[Build Backlinks Online] Operation Clean Air: Clearing Up Misconceptions of Yelp's Review Filter

Build Backlinks Online has posted a new item, 'Operation Clean Air: Clearing Up
Misconceptions of Yelp's Review Filter'

Posted by David-Mihm
Last week, the New York Attorney General's "Operation Clean Turf" fined 19
companies a total of $350,000 for writing fake reviews on behalf of their
clients. The case sets a laudable precedent not only for the future of local
search, but for digital marketing more broadly.


While the amount of the fines is hardly Earth-shattering, the outcome of this
operation should give pause to any SEO or reputation-management company
considering quick-and-dirty, underhanded tactics to boost their clients'
rankings, "improve" their clients' reputations, or launch negative attacks on
competitors.


In the wake of this settlement, however, a wave of media coverage and a study
by researchers at the Harvard Business School have clouded the reality of Yelp's
review filterâalready poorly understood by typical business
ownersâeven further. In this piece I hope to dispel four misconceptions
that it would be easy to conclude from these recent publications.



Likely elements of review filters

Review characteristics

Use of extreme adjectives or profanity in the review
Overuse of keywords in the review
Inclusion of links in the review
1-star or 5-star rating (see discussion of HBS study below)

User characteristics

Total number of reviews a user has left on the site
Distribution of ratings across all of a user's reviews
Distribution of business types among all of a user's reviews
Frequency of reviews that a user has left on the site
IP address(es) of the user when leaving reviews

Business characteristics

A sudden burst of reviews preceded by or followed by a long lull between them.
Referring URL string to business page (or lack thereof)

1. "Most aggressive" review filter â "most successful" review filter

Yelp representatives made little effort to contain their glee at being cited
by the NYAG as having the "most aggressive filter" of well-known local review
sites. In an interview with Fortune, Yelp's corporate communications VP spun
this statement by the NYAG as validation that his company's filter was
"presumably the most progressive and successful."


As I stated in the same Fortune story, I agree 100% with the NYAG that Yelp's
filter is indeed the most aggressive. Unfortunately, this aggressiveness leads,
in my experience, to a far higher percentage of false positivesâi.e.
legitimate reviews that end up being filteredâthan the review filters on
other sites.


Google, for example, has struggled for almost as long as Yelp to find the
perfect balance between algorithmic aggression and giving users (and indirectly,
business owners) the benefit of the doubt on "suspicious" reviews. Now that a
Google+ account is required to leave a review of a business, I suspect that the
corresponding search history and social data of these accounts give Google a
huge leg up on Yelp in identifying truly fraudulent reviews.


I'm not necessarily saying that Google, TripAdvisor, Yahoo, or any other
search engine presents the most representative review corpus, but it's a pretty
big stretch for Yelp to equate aggression with success.

2. "Filtered reviews" â "fraudulent reviews"

To Yelp's credit, even they admit that legitimate reviews are sometimes
filtered out by their algorithm. But you sure wouldn't know it by reading a
recently published study by the Harvard Business School.


In a throwaway line that would be easy to miss, the authors state that they
"focus on reviews that Yelp's algorithmic indicator has identified as
fraudulent. Using this proxyâ" they go on to draw fourâactually
fiveâconclusions about "fraudulent" reviews:

Their star ratings tend to be more extreme than other reviews.
They tend to appear more often at restaurants with few reviews or negative
reviews.
They tend to appear more often on independent restaurants rather than chains.
They tend to appear more in competitive markets.


"Fraudulent" 5-star reviews tend to appear more on claimed Yelp pages than
unclaimed ones.



The authors attempt to use statistical equations to justify the foundation of
their study, but the fundamental logic of their equations is flawed. I'm by no
means a statistical wizard, but the authors suggest that readers like me scan
filtered reviews to validate their assumption.

I would only highlight my friend Joanne Rollins' Yelp page, and thousands of
other business owners' pages just like her, as qualitative evidence to rebut
their logic. I don't dispute that Yelp's review filter is directionally
accurate, but it's crazy to assume it's anywhere near foolproof to use it as a
foundation for a study like this. It leads to self-fulfilling prophecy.


