Oops! It appears my commitment to the last Thursday of each month (aka BluMihmShaw Day) escaped me. While I had all the data long ready to go, I spent my week focusing on collecting and correcting the data from an earlier post. Rats!
The data I have for you today is very simple. It’s all about these:
You will remember, the above are properties that Google has scraped, and places on the Google+ Local page -near the bottom. Not long ago I did a bit of research, though I am afraid it was a bit backwards.
You see, these properties that Google is scraping are important. I knew that before I did the research. Google, above all other properties, chose to scrape these websites. While I can only make assumptions for the “why”, I knew, based on working in the space long enough, that there was connection between these, and rank. The more the better I was sure. And so, without digging around to see if there was any connection between how many you had and rank, I scraped all the damn properties Google had, and told you which ones, in which markets, and in which cities stuck out most. Because I think they are darn important. Here is the data in case you missed it.
Now, I only realized that this was backwards once Darren Shaw brought it to my attention. Quick note: you will hear me reference Darren a lot because more often than not, before and after publishing/researching any data, I consult him. Between me and you, he’s a local data whisperer. When I brought this previous data to him he said something like “cool, but so what?” I nearly died. I don’t recall how many hours I spent pulling that data, but my heart was broken. He was right though. How does he know these properties mean anything? What proof is there that Google scraping them is a positive thing? None. Eff!
And so, I said to Darren, “I’ll Be Back!! I’m Not Done With You!”
Here’s the proof. Sort of*.
The only way that I could think of that might tell me if Google scraping these things influenced rank at all was: IF on page 1 for any given local term there were more scraped properties than page 2, and page 2 had more than page 3, etc. As it turned out, as I predicted, and anyone with half a brain could have predicted, my assumptions were correct. Here are the numbers:
Page 1: 13,975
Page 2: 12,361
Page 3: 11,703
We scraped Google+ Local listings in 55 cities and 71 industries. If you assume there was 10 listings on each page (we conducted our searches in maps.google.com), and we went back 3 pages then we scraped 117,150 listings. One point was assigned to every property found scraped, for example, in the above picture, that would count at 3 points, which was all be pooled into three totals: page 1, 2 and 3. The totals are as follows:
Now, I said “sort of” above because while this tells us that that listing with more scraped properties tend to have more choice rankings, it does not prove anything. I cannot tell you that if you submit to the properties most commonly found scraped in my previous research, and they get scraped you will rank better. While there is probably some truth, this data does not prove that.
So, I will let you interpret it in whatever way you wish, and do with it what you will.
I however will be making sure that I my clients, at the very least, are all submitted to these properties ;) But that’s just me.
Am I crazy? Let me know below.
Where do we go from here? Well, my next step is collect some of these properties for our UK and Canadian friends, as I only focused on the US last time. Once those are published, I may do a couple popular niches. Fortunately for me, I have built a tool that collects this data for me, and so gone are the days where we do this manually. With a bit more work, I just may open this to you guys. If you wish you have first dibs, please register to our newsletter, so I can keep ya in the know – I will need a few beta testers!!