More threads by LocalListings303

Jun 10, 2022
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Hold on folks, this is going to be a doozie.

We work with several hundred retail storefronts in the same industry. A new client added us to their GBPs, and we’ve noticed a number of different trends that don’t make a lot of sense to us.

Within this industry, exposure, engagement and sales all tend to be higher on the weekends. And search exposure/engagement ebbs and flows based on myriad factors - weather, local events, holidays, etc. A typical GBP insights graph looks similar to the below graph.

However, we’re seeing something very different on the GBPs they’ve just added us to (we haven’t started working on them because we want to understand where these anomalies are coming from).

Their exposure appears to be almost standardized. Peaks and valleys in listing on maps and search that occur with startling regularity. The chart almost seems too symmetrical to match the typical fluctuations we normally see in this industry. Not only that, but the peaks are on weekdays (typically less busy) and the valleys are on weekends (typically more busy). See below. As well, these stores get a TON of direct search typically.


This pattern only seems to happen with large, multi-location brands in cities and markets where this particular industry is well established. This particular brand has excellent domain authority and gets a ton of organic traffic.

What’s also a little bit strange is that the fluctuations are specific to Maps. If you isolate search, the pattern disappears. See below for the same graph you see above with “listing on search” isolated.

With a chart that shows a pattern like the one you see above, we’ve noticed some sharp and rather precipitous falls in exposure like what you see below, although the actions seem to stay relatively consistent (actions over the same time period).

What’s even stranger is that, for a few of the locations they added us to, we’ve noticed a jump in exposure followed by a fall. The jumps display the symmetrical pattern, whereas after it falls, it looks a bit more normal. See below.

The brand previously did not have total control over all GBP pages at the corporate level - they were generally managed by the store level leadership - so we don’t really know what changes or updates might have been made to correspond with jumps or declines - but something is going on here, and without understanding what, we’re not sure how to fix it for them. We’re the first digital agency they’ve engaged with, so we don’t think that the falls are related to API integrations with tools like Yext, but we’re not entirely sure either.

The other thing that we’ve noticed is that, when a drop off happens, it usually corresponds to a sharp drop in direct search. We know that consumer behavior won’t change overnight, so we’re theorizing that a change impacted visibility on a critical directory (Apple maps or Yelp?)

The images below show the insights graph with the drop off, as well as the thirty days just prior to the drop, and the thirty days after. It’s a huge loss of direct search.


Before (above) vs. After (below)


Like I said, this has our entire team scratching our heads and the new client will be an excellent partner, especially if we can figure out what’s happening with their digital exposure and correct it. Any insights you can give would be greatly appreciated. Thanks in advance!
I’m interested to hear what others have to say, but as far as I know, the data in GMB insights is extremely unreliable and big swings like this are expected behavior that doesn’t represent what’s actually happening with your listings.
I’m interested to hear what others have to say, but as far as I know, the data in GMB insights is extremely unreliable and big swings like this are expected behavior that doesn’t represent what’s actually happening with your listings.

Given that is Maps views related, is it possible that the maps data is a result of them being at an address that is getting lots of driving directions for another business also at that address?
To clarify some of the comments, you should absolutely have a quick look at this article to understand exactly what the views metric represents in the first place.

I've seen peaks happen for all sorts of reason. Pins in highly looked area can have MASSIVE variations because as soon as they appear or disappear (occluded by a different pin for whatever reason), it generates a lot of views. On one memorable occasion, the client had a name similar to someone whose name had been in the news.
As for as the peaks and valleys in the where customers view your business profile I have one client with the same pattern. Deep valleys on Saturday and Sunday. They are closed on Sunday and only open till 1:00 PM on Saturday. Most of their business is contractor related which I think explains a good bit about my pattern. They do have consumer interest that will typically work with a contractor when they are close to pulling the trigger on a remodel or new construction.

In your case if you are UTM tagging the GBP buttons take a look at Search Console and see how the GBP pages are performing. Does the pattern persist there? Also, take a look at this data by Device and Country. I have seen GBP tagged pages showing unusual Device and Country data.

Sorry I don't have an answer but you are putting some solid thought into analyzing your data. Keep in mind what Darren said about the unreliability of the Insights data and I like where Mike was going with the Maps thoughts. In that vein I once worked with a Fast Food Restraunt that served Hamburgers and Ice Cream that kept showing up for pizza searches. Turns out there was an popular pizza chain next door.
We're utilizing UTMs but just launched the campaigns, so don't have a ton of data to work with yet. Could it be that the reason why maps views are so high and symmetrical is because, for some reason, the algorithm is choosing to pin them on maps on a specific schedule, and when something changes on the backend, it kicks them off that schedule? I know views don't mean all that much but if the pin is no longer showing up, it would drastically lower views, right?
Whilst I agree with most of the previous comments, I would not just discharge the data. If you look at the data for all 300 listings it will make more sense than looking at individual stores, which ca indeed be all over the shop. I am more than happy to download the whole dataset and run your through it and give you my POV. My advise would be to use the API and store the data in Google Cloud so you have content when you need to explain these spikes. We can help with added context from other multi location businesses if needed.

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