In fact, there are five very easy explanations of their conclusions that in no
way lead you to believe that the overlap between filtered reviews and fraudulent
reviews is even close.

Yelp uses star rating as part of its filtering algorithm. This is an interesting
finding, but not applicable to "fraudulent" reviews.
Restaurants with few reviews or negative reviews are engaging in proactive
reputation management by asking customers with positive experiences to review
them. This is simply a best practice of online marketing. While it violates
Yelp's guidelines, by no means does it indicate that the reviews generated by
these campaigns are fraudulent.
Independent restaurants tend to be much more engaged in online marketing than
chains. Speaking from years of personal experience, chains have by-and-large
been very slow to adopt local search marketing best practices, from
search-friendly store locators to data management at local search engines to
review campaigns. Independent small business owners simply tend to be more
engaged in their digital success than corporate managers.
Restaurateurs in competitive markets tend to be much savvier about their
digital marketing opportunities than those in less-competitive, typically rural
markets.
Engaged restaurateurs are more likely to pursue proactive reputation management
campaigns (see bullet-point number two).

While the HBS study highlights a number of interesting attributes of Yelp's
review filter, it's simply impossible to draw the kinds of conclusions that the
authors do about the truthfulness or fraudulence of filtered reviews.
3. "Filtered reviews" â "useless reviews"

I consider my friend Joanne Rollins to be a fairly typical small business
owner. She runs a small frame shop with the help of a couple of employees in a
residential neighborhood of NW Portland. She's not shy about sharing her ire
with Yelp, not only around some of their shady sales practices, but especially
about her customers' reviews getting filtered.


Trying to explain some of the criteria that cause a review to be filtered
simply takes too long, and Joanne is easily frustrated by the fact that a
faceless computer algorithm is preventing testimonials from 13+ human beings
from persuading future customers to patronize her business. On the customer
side, they're usually disappointed that they've wasted time writing comments
that no one will ever see.


But all is not lost when a review is filtered! With permission from the
customer, I encourage you to republish your filtered Yelp reviews on your own
website. There's no risk of running afoul of any duplicate content issues, since
search engines cannot fill out the CAPTCHA forms required to see filtered
reviews.


You as the business owner get the advantage of a few (likely) keyword-rich
testimonials, and your customers get the satisfaction in knowing that hundreds
of future customers will use their feedback in making a purchase decision.
Marking these up in schema.org format would be the icing on the cake.

4. "Filtered reviews" â "reviews lost forever"

A review once-filtered does not necessarily mean a review
filtered-for-alltime. There are steps that I believe will make their review more
likely to be promoted from the filter onto your actual business page:

Complete their personal Yelp profile, including photo and bio information.
Download the Yelp app to their mobile device and sign in.
Connect their Facebook account to their Yelp profile.
Make friends with at least a handful of other Yelpers.
Review at least 8-10 other businesses besides yours.
Leave at least one review with each star rating (i.e. 1-, 2-, 3-, 4-, 5-).

For those customers who are super-frustrated by Yelp's filtering of their
review or with whom you, as a business owner, have particularly a strong
relationship, consider requesting that they undertake at least a couple of those
tactics. I certainly don't guarantee their success, but it's worth a shot.

The reality of Yelp's review filter


As the infographic above demonstrates, Yelp's excitement over the citation
from the NYAG as having the most aggressive filter underlines a fundamental
business problem for the company that I've written about for years.


Yelp's fortunes are tied to their success in selling business owners
advertising. Yet these same business owners:

don't understand how the site works (at best)
think that every Yelp salesperson is out to extort them (at worst)

Despite commendable efforts like their Small Business Advisory Council, Yelp
clearly has a long way to go in educating these business owners. And they
certainly have a long way to go with reining in rogue salespeople.


But the bigger issue is the consistent disconnect with their customers on the
issue most important to their businesses--their guidelines for solicitation and
display of reviews. Until they resolve that inherent conflict, I find it hard to
see how they'll grow their revenues to the levels that Wall Street clearly
expects.
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