User-generated content, particularly online product reviews by customers, provide marketers with rich data of customer evaluations of product attributes. This study proposes, benchmarks, and validates a new approach for inferring attribute-level evaluations from user-generated content. Moreover, little is known about whether and when insights from product reviews gained in such a way are consistent with traditional research methods, such as conjoint analysis and satisfaction driver analysis. To provide first insights into this question, the authors apply their approach to a dataset with almost one million product reviews from 52 product categories and run conjoint and satisfaction driver analyses for these categories. Results indicate that the consistency between methods largely varies across product categories. Initial exploratory analyses suggest that consistency might be higher for categories characterized by low experience qualities, high hedonic value, and high customer willingness to post online reviews—but further work will be necessary to validate these findings.

用户生成的内容，特别是客户的在线产品评论，为营销人员提供了客户对产品属性评价的丰富数据。本研究提出、基准测试并验证了一种从用户生成的内容推断属性级评估的新方法。此外，对于以这种方式从产品评论中获得的见解是否以及何时与传统研究方法（例如联合分析和满意度驱动因素分析）一致，我们知之甚少。为了对这个问题提供初步见解，作者将他们的方法应用于一个数据集，该数据集包含来自 52 个产品类别的近 100 万条产品评论，并对这些类别进行联合分析和满意度驱动因素分析。结果表明，方法之间的一致性在很大程度上因产品类别而异。