The Place of Review Filters in Local Search

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Posted by David Mihm

In its recent report on „obrazy na płótnie canvas,” PBS Media Shift was the latest mainstream media outlet to cover one of the most controversial topics in all of local search: search engines’ filtering of customer reviews.

The topic first came to prominence four years ago in obrazy na płótnie on Yelp’s aggressive sales pitches — or extortion, depending on your perspective and whom you believe. While I was drukowane obrazy na płótnie canvas of corporate misbehavior on Yelp’s part, the company hasn’t done itself any favors by continuing to allow its field operatives to use deceptive sales tactics. Despite its best efforts to educate both business owners and everyday users of the site, the poor reputation of Yelp’s salespeople continues to contribute to confusion around review filtering among the small business community. I hope to be able to clear up some of that confusion with this post and offer a few tactical tips to help avoid the frustration these filters can cause.

Why review filters exist

As local search usage among the general public has exploded over the last several years, more and more directories have (rightly) seen reviews as a way to:

  • Gauge the offline popularity of a business in their algorithms
  • Provide better insight to searchers into the experience at that business
  • Increase the „stickiness” of their sites by increasing the sense of community
  • Get out of Google’s Panda/Farmer purgatory by adding unique user content

In many ways, Yelp was ahead of its time on all four of these bullet points, and as a result, it had to tackle the inevitable review spam that accompanied its popularity.

Its answer was arguably the first widespread local review filter: an algorithm for detecting and removing spam or suspicious-looking content.  In Yelp’s own words:

For those of you who couldn’t quite keep up with Yelp’s version of drukowane obrazy na płótnie, the primary reasons are:

  • To make sure reviews are left by actual people (not robots)
  • To make sure reviews are left by customers and not just hired third parties
  • To make sure businesses don’t leave reviews of themselves

Yelp’s CEO, Jeremy Stoppelman, recently gave his own slower version of this rationale in a company-produced video:

How review filters work

While I don’t have any detailed knowledge of Yelp’s review filter specifically, many comparable filters seem to kick into action if any of the following is present in the content of the review:

  • Use of extreme adjectives or profanity in the review
  • Over-use of keywords in the review
  • Inclusion of links in the review

Another criterion that also tends to trigger filtering is a sudden burst of reviews preceded by or followed by a long lull between them.

Some of the more sophisticated review filters, including Yelp’s, take a look at user characteristics, too, including:

  • 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
The bottom line is that reviews written by active users have an astronomically-higher likelihood of „sticking” on a local search engine than those written by first-time or infrequent reviewers. And even beyond their stickiness, many local search experts (including myself) speculate that reviews left by active users also influence rankings to a much greater extent than those left by first-time or infrequent reviewers.
Problems with review filters
obrazy na płótnie canvas

“I Can See the Future of Your Google Reviews”by Margaret Shulock is licensed under a Creative Commons Attribution 3.0 Unported License.

The algorithms behind review filters are far from perfect, as many readers probably know, and Yelp is far from the only local search engine with a review filter. In fact, Google+ has probably accrued more drukowane obrazy na płótnie canvas as a result of its filter in 2012 and 2013 than any other site.

Unfortunately, these filters frequently:

  • Lead to obrazy canvas about the experience at a business
  • Remove legitimate reviews, especially from less-sophisticated, less-active customers
  • Discourage new users from leaving reviews

All of which leads to frustration from the standpoint of a small business owner.

Avoiding review filters

Yelp is probably the most aggressive of its peers at enforcing its business drukowane obrazy na płótnie, which also happen to be the most onerous guidelines of any local search engine.  Yelp’s filtering is so aggressive that one in five reviews written on Yelp never shows up on the site!

To sum up those guidelines:

  • Don’t ask anyone to review your business on Yelp.
  • Don’t ask anyone to review your business on Yelp.
  • Don’t ask anyone to review your business on Yelp.

O ye business owner who disobeys those guidelines, beware!  You run the risk of a public shaming.

Although Yelp’s guidelines are considerably more onerous than its peers’, Google+ is not far behind in stringency. However, many local search engines are far less prickly about soliciting reviews from customers, or even incentivizing them, and some.

For those who have been caught in the Google+ review filter, Mike Blumenthal has covered your travails par excellence and has authored a most reasonable response. Joy Hawkins have also given excellent advice on this front.

Review guidelines at major local search engines

Here are direct links to those guidelines at a few of the biggest players:

The review filters of the future

While the search engines may from time to time, the review filter is a local search institution that is .

The primary methods of these filters, though, I think will change pretty dramatically. Rather than judging a review by its content or looking at website behavior (e.g. how many reviews a user has left for other businesses), the explosion in smartphone adoption is enabling a couple of far less easily-manipulated criteria to judge the veracity of a review.

  • Any local search platform operated by a handset maker (Google, Apple, Microsoft, Nokia, …Amazon?) could register the device ID at the time of review and tie it to a bonafide human being.
  • Any local search platform that has implemented mobile payment processing (Google, Apple, …Amazon?, any Square/PayPalHere partner) could disable the ability for a user to leave a review of a retail-category business unless he/she had completed a transaction at the storefront.

And even for those platforms without the handset or payment-processing advantage, requiring location-awareness for users of mobile applications prior to leaving a review seems like a no-brainer (which Yelp has already implemented and Google may be).

For those sites that are more desktop-dependent, widespread adoption of primary social logins (Google+, Facebook, Twitter, etc.) could lead to a baked-in layer of spam-fighting.

As Eric Schmidt.

“Within search results, information tied to verified online profiles will be ranked higher than content without such verification, which will result in most users naturally clicking on the top (verified) results. The true cost of remaining anonymous, then, might be irrelevance.”

In some industries (e.g., DUI law, plastic surgery, psychology), anonymity may be a pre-requisite for any user reviews and these local search platforms may need a Plan B. But for most industries, requiring some sort of verified social profile would solve a lot of problems.

Facebook, of course, has a huge leg up on everyone else based on its knowledge of a user’s social connections. Google+, meanwhile, could look at a user’s activity across Google’s entire range of products (web search, Gmail, YouTube, etc.) to stop spammers in their tracks.

While consumer privacy concerns around these mechanisms for review filtering may arise, many business owners would likely rejoice at a truer, less bug-ridden filtering algorithm and a more accurate and complete representation of their customers’ experience.

Well, that’s enough out of me for this week! How about you? What are some of your strategies for avoiding these dreaded review filters? What other methods of filtering do you see coming to Local Search?

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