Market Pulse AI™️: A Unified Machine Learning Framework for Marketing Mix and Enterprise Analytics

Abstract

There has been a rise in the complexity of digital marketing environments, and an attendant need for analytical frameworks that are more intelligent, that can incorporate marketing intelligence with enterprise-wide decision making. Existing methods of the Marketing Mix Modeling (MMM) are based on static statistical assumptions and are difficult to deal with high dimensional, heterogeneous, and real-time business data. The study introduces a single machine learning framework called MarketPulse AI™ that integrates customer segmentation, predictive analytics, sales forecasting, sentiment analysis, marketing mix optimization, and enterprise performance assessment into a single intelligent architecture. The machine learning framework combines K-Means clustering, Random Forest, Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) to process structured and unstructured enterprise data. The experimental evaluation proved the superior performance, with a Silhouette Score of 0.81, customer behavior prediction accuracy of 98.2%, sales forecasting MAPE of 3.8% and sentiment analysis accuracy of 96.4%. Moreover, the proposed framework helped to boost customer retention, marketing ROI, and revenue growth by optimizing resource allocation and making decisions based on data. The results illustrate how MarketPulse AI™ empowers digitally transformed organizations to improve marketing's effectiveness and enterprise analytics in a scalable, intelligent, and practical way.