Enhancing Customer Engagement through Smart Marketing Strategies: The Role of Predictive Analytics and Deep Learning in the Iraqi Market"

Abstract

This study delves into the transformative potential of predictive analytics and deep learning techniques to enhance customer engagement through smart marketing strategies, with a specific focus on the Iraqi market. In today's highly competitive business environment, understanding and predicting customer behavior is crucial for crafting effective marketing strategies. The research leverages historical pricing data of various commodities from Iraq, employing advanced statistical and machine learning models such as ARIMA and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) layers. The data, spanning multiple years and regions, underwent extensive preprocessing to ensure its suitability for detailed analysis. The ARIMA model was employed to capture and forecast linear patterns in the time series data, while the RNN model aimed to capture more complex, nonlinear temporal dependencies. Our analysis revealed significant fluctuations and spikes in commodity prices, particularly in recent years, indicating a volatile market environment. The performance of these models was evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. Although the RNN model showed a better fit compared to ARIMA, both models faced challenges in accurately predicting the high volatility in the test data. The findings underscore the importance of incorporating additional variables such as economic indicators, political events, and seasonal factors to improve model accuracy. This study provides actionable insights and practical recommendations for businesses in Iraq to develop more robust and adaptive marketing strategies. By leveraging the power of predictive analytics and deep learning, businesses can not only enhance customer engagement but also make more informed decisions, ultimately leading to improved customer satisfaction and business performance.