That’s the finding of an Los Angeles Times story this week, which reports that a significant portion of the record $6.4 billion allocated by the hotel industry for upgrades and improvements in 2014 was spent in response to complaints on social media sites like Yelp, Twitter, and TripAdvisor.
This year’s edition of an annual report from New York University’s Tisch Center for Hospitality and Tourism concludes, according to the Times, that “complaints — and compliments — on Yelp, Twitter, TripAdvisor, and other online review sites motivated hotel owners to invest in carpeting, high-speed internet and remodeled lobbies, among other hotel improvements,” an effort that amounted to a 7% spending increase over the previous year.
The report’s author, NYU professor Bjorn Hanson, suggests that nationwide hotel brands such as Marriott, Hilton, and Best Western have pushed for improvements to chain and franchise locations, in large part because of visitors who complain publicly about outdated facilities left to languish during the recession. Some hotel executives, Hanson notes, are even taking guidance from rave reviews of their competitors.
Search marketers have long advocated for review monitoring as a cornerstone in local businesses’ online presence management. Several tools exist to help businesses find, keep track of, and respond to reviews on sites like those mentioned above, as well as Facebook, Google, Foursquare, and other social platforms frequented by consumers.
In recent months, with more consumers using mobile devices as their primary means of going online, review monitoring from companies like MomentFeed and Closely has focused on the mobile consumer as well as the mobile-oriented business owner, in a trend that attempts to close the communication gap between consumers and merchants.
The NYU report, however, suggests another trend pointed in a different direction, where reviews aggregated across multiple store locations can help to create a business case for widespread changes in a nationwide brand. Although the report focuses on physical upgrades, it’s easy enough to envision other use cases for an aggregated approach, where pricing, service features, sales and promotions, and many other aspects of the customer experience are subjected to scrutiny on the basis of broadly sampled online feedback.,
This is a classic example of crowdsourcing, where the efforts of many small contributors form a whole greater than the sum of its parts. In this case, however, the contributors may have no idea they are helping a brand make decisions that entail widespread upgrades and large capital investments.
The ostensible goal of an online review is to let other consumers know what to expect from a business. Secondarily, reviewers might post negative comments in the hope that the business will pay attention and act on their complaints and suggestions. But the data aggregation model implied by the NYU report goes far beyond this. Instead of treating reviews as a collection of anecdotes about customer sentiment, the concept at play is one where businesses mine review data for concrete, actionable suggestions that stand a strong chance — since they originate from actual customers — of meeting very specific customer expectations.
Brands could presumably mine aggregated reviews for insights on any customer experience topic about which they might want to learn more. The key is strength in numbers: A national brand will be more likely to have the critical mass of reviews required in order to move beyond anecdotal evidence and glean statistically significant results.
Consumer comments could effectively act as votes in favor of one course of action over another. Is it more important for a restaurant chain to update its menu or to stay open later on weekdays? Or is there some other needed improvement the chain hasn’t anticipated at all? Review data could turn out to be the most straightforward way to find out.
As noted in the NYU report, competitive intelligence may offer further benefits. National brands likely need to pay attention to not just reviews of their own locations but also those of all relevant competitors. Such data will offer insights into where a brand is falling short of the competition, while the larger dataset of all reviews in a given vertical brings its own intrinsic value, forming the basis for a comprehensive view of consumer sentiment in that vertical. It’s possible the feature that will successfully differentiate a given brand could be found by analyzing large review data sets comprising entire verticals, geographies, or consumer demographics.
In the realm of big data, reviews and social posts become another set of data points to be correlated with payments data, mobile location, CRM data, and other inputs used today by companies such as 4INFO, Factual, and Placed, which are working to build a holistic view of consumer behavior and intent. Visible differentiators like average star ratings may prove less valuable in the long run than the actual content of consumer reviews, once brands mine review data for the insights it contains.