475.
- Goyat, S., 2011. The Basis of Market Segmentation: a Critical Review of Literature. European Journal of Business and Management, 3(9), pp.45-54.
- Howard, J., 2012. From Predictive Modelling to Optimization: The Next Frontier. [Online] Available at: https://www.youtube.com/watch?v=vYrWTDxoeGg [Accessed 13 January 2018]
- Kim, K.Y. and Lee, B.G., 2015. Marketing Insights for Mobile Advertising and Consumer Segmentation in the Cloud Era: AQ–R Hybrid Methodology and Practices. Technological Forecasting and Social Change, 91, pp.78-92.
- Morabito, V. (2015). Big data and analytics: Strategic and Organizational Impacts. Springer. pp. 183.
- Muley, P. and Joshi, A., 2015. Application of Data Mining Techniques for Customer Segmentation in Real Time Business Intelligence. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(4), pp.106-109.
- Nevin, J.A., 1969. Signal Detection Theory and Operant Behavior: A Review of David M. Green and John A. Swets’ Signal Detection Theory and Psychophysics. Journal of the Experimental Analysis of Behavior, 12(3), pp.475-480.
- Quinn, L., Hines, T. and Bennison, D., 2007. Making Sense of Market Segmentation: a Fashion Retailing Case. European Journal of Marketing, 41(5/6), pp.439-465.
- Saggi, M.K., Jain, S. (2018). A Survey Towards an Integration of Big Data Analytics to Big Insights for Value-Creation. Information Processing and Management, 54 (5), pp. 758-790.
- Shmueli, G., 2010. To Explain or to Predict? Statistical Science, 25(3), pp.289-310.
- Smith, W.R., 1956. Product Differentiation and Market Segmentation as Alternative Marketing Strategies. Journal of marketing, 21(1), pp. 3-8.
- Verhoef, P.C., Kooge, E., Walk, N. (2016). Creating value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge: London. pp. 338.
- Wong, E., Wei, Y. 2018. Customer Online Shopping Experience Data Analytics: Integrated Customer Segmentation and Customised Services Prediction Model. International Journal of Retail and Distribution Management, 46(4), pp. 406-420.