Customer personality analysis and clustering for targeted marketing

A customer’s personality has been stated to be a key determinant in his or her purchasing behavior. Therefore, in today's highly competitive market, understanding customer behavior and preferences is very important for businesses aiming to stay ahead. This research presents a customer personality analysis and clustering of consumers using machine learning into different consumer groups, this kind of clustering is key for targeted marketing strategies. The machine learning technique used in this work is the k-means machine learning clustering algorithm which divides a set of n observations into k clusters with the aim of designating each observation as a representative of a cluster. The model revealed insightful patterns and correlations between customer characteristics and their purchasing behavior. Through comprehensive data analysis and feature engineering, the model identifies key personality traits such as extroversion, openness, conscientiousness, agreeableness, and neuroticism, based on customer interactions, demographics, and psychographic data. The dataset that was used for this work was obtained from kaggle, it originated from different sources such as customer relationship management (CRM) systems, e-commerce platforms, social media platforms, surveys. The dataset comprises information on "people," "products," and "promotion," aiming to understand customer behavior, preferences, and responses to various marketing efforts. The output of this work provides businesses with actionable insights to tailor marketing campaigns and product offerings to individual customer preferences, thereby enhancing customer satisfaction and maximizing profitability.