Cognitive analytics management of the customer lifetime value: an artificial neural network approach

Purpose: The purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses. Design/methodology/approach: Starting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV). Findings: After comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model. Research limitations/implications: The results of this methodology are strictly applicable to the retailer which provided the data. Practical implications: Even though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers. Social implications: Customer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer. Originality/value: This paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.