Enhancing Small Business Customer Engagement through Sentiment Analysis and Predictive Modeling
DOI:
https://doi.org/10.71222/s31adn33Keywords:
small business, customer engagement, sentiment analysis, predictive modeling, data-driven strategy, personalized marketingAbstract
In today's highly competitive business environment, small enterprises face multiple challenges, including limited resources, high customer acquisition costs, and insufficient customer engagement. Customer engagement not only impacts sales and brand loyalty but also directly affects the long-term growth of the business. This paper explores the practical value and strategies of applying sentiment analysis and predictive modeling to enhance customer engagement in small enterprises. First, the study analyzes the current state and challenges of customer engagement for small businesses, including fragmented data, difficulties in personalized marketing, delayed response to customer feedback, and intense market competition. It then discusses how sentiment analysis can identify customer emotions, satisfaction levels, and potential pain points by analyzing feedback, social media comments, and survey data, thereby helping businesses optimize their service and interaction strategies. Predictive modeling, by combining historical behavioral data with emotional information, forecasts future customer behaviors, purchase tendencies, and churn risks, providing data-driven support for personalized marketing and proactive customer care. Furthermore, the paper examines the integration of sentiment analysis and predictive modeling, creating a closed-loop system from emotion perception to behavior prediction. This approach enhances the accuracy and timeliness of customer interactions, enables early detection of potential issues, and improves customer satisfaction and loyalty. Implementation steps are also outlined, including data collection, cleaning, analysis, modeling, and strategy execution, while potential challenges and mitigation strategies are discussed, such as insufficient data, technical costs, employee capability limitations, and privacy compliance concerns. Finally, the paper highlights future development directions, including multi-channel data integration, advanced emotion recognition (e.g., voice and image analysis), AI-driven automation in customer interactions, and continuous optimization of data-driven strategies. The study demonstrates that the combination of sentiment analysis and predictive modeling can significantly enhance customer engagement in small enterprises while providing data-driven decision support, enabling small businesses to achieve growth and sustainable competitive advantage despite limited resources.
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Copyright (c) 2025 Zongze Li (Author)
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