Beyond means: A dynamic framework for predicting customer satisfaction
提出高斯过程模型聚合在线评分，捕捉评分随时间变化及评论异质性，基于Yelp数据验证其预测未来评分的准确性比样本均值高10.4%。
Online ratings influence customer decision-making, yet standard aggregation methods—such as the sample mean—fail to adapt to quality changes over time and ignore review heterogeneity (e.g., review sentiment, a review’s helpfulness). To address these challenges, we demonstrate the value of using the Gaussian process (GP) framework for rating aggregation. Specifically, we present a tailored GP model that captures the dynamics of ratings over time while additionally accounting for review heterogeneity. Based on 121,123 ratings from Yelp, we compare the predictive power of different rating aggregation methods in predicting future ratings, thereby finding that the GP model is considerably more accurate and reduces the mean absolute error by 10.4% compared to the sample mean. Our findings have important implications for marketing practitioners and customers. By moving beyond means, designers of online reputation systems can display more informative and adaptive aggregated rating scores that are accurate signals of expected customer satisfaction.