 OK, so we're going to dive right into it. Back in August 2017, Think With Google released this article where they said that they'd seen a 150% increase in the number of local queries that people were doing without the use of the near me modifier. And that across the entire SERPs or across all their searches, they were seeing a decrease in people doing local queries with geomodifiers. So people were dropping geomodifiers from their queries. And the reason for this, Google said, is that people tend to think that they're able to find what they're looking for by just looking for a simple phrase or a simple word. So for us as SEOs, the lesson is super obvious. It means that we can't ignore the fact that our searchers may be searching for things that have implicit intent rather than explicit intent. And we need to figure out exactly what queries people are using that have implicit intent, dig into that intent, and then find ways to optimize ourselves accordingly. That's it. Problem solved. Everything's figured out. I hope you have a great MozCon. Thank you very much. Except, of course, we forgot about one really important thing. And that's the middleman. It's the middleman whose job it is to understand that intent and to match that intent with the right kind of content to service to the searcher. That middleman is Google. Google's the middleman. So in other words, we can spend all the time we want trying to figure out exactly what our searchers mean when they search for something. Except, well, if we don't understand what Google is doing and what Google understands, then we're missing a really key part of the puzzle. To explain this, I'm going to show you some queries. Sushi near me. It seems to indicate that close proximity is the most important thing. Sushi in Seattle, it seems to indicate that anywhere is good, anywhere in the city, cast a city-wide net. Sushi, it's ambiguous. Are you hungry, or are you doing research for a school project? Best sushi. It seems to indicate that quality is more important than proximity. Understanding intent is hard. And Google doesn't always get it right, but they're making some kind of executive decision. So we're here to ask, what is Google deciding to do? Before we dig into that rabbit hole and fall down it, I think it's important to take a step back and talk a little bit about why this is so crucial. You see, back in the day, we used to be able to rely on the idea that, as SEOs, we could track a national SERP in the United States and trust that that was accurate for what a searcher was seeing, whether they're on the east coast or the west coast. National SERPs got the job done, but nowadays we're living in a new world and the realities have changed. Nowadays, every single SERP is localized to at least some degree, right? So it's no longer going to cut it for us to track these national SERPs because Google is blending in all sorts of local results based on the telemetry that you get from your searching device. We're advising our clients to move away from these strategies of national versus local keyword tracking and to move towards local versus hyper-local because that's just the reality we're in now. Every SERP is local. We just can't keep denying the fact that localization is an important, if not the most important factor that Google filters its search results through as it builds a SERP. We have Danny Sullivan on the Twitter record saying just as much. And in the past few months, we've had several updates from Google that reinforce the importance of local to them. So here's one near the holidays where Google actually baked localization into the core of the Google search experience. So now when you search, all of your location stuff or your localization is done automatically for you. And then we have another update that came a little bit later where Google was tinkering with local SERP some more. So what we know is that local search is important to Google. And we know that they're making changes. And we know that with change comes opportunity. So we're here to ask a couple of questions. First, does Google actually know what people are after when they search for something? Is Google able to define or divine their local intent? Second, does Google have a nuanced understanding of local intent? Does it understand the different types of local intent? Does it respond differently? Or does it have a one-size-fits-all approach for any kind of local intent on display? To answer this, we did a research study, which is what we love to do at STAT, because we sit on a ton of data. We took geomodified and non-geomodified queries. We analyzed the SERPs, and we looked for patterns. And we hoped that those patterns are going to shine some light on the questions we're asking. And if you're super confused, don't worry. I've got definitions, and I've got explanations, and we'll get through this together. So here we go. First up, a definition. Geomodification. Geomodification is when a searcher includes a geographical term in their search query. The definition I have here or the example I have here is best shoes in NSW Australia. I think NSW stands for New South Wales. Are there any Australians in the room that can confirm that for me? Maybe. All right. Geolocation is the telemetry and metadata that comes from your device that helps tech companies like Google know where you are. It's things like your IP address, your location services, even like your language settings. All these things are used to determine your location when you connect to their services. It's this capability of Googles that supposedly, according