Data-driven power marketing strategy optimization and customer loyalty promotion

Abstract

In the context of intensifying competition within the power market, power companies face the dual challenges of enhancing customer loyalty and optimizing marketing strategies. This study addresses these challenges by employing the long-term and short-term memory (LSTM) network model to analyze data-driven power marketing strategies and their impact on customer loyalty. The LSTM model is trained on a dataset combining time-series power consumption data with customer interaction scores and market response rates. This enables the model to predict and explain customer responses to marketing efforts with greater accuracy. Unlike traditional marketing models, which lack the capacity to capture dynamic customer behavior over time, the LSTM model accounts for both the temporal nature of consumption patterns and static customer feedback, offering a more holistic view. Key findings indicate that improving the quality of customer service interaction and accurately targeting marketing activities significantly boosts customer loyalty. In particular, customer interaction scores and market response rates are the most influential factors driving customer loyalty, providing critical insights for companies to adjust their strategies effectively. This study’s novelty lies in its application of advanced machine learning methods, such as LSTM, to the power industry—a sector traditionally slower to adopt such innovations. By bridging this gap, the research provides actionable recommendations on how power companies can implement data-driven marketing strategies to improve service quality, increase customer retention, and enhance their competitive position in the market. Additionally, the results underscore the model’s effectiveness in forecasting and optimizing marketing outcomes, offering a scalable solution for the evolving power sector. In the power market, companies face challenges in enhancing customer loyalty and optimizing strategies. This study employs the LSTM network model, trained on combined time-series power consumption, customer interaction scores and market response rates data. Unlike traditional models that struggle with dynamic customer behavior capture, LSTM accounts for consumption pattern temporality and static feedback. It outperforms other techniques like Random Forest and XGBoost in handling time-dependent consumption data. The key findings highlight the importance of customer interaction and targeted marketing. By applying LSTM, power companies can better predict customer responses, optimize marketing, improve service quality and enhance competitive position, providing a scalable solution for the evolving power sector.