Abstract

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

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