Optimizing Third-Party Product Marketing Strategies Using AI-Driven Consumer Analytics

Third party product marketing acts as an important link in identifying consumer interest from business establishments while at the same time providing brand names an opportunity to gain wider access to other outside markets. Nonetheless, conventional marketing communication approaches rarely fit the complex interaction between consumers and products, which negatively impacts targeting, customization level, and performance. This research unveils a novel GNN-based framework to overcome these difficulties for consumer and product data by capturing their interactions. The proposed method presents consumer behaviour as structure graphs, based on the E-commerce Customer Data for Behaviour Analysis dataset, in order to better support and predict customer preferences, improve segmentation and product recommendations. The GNN designed and built using Python DLP has integrated extraordinaire algorithms that support scalability of big data processing and consumer pattern learning. Assessment tests establish that technical outcomes score a classification accuracy of above 92%, which unveils the efficiency of the strategy in delivering useful knowledge. This model assists other platforms targeting the management of third-party relationships in developing better and more effective advertising strategies that improve customer appeal and sales. As well, it helps the consumers make more-informed decisions and therefore enhances their shopping experience. Beyond overcoming existing shortcomings, the study’s contribution is in increasing scalability, flexibility in adjusting to changing consumers’ demands, and itself, and insensitivity to noise. This work provides the keystone for such improvements to mainly be offered by AI-based approaches for different e-commerce marketing strategies.