Management Respond to Negative Feedback: AI-Powered Insights for Effective Engagement
本研究利用机器学习分析管理者对负面评论的回应策略，发现承担责任、请求直接联系等行为能提升不满顾客的忠诚度和后续评分，但回应文本长度过长反而有负面影响。
The reputation of a business is significantly influenced by online reviews, with negative feedback having the potential to harm a brand's image and dissuade potential customers. To safeguard their image and convert dissatisfied users into loyal ones, businesses must formulate effective strategies for managing negative reviews. This study investigates response strategies aimed at enhancing the relationship between people and organizations among dissatisfied users upon their return. Using AI as a methodology by leveraging machine learning in our research, we managed to achieve remarkable accuracy using only response attributes to predict there is an increase in subsequent ratings of dissatisfied return customers. The study reveals that specific actions taken or planned in response to a user's complaint, a statement accepting responsibility for service failures, and a request for direct contact through phone or email can positively impact user loyalty and elevate subsequent ratings from returning dissatisfied customers. However, there is a noteworthy negative correlation between the length of the response text and the subsequent rating from returning customers. These findings not only provide theoretical insights but also have practical implications, underscoring the value of machine learning and data analytics in effective reputation management.