In this episode of The Visibility Brief, Yext SVP of Marketing Rebecca Colwell is joined by Anthony Rinaldi, Senior Director of Insights & Analytics at Yext, to talk about a growing challenge every marketing team is facing: the old playbook for measuring success is starting to break.
Impressions are dropping. Rankings seem less reliable. Traffic doesn't tell the full story. And yet… business results haven't necessarily tanked. So what gives?
Rebecca and Anthony dig into what's really happening behind the scenes — how discovery is shifting into AI conversations, why a single click today often represents the work of dozens of searches, and what metrics marketers should actually be paying attention to now.
The episode breaks down:
Why impressions and traffic are no longer a reliable signal
How "rank" fails to capture the complexity of AI-driven results
What new models of brand visibility can reveal — and why they matter
How predictive ranking models change measurement and help define performance
Why benchmarking must be local, competitive, and contextual
How to turn visibility insights into action
If you're a marketing leader struggling to explain declining top-of-funnel numbers, this episode will give you the clarity (and language) to explain what's really going on — and how to measure visibility in a world where AI shapes the answer.
Episode Links
Transcript
Rebecca Colwell (00:03) Anthony, it is so great to have you on the visibility for you. Thank you for coming.
Anthony (00:07) Yeah, thanks for having me. I'm excited to be here.
Rebecca Colwell (00:10) Excellent. So before we jump into our episode for today, I would love for you to explain to everyone what you do at Yaxst as our Senior Director of Insights and Analytics. Sounds super cool.
Anthony (00:24) Sure. I've been at Yext for going on seven years now. And the team that I lead here is called Insights and Analytics. ⁓ Historically, what that's meant was half the folks in my team are really good at insights. Half the folks in my team are really good at analytics. So the analytics folks, very technical. They're getting into the data. They're living and breathing in data. The insights folks are much better at, they're technical. They're data literate. But they're taking the insights, they're taking the data and turning it into something that matters. So what that means from like a week to week, month to month basis is that we often live with clients and with product.
So we are good storytellers. So we'll take data and go out to clients and try to find out what's resonating and what they care about and what we should be looking at and what kind of research we should be doing. And we'll take those findings and if it's good, if it's useful, if it's resonating. We'll take that back to product and tell them this is the direction that people care. So instead of the super technical people saying, you know, this number is good or this number is bad, or this number is up or this number is down. It's my team that's looking a little more holistically and trying to add business value and context.
Rebecca Colwell (01:35) Excellent. So seven years. You've been out a little bit recently though for paternity leave, is that right?
Anthony (01:42) The end, just got back right before the holidays, which I guess means I got back right after the holidays.
Rebecca Colwell (01:47) Well, congratulations. ⁓ I'm curious what that experience was like for you because the world is moving so, so quickly. So, ⁓ you know, how was that experience?
Anthony (02:01) It was ⁓ fulfilling. ⁓ was ⁓ purposeful, which is like a really overarching way of putting it. I feel like I'm more important. I mean, I am the most important thing for now somebody. ⁓ It's been fun. It's been fun to unplug. I think more specifically, he was born in the middle of October. And I joked to my wife that this feels like it was the first autumn I've had since college. Because we get wrapped up in work and it's like, Hey, it looked really nice out today. I hope I get out before the sun goes down. ⁓ and because I wasn't working for that whole autumn, was, Hey, it's really nice out. Let's go on a three hour walk. Like, Hey, it's really, really nice out. And that's just not something I feel like we get on a year by year basis. You go to the office or you work from home and you see it's the leaves look nice. And hopefully we get a nice weekend, but if it rains, then I don't know if you get single digit hours of like autumn, which was fun. and I know, I know we're here to talk about AI and I think it's a fun conversation to be the data guy.
And a lot of people ask me, they're like, what are you tracking? How are you tracking it? ⁓ and a lot of my friends that, that know me really well know that I have. Spread seats and dashboards for my life. ⁓ I can tell you when the last time I went to the movies is I can tell you the last time I got a haircut. I could tell you the last time I had eight ounces or eight cups of water. I just have dashboards. And so people like.
Rebecca Colwell (03:33) I have so many follow-up questions. are you ⁓ taking these spreadsheets and dashboards and applying them for your son? Like, are you tracking all of his growth progress and those sorts of things?
Anthony (03:47) That's, think, all of my friends and coworkers first question. They're like, I've seen your movie dashboard. I've seen your personal dashboard. I've seen your habits dashboard. I know how many times you've been on a flight lately. And when it comes to my son, the answer is no. ⁓ philosophically, I think it relates to my opinion on AI in overarching, which is I ⁓ may be controversial, but like I feel strongly that AI has a place, of course. I wouldn't be here if it didn't think it did. But I want to keep it right now pretty far away from the soft things. I'm not on X having fun looking at the generated images, because I think, for example, ⁓ I saw somebody say that AI should be used to do the laundry so that we can make art. AI should not be used to make art so that we can do the laundry or replace laundry with coding. So I... ⁓
Rebecca Colwell (04:43) Yes.
Anthony (04:45) Take that approach with my son now. And I'm like, you know what? There's probably a way to automate some of this. There's probably way to track some of this. There's probably a way to use AI or to use data to know when his next nap should be, or to know when his next feeding is different because that's keeps him alive, but to know when his next whatever should be. And it just doesn't seem like the right application. just doesn't, it feels like it's, it's the art section where you're, you know, trying to automate the fun part, the figuring it out part.
