Enhancing intelligent marketing systems: a multi-layer hypernetwork approach integrating evidence theory for influential node identification
Authors: Guo Shuaifeng 
Published: January 2025

Purpose

This study proposes an intelligent marketing system model based on a combination of multi-layer hypernetworks and evidence theory, aiming to address the shortcomings of traditional marketing models in accurately identifying key nodes. We propose a new method to improve the accuracy and response speed of intelligent marketing systems by combining evidence theory with multi-layer hypernetworks. We conducted an experiment using a certain car brand (SUV) as an example, which has a wide customer base in both domestic and international markets and has branches in multiple countries. By analyzing its sales data and user behavior, we evaluated the potential reduction in advertising costs and improvement in user satisfaction that may result from adopting this model.

Design/methodology/approach

The proposed model begins with the development of a user interest model, which is subsequently converted into a user label model based on user behavior and a rating matrix. A multi-layer aggregation hypernetwork is then constructed to define the network’s topology. An identification framework is established using evidence theory, and the Dempster–Shafer (D-S) evidence combination method is applied to integrate local, positional and global network indicators. Simulation experiments are conducted to evaluate the model’s performance.

Findings

This study proposes an intelligent marketing system model that integrates multi-layer hypernetworks with Dempster–Shafer evidence theory to address the limitations of traditional marketing models in identifying influential nodes. The proposed model is tested in the automotive industry, specifically using sales and user behavior data from a well-known SUV brand operating globally. This industry provides a complex and competitive environment ideal for validating the model’s ability to improve marketing precision. The results demonstrate that the model significantly enhances the accuracy of key node identification, reduces advertising costs by 10–15% and improves customer satisfaction scores to over 90%. Furthermore, preliminary experiments in the retail and e-commerce sectors highlight the model’s adaptability and potential for broader application. By combining local, positional and global indicators, the model effectively optimizes marketing strategies, providing a novel framework for intelligent decision-making in diverse industries. This study selected a well-known SUV car brand as the experimental subject. This brand mainly sells SUV models and has a wide customer base worldwide. Its products are known for their high performance and reliability. The brand has millions of customers, and its main markets include North America, Europe and Asia. It has branches in multiple countries and has significant international influence. According to publicly available data, the brand’s annual revenue reaches billions of dollars.

Originality/value

The main contribution of the research is the proposal of a novel intelligent marketing optimization framework based on multi-layer hypernetworks and evidence theory, which can effectively solve the problems of data silos and information asymmetry faced in traditional marketing systems.