to Think With Google, is causing searchers to geomodify their queries less over time. Now, whenever we do a study, the very first thing we have to do is to build the keyword set. I'm going to take a little bit extra time here, and I'm going to explain this because, one, building a keyword set is important, but it's super daunting. And two, the way we segmented our keywords was essential to our findings, as segmentation tends to be. So first, we're going to create a bunch of words that have to do with businesses that require an in-person visit in order to be satisfactory. So we have things like restaurants and nail salons and mechanics. And then we're going to duplicate that keyword set, and we're going to add in relevant adjectives. So we have Chinese restaurant, acrylic nail salon, auto mechanics. These are what we call our base keywords, and they're going to help us measure how well Google really understands that implied local intent. These form the first two-thirds of our keyword set. Then, to make things both more difficult and more interesting at the same time, we're going to layer and qualify our words. These are words that may compete with geolocation or geomodification for the amount of local intent signal they send. Hold on to that. We'll come back to that later. But for now, understand that we layer it in three different kinds of qualifier words. Quality terms, affordability terms, and brand terms. So we end up with best Chinese restaurants, affordable acrylic nail salon, Porsche Auto Mechanic. At this point, it's time for us to layer in the geomodification. So here we have three geomodifiers that we appended to the end of our queries. So we have near me, in Portland, and in New York. So you end up with best Chinese restaurant near me, affordable acrylic nail salon in Portland, Porsche Mechanic in New York. At this point, we stuff it all into stat. And we track it in two different zip codes and two different cities. We tracked it in two different zip codes because we wanted to be able to measure the differences in the SERP when somebody is standing at two different locations in the same area or the same city. We track it in two different cities because we want to smooth over any word outliers in the data. This is about 1.2 million queries we track for this study. And in case you're curious, we tracked about 70% on mobile, 30% on desktop, the justification being that, well, Google's pushing their mobile first index really hard. And we know that when people are doing hyperlocal searches, they tend to use their mobile phones. Whoops, too fast. So with that in mind, we're not ready to actually dive into the findings, which starts with an analysis of everything we found in, around, and about the local pack. You know, the local pack. It has a map. You got some handy pins. You got three brick and mortar businesses. There's a name. There's ratings. There's reviews. There's descriptions. There's times you get the picture. It's a local pack. The thing I want to know at first is how many local packs am I dealing with? Across our 1.2 million keywords, we found that 73% of them return to local pack. So yeah, they're like a huge massive opportunity for exposure if you're a brick and mortar business. And they're a huge, syrup-hogging annoyance if you're not a brick and mortar business. I feel you. The next thing I want to know is, how does the appearance of those local packs change based on the geomodifier that we use? Here we see that when the word near me is used, 96% of the time, we see a local pack, which is about par for what we expect, because these queries are explicitly local in their intent. So Google's just delivering the goods as we expect them to, which is why we're a bit surprised by the in-city queries, because in my opinion, those are also explicitly local in their intent. Yet they don't perform quite as well as the near me's. And then we have our base queries, those ones with implied intent. 52%, the worst performers. So think with Google maybe telling us that those base queries have implicit intent, but Google is not servicing the local packs to service that supposed intent that they say they should. We think that Google does understand that at least some portion of people who search for a base query, like one of ours, has an intention of visiting a local business, but they're not willing to go all in with that assumption every single time. We know for sure that that local pack opportunity that we have varies by a geomodifier. And it's one of the first and earliest things we discovered in our study is that, when you use near me, you have more local pack opportunity than when you use in-city. So a very first early finding for us. And based on that, we started to realize that Google doesn't see these three different geomodifier and keywords quite the same. So the next thing we want to know is, how well does Google understand the meaning of our different geomodifiers? And where does Google place local intent, like near me intent, and far away intent, in-city intent, on the map? And how about that base implied intent? Where does that fall? So in order for us to figure this out, we had to do something pretty creative and a little bit weird. We took the maps from the Google local pack. We measured the distance between the center point in the map and the center point of the zip code. The justification being that the center point of the map is where Google tends to cluster its results on the map. The center point of the zip code just happens to be where the search is coming from. So the shorter the distance between