Simplex Social Spider Optimization with Decision Tree Consumer Behavior Patterns

The population growth and the economic development are growing rapidly as a result of this makes more understanding the population behavior towards buying things. Understanding consumer behavior plays a major role for businesses in order to enhance the product quality and to provide a satisfactory product for consumers. The consumer behavior is considered in various factors based on age, their income, and their previous purchase history b considering all this factors a chart known as Customer Relationship Management (CRM) is made in order to maintain all the details of the customers of that particular company. In this study we proposed Simplex Social Spider Optimization (SSSO)-Decision Tree of the consumer behavior. This technique uses each customer information as a spider web and splits the data based on the feature values. The results indicated that specific features, such as purchase frequency, average transaction value, and product preferences, significantly influenced customer classification outcomes. A decision tree is used for the classification of the consumer behavior. This enhances the predicting and classifying the customer's attitude while purchasing the products. This research highlights the potential of predictive analytics in tailoring marketing strategies and improving CRM. The proposed method have achieved the accuracy of 92.42 % The findings suggest that businesses can leverage these insights to optimize targeted promotions and enhance customer experience, ultimately driving sales and fostering brand loyalty.