The Wisdom of Crowds
Does higher customer satisfaction on social media translate into higher revenue?
Posted Feb 09, 2018
Conventional wisdom suggests that if the customer is satisfied, then a business is able to generate higher revenue. Today, one state-of-art way to gauge customer satisfaction is to summarize the tone of comments about a particular product or brand on social media, such as tweets on Twitter. Twitter had 319 million active monthly users at the end of the fourth quarter of 2016, which is close to 10 percent of the population in the United States. So the question becomes, does higher customer satisfaction on Twitter translate into higher revenue?
The results may surprise you. A recent paper by Vicki Wei Tang, associate professor at Georgetown University’s McDonough School of Business, examines a large sample of 2,000 firms over a four-year period. Tang finds that there is no systematic association between the aggregated tone of comments about products and services and revenue of the firm that owns those particular products and brands. The aggregated tone of comments is computed as the number of positive comments over the number of non-neutral (both positive and negative) comments. The intuition is the following: only extremely satisfied customers are likely to initiate positive comments and only extremely dissatisfied customers are likely to initiate negative comments voluntarily on social media. As a matter of fact, the vast majority of comments for products and brands are neutral in tone. All neutral comments are ignored in measuring the aggregate tone. Accordingly, the aggregated tone does not fairly represent the broad customer response.
One may quickly point out that social media failed to predict the outcome of the presidential election in 2016. Now, we have systematic evidence that social media also fails in predicting business outcomes. A powerful finding from the study is that a simple count of the times Twitter users mentioned their past purchase actions or intent to purchase a particular product in the future is indicative of revenue both concurrently and in the future. The predictive power varies across contexts. First, such a simple count is more powerful in predicting revenues for consumer-facing companies than companies whose major customers are other business entities. In the context that Twitter is largely a social platform for leisure, consumers are more likely to share their thoughts and experiences on Twitter than purchasing managers of business entities—the differential predictive power is logical. Second, such a simple count is more powerful in predicting revenues for firms with limited advertising activities than firms that spend big bucks on advertising campaigns. Twitter comments can be viewed as consumer-generated brand awareness, while advertising is one channel for producer-generated brand awareness. The finding suggests some sort of substitution between consumer-generated product awareness and producer-generated brand awareness. Third, the predictive power also depends on who initiated the comment—the more positive comments initiated by expert reviewer publications, the higher the sales of the firm.
So, what is the message? A direct take-way is that virtual “word of mouth” can substitute for proactive advertising in generating more sales. If business managers want to get more bang for their buck with their advertising dollars, he or she could cut some spending on products and brands that have a huge presence on social media and allocate more to the subsets of products and brands that have a rather limited presence on social media. Meanwhile, actions need to be taken to ensure that expert reviewers have positive attitudes toward the company’s products and brands. For the investment community, the obvious takeaway is that product information disseminated on Twitter is a leading indicator of revenue. Investors can capitalize the informational advantage by buying (selling) a company whose products and brands have generated quite a bit of buzz (been largely ignored) on social media before the public release of quarterly financial numbers.
Vicki Tang, an associate professor at Georgetown's McDonough School of Business, authored the study, "Wisdom of Crowds: Cross-Sectional Variation in the Informativeness of Third-Party-Generated Product Information on Twitter."