these two points, the more hyper-local the results are. You got me? We good? Everyone follow me? All right, so we're gonna carry on. So we took that analysis method and we applied it to our keyword set. And what we found was about what we expected and that's a good thing. We found that for near me queries, the distance between the searcher, like where the search is being conducted and the results being returned was the shortest. So these are hyper-local requests and Google is delivering hyper-local results. For in-city, the distance was the longest, which is Google agreeing with our assumption that when somebody searches for sushi in Seattle, they're willing to go further afield and willing to accept results that are not immediately close to them. The base queries fall somewhere in the middle. And Google basically, when it does deign to show a local pack for a base query, it's willing to assume that your needs are not immediate and you don't need something right next door to you. You're willing to travel a bit further away for it. Next, we wanna understand how geomodifiers influence the composition of a local pack. In order to do this, we have to do a ton of side-by-side comparison. And we have to control our variables because we only want to look at the influence of geomodification and nothing else. So here's the scenario. We have two searchers standing in the exact same spot in New York City, both looking for a cheap night out. One searches for a cheap night club near me, one searches for cheap night club in New York City. And what we can see is that two of their results are the same. This trend was born out across our entire keyword set. And we can tell you that geomodifiers will change a local pack by roughly only about 20%, which was quite surprising to me. We expected more volatility based on the different geomodifier used, but it turns out that 20% is about the same no matter what geomodifier you use. So if you're trying to think about how to be more budget conscious with your tracking strategy, you can just track one geomodifier. You don't have to track them all based on our results. You just have to accept the fact that there may be 20% variance if you're not tracking every different one. Now, if we're gonna look at the influence of geomodification, we also have to look at the influence of geolocation. Here we wanna know how is the local pack different when the searcher is standing in two different locations? So again, side-by-side comparison, controlling the variable. This time we controlled zip code. So the scenario is this. We have two searchers, both in New York, standing in two different locations, and they search for the exact same thing. And what we see is only one result overlaps. And again, this trend was born out across our entire keyword set. And so we can tell you that geolocation has a bigger influence on the local packs than geomodification does. Google cares more about the location of where the searcher is when it builds the local pack than it does about what they actually search for or how they constructed their search. So this is not the same for different geomodifiers, and that's really interesting. The example could be that me and a friend are both in Seattle, two different locations, and we search for a sushi near me. We're not going to see much overlap. But if we both search for a sushi in Seattle, we're going to see more overlap. The data tells us that Google uses the location of the searcher to determine where the center point on the map should be. It then uses the geomodifier to draw a big circle around that center point from which it determines where it can select eligible results. So your location determines the center of the map. Your geomodifier determines the area from which the businesses are selected. That's a lot about how Google handles things on the searcher's side, right? But how does it handle things on your side? Like we know on the searcher's side, it handles things through the way they query and the location of the searcher, but what are some of the things that you can do to influence your ability to rank high in a local pack? Sometimes when we look at the local pack, Google will give us the results distance away from the searcher, which is pretty cool, and we can measure that. And we measure that to try to determine if there was a relationship between the distance away from the searcher and how high a business ranks in the local pack. And across our data set, what we found on the whole was that the third result in the local pack was usually the farthest away. So you might look at that and think, great, that means that the way one ranks in the local pack is inherently tied to how far away a business is from the searcher, but you have to factor in density because that remained true in Portland, but it fell down in New York. In New York, we have the first and the third result that have the same distance away from the searcher. So we believe this is happening because Google has a greater density of eligible results to select from, and it satisfies its need for the number of results it has to return before it actually expands its net to look farther abroad. So we know that Google tends to measure the distance of those results, and then, well, so we know that we can measure the distance of our results and then find competitors within that radius. You may think, like, how is this useful to me? Like, there's no way that I'm gonna know where a searcher is standing when they do their search, and you're right, you don't know where the searcher's gonna be, but what you do know is where your