AI-Integrated Decision Support System for Real-Time Market Growth Forecasting and Multi-Source Content Diffusion Analytics

TL;DR

This paper introduces an AI-driven decision support system that combines multi-source data and advanced neural network architectures to accurately forecast market growth and analyze content diffusion in real-time, aiding digital marketing strategies.

Contribution

It presents a novel hybrid GNN and Temporal Transformer framework for integrated diffusion and influence modeling, with causal inference modules for marketing impact analysis.

Findings

Outperforms existing baselines in six key metrics

Provides interpretable real-time insights into content diffusion

Enhances decision-making in digital marketing

Abstract

The rapid proliferation of AI-generated content (AIGC) has reshaped the dynamics of digital marketing and online consumer behavior. However, predicting the diffusion trajectory and market impact of such content remains challenging due to data heterogeneity, non linear propagation mechanisms, and evolving consumer interactions. This study proposes an AI driven Decision Support System (DSS) that integrates multi source data including social media streams, marketing expenditure records, consumer engagement logs, and sentiment dynamics using a hybrid Graph Neural Network (GNN) and Temporal Transformer framework. The model jointly learns the content diffusion structure and temporal influence evolution through a dual channel architecture, while causal inference modules disentangle the effects of marketing stimuli on return on investment (ROI) and market visibility. Experiments on large scale real-world datasets collected from multiple online platforms such as Twitter, TikTok, and YouTube advertising show that our system outperforms existing baselines in all six metrics. The proposed DSS enhances marketing decisions by providing interpretable real-time insights into AIGC driven content dissemination and market growth patterns.