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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v7i6.9119</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Exploring the Pathways of Power Grid Marketing Management Based on Big Data</title><url>https://artdesignp.com/journal/PBES/7/6/10.26689/pbes.v7i6.9119</url><author>FengNaihai</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>7</volume><issue>6</issue><history><date date-type="pub"><published-time>2024-12-23</published-time></date></history><abstract>The electric power industry is undergoing profound transformations driven by big data, posing challenges to the traditional power grid marketing management model. These challenges include neglecting market demands, insufficient data support, and inadequate customer service. The application of big data technology offers innovative solutions for power grid marketing management, encompassing critical aspects such as data collection and integration, storage management, analysis, and mining. By leveraging these technologies, power grid enterprises can precisely understand customer needs, optimize marketing strategies, and enhance operational efficiency. This paper explores strategies for power grid marketing management based on big data, addressing areas such as customer segmentation and personalized services, as well as market demand forecasting and response. Furthermore, it proposes implementation pathways, including essential elements such as organizational structure and team building, data quality and governance systems, training, and cultural development. 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