bricks and mortar are, and you know what keywords you care about, and you can look at your terms and you can look at the local packs for them, and you can decide how far away those local packs are pulling, and you can use that data for your own website. So I'll explain. In our keyword set, we found that in New York, the average distance was 0.3 miles. In Portland, the average distance was 0.7 miles. So if we had a brick and mortar in any one of those markets, we could put our brick and mortar on the map, we can draw a circle around it using those values, we can determine where the competitors are, and we can decide how we need to compete with them. We know that distance isn't the only thing that affects one's rankings in the local pack, so we also wanted to look at Google ratings, and what I can tell you is that the first rating or the first ranked result in the local pack has the highest rating. The second ranked result in the local pack has the second highest rating, the third has the third highest rating, and so on. But those differences are actually quite small between first, second, and third, so at the best, I can tell you that you need to have a good rating in order to appear in the local pack, fine. Not willing to leave it there, we took it a step further and we started a segment out, based on our modifiers, and that's where we discovered something really interesting. It turns out that for near-me queries, the average Google rating is actually quite a bit lower than any other query. Google's seeking to satisfy the proximity of the request before it worries about the quality of the business it's returning. It's very different for in-city. For in-city, Google has a larger area to select from, and therefore in that case, it's looking to only surface the best businesses, so for in-city queries, the average rating was actually quite a bit higher. So we know that Google cares mostly about the distance of the request before it cares about the rating of the business. One of the things that you actually have a lot of control over that you can influence is your organic rank in the SERP, and so we wanted to know if your organic ranking influenced where you appeared in the local pack. A couple of caveats here, first of all. One is that not every single business that has a local listing has a website. Shocking, I know, but not every business needs a website, surprise, surprise. Second is, not every business that has a listing and has a website has that website listed on the listing on the SERP. Yeah, Google likes to keep you on their stuff. Sometimes I have to click like three or four times in order to find their website for a restaurant and it's really disappointing, but that's the world that Google's created for us. So what we can tell you definitively though, is that you don't need to be ranking on the first page or ranking it all in order to be eligible for a position in the local pack. In fact, only 8% of our local listings had website links and of those only 12% actually appeared in the organic results, but when Google does pull local listings from the organic results, it tends to prefer the first 10 spots on the SERP. We can also tell you that base and near me keywords pulled mostly from rank four, right below the local pack. And this is actually really, really interesting. Google believes that near me has immediate priority intent signal for location. People want to get to things immediately because they're on the go or they're about to go somewhere. And so Google actually waits localized businesses highly in the SERPs for those queries. So you end up with those three businesses in the local pack and then their corresponding organic results right below because Google will do anything to satisfy their intent as quickly as possible. If you don't get it in the local pack, you're gonna get it in the organics. Where, meanwhile, in city queries, they tend to pull more from like rank seven and that's because you have aggregator sites like Yelp and TripAdvisor that appear below the local pack because they're not eligible for the local pack. Final part in this section is we want to look and understand about those positions that you pay for and you don't earn the ads. So we can tell you that 16% of our local packs have an ad. It varies on the different geomodifier that you use. Near Me has the most ads. Base has the least ads and the advice is pretty obvious. You're gonna compete less if you go after those non-geomodified. Of course, those may not work for your particular strategy. They may not be targeted enough. So keep in mind that if you do decide to go after the Near Me's, you're gonna have the most competition. Although on the whole, there's not that much advertising happening in these local packs. Next, we want to understand how those different qualifier keywords, the quality, the affordability of the brand, will impact the local pack. So we want to understand, based on the types of word we use, how do the local packs change? First, we're gonna start with best and we're gonna understand how the local pack is different when I use queries that have words like best or good or recommended. When you think of the best anything in your town, you're probably thinking of like a small independent business, right? You're not thinking of a large multinational chain because they rarely have the best anything, best food, best reviews, nothing. And we believe that this reality actually explains the results we see here because we don't see a lot of similarity between two different locations when the word best is used. And this is actually