Social Influence on Shopping Using Ant Colony Optimization Technique

Understanding how social influence on purchasing affects customers is critical for businesses looking to increase customer engagement, sales, and the shopping experience. This project's main objective is the development of a model that would identify the market segment that will react effectively to attractive approaches to marketing, such as influence marketing, social mediapromotions, and specific advertisements. To do this, to predict customer responsiveness, we used a variety of regression models, including Random Forest, Support Vector Machine, Ant Colony Optimization, and Normalization. The results of this research provide important details about how various customer categories respond to social influence strategies. In conclusion, Organizations must understand which market segments respond best to these techniques. It enables them to properly manage resources and target particular groups with their marketing initiatives, optimizing their return on investment. Our experiment helps organizations to improve their marketing tactics with the aid of this study. For instance, if a certaincustomer category is shown to be extremely sensitive to influence marketing, a business might commit more resources to this channelto efficiently target that segment.