Building Customer and Product Networks with Cosine Similarity in Graph Analytics for Deep Customer Insight

Abstract

Creating connections that allow users to share information, experiences, and product recommendations is the main goal of social networks. These networks are essential for assisting companies in comprehending user preferences, behavior, and buying trends. Graph theory is a crucial tool for analyzing and interpreting the intricate relationships found in such systems. It enables a structured depiction of users and their interactions through nodes and edges, offering important insights into the information and influence flow within the network. This idea is used in our customer network model to enhance recommendation and product engagement tactics. We can find users with similar interests and recommend pertinent products by examining the relationships between customers. Two customers are said to have closely aligned preferences and behaviors when their cosine similarity is greater than 70%. This makes it possible for the system to suggest goods that a customer has bought or given a high rating to another customer in the same similarity cluster. Additionally, we can track price sensitivity and market trends by mapping products within a product network. The network analysis enables us to see how a product's price impact on demand in comparison to similar items is affected if it is more expensive than comparable alternatives. All things considered, social network analysis and graph theory together provide a potent method for comprehending customer behavior, improving personalization, and refining marketing tactics for improved business results.