really surprising for me. I come from Vancouver, it's not a giant town, it's not a small town either. But there are relatively few things that I would consider the best. Like there's 10 great restaurants and there's like maybe two great barber shops and a couple pubs that I think are really great. So I would expect best to have a lot of overlap no matter where I am in the city. Google doesn't agree with me. Google feels like there's a lot of different bests and it all depends on where you're standing. Now the differences were quite small so we wanted to understand that our quality words were indeed having an impact on the local pack and this chart proves that because this chart shows that when we use the word best or other quality words the average Google ratings are higher. Not only are they higher, but there's more ratings. So I can tell you that if you wanna be a best business you have to have two things. You have to have a certain threshold of results of ratings and you have to have good ratings. Those are the two things that are gonna make you eligible to be a best business. Next we wanna understand how things change when we're concerned with price where it's like cheap and expensive, budget. And here I can tell you that when we have serves that are concerned with pricing we see a lot more overlap in the queries and this is the exact opposite of the quality terms. So here we believe that Google is having a harder time finding results that it can actually qualify as being eligible for the term affordable. And when we measure the median distance away from the searcher we see that it's farther away which again serves to prove the finding that Google has a harder time finding businesses that it can qualify as affordable and therefore it's willing to search farther away. Unsurprisingly when we look at the Google ratings we see that on average those ratings are worse for affordable businesses than they are for best businesses. It seems like when people want to go review an affordable business they usually wanna give it a bad review. And this makes me think of Great Clips. And now Great Clips is probably the place to go if you want an inexpensive haircut. You sure, I mean like the GMB listings may have worse reviews than the location may be further away from you but it has what you want. It has cheap haircuts, at least cheaper than that super expensive barber down the street who has great reviews and is really close to you but also charges $50 for hair wax or Pima gel. I don't know what the kids are calling it nowadays. Anyway, moving on we have brand keywords. We have words like Ford, Mazda, iPhone, Pixel and so on. Admittedly we had fewer words here because we only had two verticals that were eligible. We had car mechanics and cell phone repair. So even though we had fewer terms we saw a very strong signal. We saw lots of overlap here. And when we looked at the distance away we saw that on average for brand searches that distance away from the searcher was greater than any other keyword. All of this makes sense when you consider like for example think of the Porsche mechanic in your town. How many Porsche mechanics are there versus how many like every car mechanics are there, right? These businesses are a lot more specific. Some of them even have the brand in their business name and Google knows that these needs that people are searching for when they use a brand word are quite specific and they're willing to travel farther to specific locations in order to satisfy them. Okay, so that's the local pack. I wanna quickly look at organic results because we know that Google handles organics differently than it handles local packs and other search features. So the first thing I wanna know is does Google interpret the meaning of our geomodifiers for organic results the same way that it does for local packs? In other words, if I search for near me are my organic results more localized? This type of analysis is a lot easier to do for local packs where we can rely on the fact that everything there is a local business and sometimes we have like distances to measure. It's a lot less clear when it comes to the organic results, what websites are what. But we need a way to separate out the online only stores or the non-e-commerce websites or the large multinationals from that local mom and pop sandwich shop down the street. And we know that that's really hard for us to do manually at scale because we can't go through and manually categorize every single thing. So we came up with a nifty workaround and that was to allow us to take a look at all the top 20 results in our SERP set, group them up by domain and then separate the top 1% of most frequently occurring domains from the bottom 99% of most frequently occurring domains. The logic here is that Wikipedia say will rank number one, two or three a lot so they'll appear in the top 1%. But your local mom and pop sandwich shop is gonna be chilling in the bottom 99% with everyone else. So therefore the more that the organic results pull from the bottom 99% of domains the more localized it is. And we know this works because in looking at the local pack we can see that it pulls heavily from the bottom 99%. Regardless of geomodifier 69% of local pack results come from that bottom 99% of domains. And we can tell you that it appears that Google interprets intent the same for organic results that it does for local packs. So when I do a near me search my organics are more localized than when I do an in city search. Next we wanna look at the influence of geomodification on those organic results. So earlier