AI-Driven Big Data Analytics for Strategic Marketing and Price Optimization in the Oil and Gas Industry

The oil and gas industry faces persistent challenges due to price volatility, market fluctuations, and evolving customer dynamics. Traditional pricing models and static marketing strategies are increasingly inadequate in responding to these complexities. This paper presents an integrated framework that leverages Artificial Intelligence (AI) and Big Data Analytics to enhance price forecasting accuracy, implement dynamic pricing, and optimize marketing strategies within the oil and gas sector. The proposed framework employs advanced machine learning techniques, including Long Short-Term Memory (LSTM) networks and hybrid deep learning models, for precise oil price prediction. By integrating scalable big data platforms such as Hadoop and Apache Spark, the system efficiently processes large-scale historical, market, and customer datasets. Furthermore, a reinforcement learning-based dynamic pricing model is developed to adapt pricing strategies in real time, while AI-driven customer segmentation techniques enable targeted marketing efforts. Experimental evaluations demonstrate that the AI models reduced forecasting errors by 30% compared to traditional methods, achieving a Mean Absolute Percentage Error (MAPE) of 8.7% using hybrid deep learning architectures. The reinforcement learning-driven dynamic pricing approach led to a 12% increase in revenue and a 9% improvement in customer retention. Additionally, AI-powered customer segmentation enhanced marketing effectiveness, resulting in a 15% increase in conversion rates. The big data infrastructure also improved data processing efficiency by 40%, supporting near real-time analytics. These results highlight the transformative potential of combining AI and big data analytics to drive data-informed decision-making in the oil and gas industry. The framework not only improves operational efficiency but also provides a strategic advantage through adaptive pricing and personalized marketing. This study offers a practical roadmap for energy companies seeking to leverage advanced analytics for sustainable growth and resilience in volatile market environments.