Adaptive Marketing Strategies in Iraq's Oil Sector Informed by Bayesian Structural Time Series Analysis

Abstract

This study aims to critically analyze how marketing strategies within Iraq's oil sector can be adapted using insights derived from time series analysis, specifically employing the Bayesian Structural Time Series (BSTS) model. Despite the Iraqi oil sector's significant contribution to the national economy, there is a gap in understanding how to effectively adapt marketing strategies in response to dynamic economic and geopolitical challenges, limiting stakeholders' capacity to enhance resilience during market volatility. A quantitative research design was utilized, analyzing data from January 2013 to December 2023, including domestic oil demand figures, global oil prices, and exchange rates. The BSTS model was employed to decompose oil price data into trend, seasonal, and regression components, providing predictive insights for strategic marketing adaptations. The study found that trends and seasonal variations significantly influence marketing strategies, with global oil price fluctuations and geopolitical instability being critical external factors. The BSTS model demonstrated substantial predictive power, offering accurate forecasts and facilitating scenario analyses under various market conditions. Adaptive marketing strategies informed by time series analysis positively impact market resilience within Iraq's oil sector. The study recommends a shift towards data-driven decision-making and flexible marketing approaches to maintain competitiveness amid market volatility.