we looked at how geomodification influenced the local pack. Now we wanna do the same thing for the organics. And you're gonna see this familiar kind of screen. We did more side by side comparison. Only this time we're looking at the organics and not the locals but again we controlled the variable. So here we have two searchers standing in the same spot searching for two different things. One searches for near me, one searches for in city and we can see only one result overlapping. And again this held up at scale across our keyword set. And we can tell you that geomodifiers have a very significant influence on organic results. The exact inverse of the local pack. So we can say that geomodifiers don't impact the local pack very much but they do impact the organics a lot. And of course we have to do the same analysis for geolocation. So again side by side and the scenario is two searchers, two different zip codes searching for the exact same thing. And here we see a lot of overlap. And again this held up at scale across our keyword set. So here we can see that geolocation while it has a lot of influence on the local pack has very little influence on the organic results. If you're confused that's because this is the profession that you chose for yourself and you decided to be an SEO and you have to deal with this kind of shit. It's hard to keep track of. I'm gonna try to break it down. Geomodification affects the organics but not the local packs. Geolocation affects the local packs but not the organics. And it's all just a crazy witch's brew that you have to hold on to and keep in mind and good luck. This is the world that Google's created for you. Anyways, moving on, we wanted to look at the types of businesses that we see appearing in those organic results. And in order for us to do this, we took our top 1% and we grouped them into the two types of domains we see most often. Those are the aggregator sites and the local sites. And I can tell you that the brick and mortar businesses tend to perform the best on near-me serves. And this was a really cool finding. Because Google is so interested in trying to satisfy those near-me searches by hook or by crook, they really wait well towards localized businesses in the organics there. So if you're a localized business and you don't have the same oomph as like a large national website, try to go after the organics for the near-me's because you're gonna have some good results there. If you're like an aggregator-style site, you gotta go after the in-cities. That's where you're gonna win. You're not eligible for local packs but you will find yourself in the organics right below the local packs for queries like in-city. Okay, there's a ton of data, I know. That's just a crap ton of data. That's a lot to follow. We put it all together at getstat.com slash MozCon. You can go there, you can pull down the white paper, you can follow all the individual threads of discussions that we have, but for now I'm gonna quickly wrap up some of the key points for you. First of all, every single SERP is localized. Keep that in mind. I at STAT, my team at STAT or the Moz team, we can give you national SERPs. Like our business was originally built on providing people national SERPs, but they're kind of a weird artifact. Like national SERPs don't really exist in the wild, right? Like we manipulate Google to make it set to just give us a national SERP but it's not accurate to what your searchers are actually seeing, it's just cost-effective. So I want you to keep in mind that location matters but so do geomodifiers. Like Google can say all they want that people are searching less and less with words like near me but the fact is is that geomodificated searches are still on the rise. People still love to display their intent by using words like near me or in-city and Google still needs to figure out how to interpret all that. People love their near me searches and they're not going away. If you're not sampling at least some local SERPs in your strategy, I promise you you're not seeing exactly what your searchers are seeing. So I want to encourage you to try to drip some local SERPs into your strategy and do like what we're telling our clients. Move away from national versus local to local versus hyper local. You may look at my talk and go, man, Rob is not giving me like this one crystal clear takeaway for me to walk away with. And that's true and it's because everything that we presented here is based on segmentation and that's where you're gonna find all the best success, all the best sort of golden nuggets. Smart segmentation is key to your success. And I gotta tell you guys, I have the immense privilege of working with hundreds of the world's smartest SEOs and I have a front row seat to some of the like most sophisticated SERP tracking and keyword tracking strategies and on the whole, people don't segment enough. So if you take one thing away from my talk, it's think about how you can segment better because that's where you're gonna find the things that no one else sees. It's gonna be specific to your business. It's gonna be an insight you own because you dreamed of how to better slice your data and you're gonna be able to take that to the bank. So if you're a Moz customer or stat customer, the one thing I want you to get out of my talk, walk away and think about, are you segmenting your local keywords better enough or good enough? You can go to getstat.com slash MozCon. You can download the talk there. Thank you very much for your time. I hope you enjoy the rest of your day. Have a great MozCon. Thank you.