Causal inference in word-of-mouth research: methods and results

Authors Seiler, Yao, Zervas
Book Customer Analytics for Maximum Impact: Academic Insights and Business Use Cases
Year 2018
Type Book | Literature Review Paper
Abstract One of the biggest changes in the marketing landscape in recent years has been a shift toward fostering word-of-mouth (WOM) to let consumers advocate on a brand's behalf. Many marketing executives consider online WOM, which has increased dramatically in volume in recent years, one of the most effective forms of marketing. Every second, 6,000 tweets are posted on Twitter, and that volume is growing at around 30% per year. Similar patterns apply to other social media platforms such as Facebook, as well as to platforms that host costumer reviews. TripAdvisor and Yelp host 570 million and 142 million reviews, respectively, and are visited by 455 million and 188 million users each month. However, reliably measuring the impact of WOM on demand is subject to some unique challenges, and many marketing managers admit that measuring WOM effectiveness remains difficult. In this chapter, we outline the current state of the academic literature regarding the impact of online WOM on demand. We first outline measurement challenges in the realm of WOM and how they can be resolved. We then summarize recent findings on the effectiveness of WOM in two domains: customer reviews and online conversations about brands on platforms such as Twitter or other social media. The former are a type of activity that typically occur after consumption and that impose a specific structure (often a rating scale) on consumers' WOM. The latter instead are less structured and can take place before and/or after consumption. These two areas are sufficiently different and shall be treated separately.
URL http://people.bu.edu/zg/publications/wom-causal-inference.pdf
Tags Archival Empirical  |   Consumer Decisions  |   Media and Textual Analysis