A Recommendation System Approach To Customer Engagement Using Personalised Ad Strategies

The advent of technological innovation today has paved the way for a new and improved approach to business and decision-making, enabling faster and more informed choices. An example of such is Customer segmentation. This study demonstrates the application of customer segmentation analytics using a dataset of mall customers. Data was collected at four supermarkets in Berlin, namely, Aldi, Penny, Kaufland and Rewe, using a questionnaire. The obtained dataset includes the following variables: spending score, age, gender, and annual income. A total of 253 respondents participated in the study, while only 201 copies of the questionnaire were found usable. The analysis employs the K-Means clustering method to uncover significant patterns in the collected data. By examining respondent income and expenditure habits, the findings segment the customer base into distinct groups according to their shared and contrasting characteristics. This process distinctly illuminated the key attributes and preferences of the various customer clusters. The understanding of these segments could help managers with targeted marketing strategies, merchandise recommendations, thereby enhancing customer experiences, and robust personalised promotions. It was recommended among others that business organisations should adopt Customer segmentation as a marketing strategy to boost sales of products and services.