Online review websites like Yelp are an important part of an online reputation management (ORM) strategy. As SEOs, we want consumers to consistently have a positive impression of our brands all the way through the conversion funnel. However, that journey to conversion can be a fragile one. The way a consumer perceives a company can be jolted when negative content surfaces. It can take years to really create a loyal, frequent consumer who has built that judgment over years of brick and mortar quality experiences with the brand.
For large brands, this is certainly the case, but for “mom and pop” shops and restaurants, this process of growing brand loyalty is on a much smaller scale. The experience of going out to eat dinner with your family and having three pleasurable meals does not equal the feeling you might have after reading a scathing review of that same restaurant where a customer was grossly offended by how he or she was treated by the staff. Sure, you may not necessarily be deterred from dining there again, but you will most likely think about that establishment in a slightly different light. And it’s during that period of dissonance from the brand, that same consumer just might see what else is out there.
To avoid this, SEOs use a variety of tactics to help us push down negative content within the SERPs, such as aggressive link-building strategies, content creation, and the shoring up of all of your brand’s social properties. But for companies that provide goods or services, understanding your consumer climate and filtering those messages to find out what people are really saying about your brand should be the foundation of any ORM strategy.
Yelp reviews are vital in this process especially on the local level as it is the most highly trafficked rating and review site for businesses. Retailers now invest heavily into Yelp optimization for business listings in an attempt to influence how their business will be perceived within the online community. Some industry accepted tactics that you can do for the best chance of business visibility include:
- Claiming your business within Yelp
- Incorporating some basic keyword research around restaurant type and potential user queries
- Uploading high quality images to enhance establishment credibility
- Providing the user as much company information as possible, allowing full disclosure to gain user trust
Unfortunately, companies have used unethical tactics to try to gain advantages over their competition within Yelp. Business listings still contain some of these fictitious reviews (both negative and positive) intended to affect user perception. Furthermore, Yelp has been stigmatized for allowing these reviews to still appear within the review aggregator. A research team from Cornell University even took a heavy interest in how the dilution of fake reviews within truthful content harms the entire system, publishing a detailed piece on the topic in 2011. They were soon after hired by a multitude of companies to find similar online consumer insights. To be fair, fake reviews can be hard to spot. In the heavy oriented blogger age we are currently in, we see writing that is of decent enough quality to deviously use language that easily tricks the consumer. Yes, you can look at a paragraph and gauge how descriptive the review really is, or even report reviews that are not 100% accurate in their analysis, but simply having editors or copywriters constantly spot check business listings for suspicious-looking text is neither a reliable nor efficient solution.
Despite the struggles to protect and provide honest and pure content to users, Yelp and other review-based companies have decided to take aggressive action against phony reviews. The following are some examples of the actionable measures that are being leveraged:
- Algorithms to detect and flag potentially phony reviews through detection of repetitive, non-descriptive language.
- A team of undercover writers responding to ads soliciting fake reviews for cash. (A proactive tactic to curb behavior at sources)
- Detection of new accounts solely created for fake reviews. For example, seeing a consistency of incomplete profiles that provide negative reviews)
- Tracking how many total reviews an account has and taking this into account within algorithm (More reviews = less likely to be filtered)
- IP tracking (Detecting accounts that have been created from a single IP)
- Product review verification through a purchase (Currently not being used on Yelp)
The hope is that these tactics would help review sites detect fraud resulting in a consumer-visible warning that would scare away enough businesses from engaging in such efforts in the first place. Yelp creates a red flag for 90 days on a business listing if it has been notified of the solicitation of phony reviews. Yelp wants all business owners to ask themselves: Are buying fake reviews (which generally only result in a half-star boost in rating) worth the shame of receiving the 90 day red flag and the subsequent loss in customer traffic because of it?
Yelp and other companies have been commended for attempting to improve the review filtering system, but the efforts have come with some inherent issues. Rival business owners have been known to falsely report each other when no fake reviews exist just to draw the attention of the review site moderators. While Yelp wants human user feedback to work alongside the algorithm in the monitoring of business listings, the problem lies in the fact that if one business is falsely accused on these major review sites, a tarnished brand image negates all of the positive work for system improvement.
Yelp assures businesses that all cases are “extensively researched” before any consumer advisory is posted, but has the Yelp brand itself already earned a negative reputation? Much like some of tarnished businesses they are trying to help to change the perception within the online community, is Yelping failing in its own ORM?
Yelp brand success and growth over the next few years depends on meeting the challenges of sophisticated and sometimes devious marketing tactics occurring within the space will be the deciding factor.