Last month we discussed Google’s move to integrate many of its local listing elements into “Google My Business” in my column: Google My Business – A Step Forward or Lipstick on a Pig? One month later, the single-location small business business platform seems to be working well. However, legacy accounts with multiple locations have not all been transitioned to the new platform. For the ones that have been transitioned, we are experiencing issues related to responding to reviews, as well as a few other anomalies. We look forward to Google completing the transition and will keep you updated on enhancements.
Now, I am note sure if the timing is related to the upgrades in “Google My Business;” but, Google also pushed a new algorithm change squarely aimed at improving local search results recently. Based on observations of local listings (yes, I used to read the phone book for fun) and specific listings have under management, it appears one of the major enhancements effects the relevancy of the listings for neighboring towns surrounding a particular location.
Forever, Google has been receiving criticism from marketers that their listing algorithm priority focused too much on a straight mileage proximity calculations. This adversely affects businesses located on the “outskirts” of towns or ones that service multiple towns from one location.
There are a few clues emerging in how Google may be achieving this. Clue number one, a greater tie of “communities served” information from the local listing information to the website of the listing owner.
In the following example, you will notice that the seven-pack contains a listings for plumber not physically located in Danbury (Renz Plumbing & Heating).
Upon review of Renz’s website, we observe a footer that contains communities served information that matches many of the new geographies that this listing is appearing in that are outside of the “home” town.
Interestingly, this listing is now appearing in a number of additional geographies and it is optimizing better than businesses that are physically located in many of the queried town names. For example, Danbury has more than 100 plumbers and yet now listings are optimizing for businesses located in surrounding areas/towns.
Clue number two: It would appear that including “communities served” information directly into the “introduction” section on the Google+ pages has begun to have a greater influence on listings appearing in geography-based keyword searches outside of the specific town designated for a listing. I conducted a designed experiment that included town names inserted in the website footer (tactic listed above) and inserting a slightly different list into the Google+ page. Towns listed only on the Google + also have relevance and these listings are now appearing for queries in neighboring towns.
Which works better? It is way too early to state definitively, as the listings seem to still be “Google Dancing” and changing position once or twice each 24-hour period. However, it looks like information contained on the website has been tied much more strongly as an optimization signal to the local listing.
So what do all of Google’s recent moves mean? I would say that Google is more actively providing attention to the local space. First by streamlining and simplifying the process for local businesses to “get on Google” and second by improving the user experience in the form of improved relevancy of listings for businesses that service but are not located within a given town.
For marketers, now is a good time to go in and review how your listings have been impacted by the algorithm change and begin optimizing based on the early signals of change, including the ones mentioned above. Happy optimizing.
Time is running out to feature your company in our inaugural Mobile Vendor Reader Survey.
Marketers create personas to better understand their target audience and what it looks like. If marketers can understand potential buyer behaviors, and where they spend their time online, then content can be targeted more effectively.
What’s behind a successful data-driven marketing strategy?
Audience targeting can be challenging in social media, especially when brands make quick assumptions about their target users. How can you avoid generalisation and what are the real benefits of it?