Abstract

Growth and survival in today’s ever-changing business environment require innovation and the will to adapt. All of these depend on the level at which available resources are utilised to reach, acquire and retain the primary source of business survival – the customer. This study considers the supply chain as a critical structure that not only positions the customer at the heart of its success; but will ensure that a business can weather the tumultuous global business environment. The ultimate goal of this project is to explore how a properly segmented customer base can improve the level of optimisation in supply chain management. Although marketing activities have always contributed to supply chain management, designing customer analytic systems with operational needs in mind will greatly improve manufacturing outcomes and minimize information loss that may be encountered while processing customer feedback from a single marketing lens.

The literature was sequentially reviewed to give insight into the field of engagement – retail, provide a background on how customer satisfaction is the ultimate goal of supply chain management as well as give insight into the available technology-driven models for segmenting data and how researchers see each model. This provides a solid basis for the choice of using a hybrid between Recency Frequency Monetary (RFM) segmentation and K-Cluster analysis models for understanding the dataset. This combination worked positively for the set goals because it provided results on product consumption which is critical for our findings to support the optimisation of a supply chain. This would not be the first time a researcher has combined two models (Dogan, et al, 2018; Fader, et al, 2005; Lumsden et al, 2008). There are also arguments in certain depositions that most of the existing segmentation approaches are vulnerable to outliers and their outcomes may be seriously biased (Wang, 2010). The K-means cluster algorithm used in this research is expected to provide better customer understanding, well-designed strategies, and more efficient decisions.

This study has successfully connected various information gathered from our data analysis to key aspects of supply chain management like inventory management.

Customer segmentation to improve supply change management