The ubiquity of social media platforms facilitates free flow of online chatter related to customer experience. Twitter is a prominent social media platform for sharing experiences, and e-retail firms are rapidly emerging as the preferred shopping destination. This study explores customers’ online shopping experience tweets. Customers tweet about their online shopping experience based on moments of truth shaped by encounters across different touchpoints. We aggregate 25,173 such tweets related to six e-retailers tweeted over a 5-year period. Grounded on agency theory, we extract the topics underlying these customer experience tweets using unsupervised latent Dirichlet allocation. The output reveals five topics which manifest into customer experience tweets related to online shopping—ordering, customer service interaction, entertainment, service outcome failure, and service process failure. Topics extracted are validated through inter-rater agreement with human experts. The study, thus, derives topics from tweets about e-retail customer experience and thereby facilitates prioritization of decision-making pertaining to critical service encounter touchpoints.

无处不在的社交媒体平台促进了与客户体验相关的在线聊天的自由流动。Twitter 是一个著名的分享体验的社交媒体平台，电子零售公司正迅速成为首选的购物目的地。本研究探讨了客户的在线购物体验推文。客户根据不同接触点的遭遇形成的真实时刻发布关于他们的在线购物体验的推文。我们汇总了 25,173 条此类推文，这些推文与 6 家电子零售商在 5 年内发布的推文有关。基于代理理论，我们使用无监督的潜在狄利克雷分配来提取这些客户体验推文背后的主题。输出揭示了五个主题，这些主题体现在与在线购物相关的客户体验推文中——订购、客户服务互动、娱乐、服务结果失败和服务过程失败。提取的主题通过与人类专家的评估者间协议进行验证。因此，该研究从有关电子零售客户体验的推文中提取主题，从而促进与关键服务接触点相关的决策优先级。