You may have noticed a change in your rankings over the Christmas period due to Google’s new local ranking algorithm (that launched in the US earlier this year) being rolled out to the UK, Canada and Australia at the end of December.
The update, code named Google Pigeon, is the largest, most influential local update that Google has released and highlights the fact that Google is really pushing local search forward and that local online visibility is more important than ever if you want to attract new patients.
It is early days, but what do we know about the update and how is it affecting the search results?
Searching Google and searching Google Maps has often provided a very different set of results. This changed after the rollout of Pigeon. The algorithm connects web search and map search in a more cohesive way.
Revised local results resulting from the new Pigeon algorithm are “similar to the page rankings of the Google web search,” as a report from Volume Nine explained.
Search Engine Land reported that “this update ties local results more closely to standard web ranking signals.”
The new local search algorithm ties deeper into the site’s web search capabilities, leveraging hundreds of ranking signals, along with search features like spelling correction capabilities, synonyms and Google’s knowledge graph.
Yelp took issue with Google in mid-2014 for apparently disregarding the major local review site in its search results. The compay accused Google of pushing its own reviews ahead of Yelp’s, even when the searcher asked specifically for “yelp” in their query.
Pigeon has corrected this problem, and now puts Yelp-specific queries ahead of its own.
Yelp isn’t the only local review site that got a boost. Sites like Urbanspoon, OpenTable, TripAdvisor, Zagat, Kayak, etc. received higher visibility within the search results following the release of Pigeon.
The queries that return these results may be as generic as [san francisco restaurant] – in other words, the user may not even be searching specifically for reviews or review sites. Essentially, this means that the algorithm places greater weight on directories and the directory listings.
The data from Mozcast registered a major fluctuation following the update. These graphs indicate the changes in the Local Pack, Carousel, and One-box:
According to Search Engine Land, “Google said that this new algorithm improves their distance and location ranking parameters.” While it’s not totally clear what the “distance and location ranking parameters” are, it seems likely that increased specificity will affect dense neighborhoods, also known as “informal space.”
Andrew Shotland wrote last year about “informal space” and the “neighborhood algorithm” update:
In the local search-data world, neighborhoods are known as “informal space,” meaning there are no standard boundaries. So the definition of where a neighborhood starts and stops can be different for everyone. This makes it hard for services like Google to get the neighborhood thing right.
Prior to Pigeon, local results from these dense spaces were hard to parse. Now, with Pigeon’s increased specificity, the algorithm is more accurate. But what does this accuracy entail? I offer the following conjectures:
The algorithm will return better results for queries that use both the conventional term for a local neighborhood and the colloquial term for the same neighborhood.
A neighborhood can be known by several different names, depending on who you’re talking to. A map-reading stranger may visit a new area and call it “Uptown,” just like his map tells him. But a local may think, “‘Uptown’? Never heard of it. We call this area ‘Trackville.’” Two names. One place. Which one is right? With the algorithm update, both are right.
The algorithm will provide local results for areas that are slightly outside of a searched-for local neighborhood.
Let’s say you’re in a city neighborhood looking for a coffee shop. You happen to be right near the “border” of another local neighborhood as defined by the maps.
You don’t care what the map’s neighborhood demarcation says; you just want a latte. So, even if you Google “Soho coffee,” Google might provide a search result that is a short distance away in Little Italy, outside of the official realm of Soho. Depending on where you are in Soho, Little Italy might actually be closer anyway.
This is the kind of non-specific specificity and location-based intuitiveness that Pigeon seems to be focused on. Shotland put it like this: “Google, in its infinite algorithmic wisdom, sees a small search area, like a neighborhood, and wants to give the searcher results from outside the hood in order to give a more robust set.”
The algorithm will give greater weight to local businesses that have neighborhood-focused keywords and citations.
Due to the hyperlocal tightening of the algorithm, I think that local businesses will begin to rank better if they have optimized their social profiles, citations, and Google+ profiles in such a way to reflect their specific location in a neighborhood of a city or region, not just the name of that city or region. Hyperlocal search is more important now.
Some businesses saw their rankings increase after Pigeon rolled out. Some, by contrast, watched their listing disappear. Here are some of the comments from Search Engine Land readers:
You win some; you lose some. Algorithm changes do that.
Based on the flood of comments in Search Engine Land, and some spadework by Darren Shaw, several business types seem to have experienced a hit from Pigeon. Here are the niches/themes that Shaw determined as losing local rank:
One of the most notable results from Pigeon was the decline in local packs. Darren Shaw of Whitespark reported a 23% drop. Mozcast registered a 60% drop! Mike Blumenthal brought some helpful perspective to this jaw-dropping number, but also noticed the reduction in 7 packs.
Early after the algo change, complaints surfaced of spammy results taking top positions in the search results. Even Expedia’s headquarters turned into a hotel after Pigeon was released.
Obviously, the algorithm update was not designed to target spam; thus, it’s possible that some spam creeped in. Barry Schwartz, who maintained a prolific output of commentary on Pigeon, wrote, “Like with any new algorithm launch, there are always bugs, unexpected outcomes and less relevant results.”
And, like with any new algorithm update, it is continually refined post launch. Since Pigeon was initially rolled out, many of the reports of spam results have faded (and the Expedia issue has been corrected as well). The algorithm seems to have stabilized by featuring of high-quality results only.