Google’s New Algo Sics Uncle Guido on Your Business
Don’t mess with Google or its users, especially if what you did makes The New York Times news. On November 26, NYTimes.com published a story that highlighted how a bad business could still rank high in Google because a ton of bad reviews elevated the company in a search. But upon further review by Byrne Hobart at Search Engine Land, bad reviews weren’t the culprit at all. Nonetheless, Google quickly took action to create an algorithm that sends Uncle Guido to bust your kneecaps if you make Google look bad are bad to your customers.
OK, OK, so maybe Google did do it for altruistic reasons, I don’t know for sure. In fact, Google’s official blog post on the topic says being bad to your customers is bad for business, inferring actual business practices, not spam, is the issue. Google says addressing “extremely poor user experience” is the target of this new algorithm.
But if the actual culprit of the high rankings for the eyeglass company exposed in The Times was a combination of spammy practices, not bad reviews, why didn’t Google say that?
Maybe the spammy site slipped through the cracks and Google didn’t want to admit it; maybe Google didn’t want to draw attention to the spammy practices the business was conducting; maybe Google decided to be the hero and address The Times’ perspective on the story. In any case, Google worked to fix the problem with its do-no-evil motto as fuel.
The New Algorithm: Dictating How Business is Run?
As usual, Google is fuzzy about the details of its algorithm. Google did give us some clues in its blog on how it addressed the problem of bad business, but not what it considered bad business. Let’s look at some of the things Google said it did not do to fix the problem:
- Block the offenders.
- Use sentiment analysis to identify negative remarks and turn those into negative votes.
- Expose user reviews alongside results (although still on the table, Google says).
And now let’s see what Google said it did do to fix the problem:
Instead, in the last few days we developed an algorithmic solution which detects the merchant from the Times article along with hundreds of other merchants that, in our opinion, provide an extremely poor user experience. The algorithm we incorporated into our search rankings represents an initial solution to this issue, and Google users are now getting a better experience as a result.
Ouch. That led me to wonder if Google’s new algorithm only targeted e-commerce or if it somehow affected local brick-and-mortar businesses. With the reviews that are now viewable alongside local businesses in a SERP via the Google Place Search rollout, will Google now be working harder to clarify the overall rating of the business based on user reviews?
And if so, I wonder what implications the following statement from Google could have on both e-commerce and brick-and mortar business:
We can say with reasonable confidence that being bad to customers is bad for business on Google. And we will continue to work hard towards a better search.
Does this mean that you not only have to please Google with your business practices online, but also offline? I could be off the mark with this one, but if so, seems like it might be crossing the line, just a little. How can algorithms truly assess a business entire worth?
It’s no surprise that Google would dive further into a social-based algorithm. Recently, Bruce has been talking about the idea of social replacing links as the “authority.” We’re beginning to see how much social influence is having on the SERPS (see Bruce talk about this in the video below). In another article today by Danny Sullivan at Search Engine Land, he gives insight into an interview he conducted with Google and Bing on what social signals count for ranking.
In typical Google fashion, there are still a lot of questions marks with this new development. The story is sure to unfold in the days to come; in the meantime, what are your predictions? [Before you comment, Michael Gray, yes, this fix seems a little ... unevenly applied, so far. --Susan]