Abstract

The present retail sector is characterized by high levels of competition and shifting consumer behaviors; therefore, the demand for decisions to be fact-based as a means of survival in the market and success has become essential. The subject of this research paper is an end-to-end solution for taking raw customer data and turning it into actionable business intelligence. The general purpose is to illustrate a segmented customer process of behavior forecasting and customer segmentation enabling the retailers to develop segmented marketing programs, enhance customer experience, and also optimize the inventory. The research employs a simulated data set of 446 customer cases with attributes such as demographic indicators, revenues per year, and a firm-specific measure of spending. Customer segmentation through data preprocessing, exploratory data analysis (EDA), and application of unsupervised machine learning algorithms, i.e., K-Means clustering, for dividing customers into distinctly differentiating customer segments. Predictive modeling is also being applied to forecast customer spend behavior. All the analytics pipelines were executed with Python as the programming language using libraries such as Pandas for data manipulation, Scikit-learn for executing machine learning algorithms, and Matplotlib/Seaborn for visualization. The findings create five customer personas that are significantly different from each other and display distinct buying behavior. The research reaffirms the sheer worth of granular customer analytics as a prescription for retailers to capitalize on data assets to deliver strategic value and build long-term customer loyalty.

References

• A. De Caigny, K. Coussement, K. W. De Bock, and S. Lessmann, “Incorporating textual information in customer churn prediction models based on a convolutional neural network,” International Journal of Forecasting, vol. 36, pp. 1563–1578, 2020.
• F. Buttle and S. Maklan, Customer Relationship Management: Concepts and Technologies, 4th ed., Routledge, London, UK, 2019.
• D. Suryadi, “Predicting repurchase intention using textual features of online customer reviews,” in Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, 2020, pp. 1–6.
• F. R. Lucini, L. M. Tonetto, F. S. Fogliatto, and M. J. Anzanello, “Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews,” Journal of Air Transport Management, vol. 83, p. 101760, 2020.
• A. Felbermayr and A. Nanopoulos, “The role of emotions for the perceived usefulness in online customer reviews,” Journal of Interactive Marketing, vol. 36, pp. 60–76, 2016.
• R. L. Oliver, "Whence consumer loyalty?," Journal of Marketing, vol. 63, special issue, pp. 33-44, 1999.
• M. Á. De la Llave, F. A. López, and A. Angulo, “The impact of geographical factors on churn prediction: An application to an insurance company in Madrid’s urban area,” Scandinavian Actuarial Journal, vol. 3, pp. 188–203, 2019.
• W. Verbeke, K. Dejaeger, D. Martens, J. Hur, and B. Baesens, “New insights into churn prediction in the telecommunication sector: A profit driven data mining approach,” European Journal of Operational Research, vol. 218, pp. 211–229, 2012 (revisited relevance for retail analytics).
• A. Barredo Arrieta et al., "Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82-115, 2020.
• P. Biecek and T. Buda, Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models, CRC Press, Boca Raton, FL, USA, 2021.
• T. Sun and G. Wu, “Consumption patterns of Chinese urban and rural consumers,” Journal of Consumer Marketing, vol. 21, pp. 245–253, 2004 (conceptual basis for retail segmentation analysis).
• T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang, and H. Cho, XGBoost: Extreme Gradient Boosting, R Package Version 0.4-2, 2015.
• S. Nanayakkara et al., “Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study,” PLoS Medicine, vol. 15, e1002709, 2018 (methodological parallel for predictive analytics in retail churn models).
• S. Bradlow, B. Gangwar, P. Kopalle, and S. Voleti, "The role of big data and predictive analytics in retailing," Journal of Retailing, vol. 93, no. 1, pp. 79-95, 2017.
• A. Bleier, C. M. Harmeling, and R. W. Palmatier, "Creating effective online customer experiences," Journal of Marketing, vol. 83, no. 2, pp. 98-119, 2019.
• T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business Press, Boston, MA, 2007.
• P. C. Verhoef, K. N. Lemon, A. Parasuraman, A. Roggeveen, M. Tsiros, and L. A. Schlesinger, "Customer experience creation: Determinants, dynamics and management strategies," Journal of Retailing, vol. 85, no. 1, pp. 31-41, 2009.
• A. K. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, no. 8, pp. 651-666, 2010.
• M. R. Solomon, Consumer Behavior: Buying, Having, and Being, 13th ed., Pearson, Hoboken, NJ, 2020.
• V. Kumar and W. Reinartz, Customer Relationship Management: Concept, Strategy, and Tools, 3rd ed., Springer, Berlin, Germany, 2018.