Rebecca Colwell (05:14) Yes.
Anthony (05:15) ⁓ and I, I want to stray from there again, I'll use AI to do the things to make my life easier, but I don't want to use AI to do the things that makes my life more fun. So it's, it's, it's interesting. ⁓ because yeah, occasionally I jump in and say, my God, this is so powerful. It should do everything. And I, you know, would prefer to keep the human side of raising a child alive right now.
Rebecca Colwell (05:36) Yes. Well, it's interesting. Two things occurred to me. The first is that, first of all, AI guiding you on kids' naptimes or whatever. The kid's going to do what he wants to do. So good luck with that. ⁓ This applies to parenting books as well as any parent will tell you. ⁓ But, you know, it's interesting when you think about AI automating the things that we don't want to do so that we have time to do the things that we love. I read this really fascinating study about machines automating housework. So, you know, we used to have to do our old laundry with washboard tugs and by hand and dishes by hand and all sorts of things like that building a fire to cook dinner. And there was. Yeah. So there was a study about how much time people spent doing housework in 1900 before all of these modern innovations came along and how much time they spend doing housework today. Do you have any idea what the difference is?
Anthony (06:18) I'll take your word. ⁓ I imagine it was the majority of the day.
Rebecca Colwell (06:38) We spend more time cleaning our houses today because our standards went up. So we're like, ⁓ now that we can do laundry with the machine, we're going to do it every week. Now that we can do this, are. So it's a really interesting, like, I'm curious with AI as we automate more things with it, are we actually going to get time back to do the things we love, or are we just going to raise our standards or start to think, you know, there's just, there's just going to be more and
Anthony (06:42) Bye. Bye. It's, it's, it's a totally different conversation and I'll stop it before it gets that far, but it reminds me of ⁓ my life right now, day to day. ⁓ I have become addicted to cloud code. ⁓ which has been, has been a revelation for me because I've always been like, Hey, this AI coding is interesting, but it's still telling me what to do. It's not doing it. Now that it can my time, quote unquote coding, and I use the scare quotes because I'm not doing it. has gone from zero to a lot. So I've been using Cloud Code to automate some of my fantasy leaks that can't be done on bigger sites, fantasy movie leak that I'm in, for example.
⁓ So again, the amount of time that I spent coding in these leagues historically was zero. And now I can't stop doing this on a nightly basis because I'm like, hey, this should be in the application and this should be in the app. So it's similar to that housework thing where you're like, it must have made the fantasy movie leak coding faster. And I'm like, There didn't used to be any. And now on nightly basis, I'm spending, I don't know, however long I have before my son needs his first overnight feeding from when he went to bed. But it's interesting, you're right. On paper, it should have shrunk it. In reality, I just have the next idea and the next idea and the next idea and end up living in that just like your laundry needs to be washed more now.
Rebecca Colwell (08:06) Right, right. Yes. Right, right. just, the technology unlocks our creativity for all of the different ways that we can use it. And that's a blessing and a curse, I think. So that's been great. ⁓ So you've been back at work now for a couple of weeks, and I'm curious how you felt getting back into it. Were you like really staying on top of what was happening in the world, or did you genuinely get a chance to check out and be present with your family?
Anthony (08:53) Yeah, I checked out. don't know if my bosses are listening. Checked out entirely. Checked out entirely. So like, yeah, I've got questions. I've got plenty of questions. I know how it felt on the other side. Once in a while, I had an inkling of a work question, but stopped myself every time from asking anybody that. But I'll give you an example. Over the holidays.
Rebecca Colwell (09:06) Okay.
Anthony (09:22) I went to ⁓ a birthday party slash Christmas party for a friend's child. ⁓ I remember going to that exact same party two years ago and showing people GPT to whatever GPT four. And at the time there wasn't the chat interface. You had to use the dev console. And, and I said to my friends, I'm trying to explain what an LLM is. We've known that at Yext for a little while. We've been using it for.
Rebecca Colwell (09:43) Hmm.
Anthony (09:50) ⁓ for YEC search and answers historically. ⁓ so I knew what an LLM was in, terms of it's going to predict the next word and it's going to write and it's going to seem like it's writing from its own brain. ⁓ and I'm explaining that to my friends at this Christmas party slash birthday party two years ago. And we're sitting down and we say, write this like a haiku, write this like, like a poem or write this like an angry tweet. and they got it, but it like, We're two years removed from that. And that same person I was explaining it to said, I've been using AI to help me write this book that I'm getting published on Amazon, self publishing, whatever, whatever. So I can tell you from my personal life and my friend groups, how much this has evolved.
⁓ I have no idea how much has this evolved professionally. Like I, the last thing I'll give you the floor in second. The last thing I think of is like the movie, big short. There's this time right before the housing crisis, right before the bubble burst, where I think there's a quote somewhere in the movie where they say, when you start getting a stock quote from your hairdresser or whatever it is in the movie, something's happening. something's that we're at above, not saying it's bubble, but we're at a, we're at an inflection point. And when I start hearing from the guy who didn't know what an LLM was two years ago at this birthday party, that he's using chat, whatever to build whatever at home.
Rebecca Colwell (10:59) Yes.
Anthony (11:15) So this is the hairdresser giving me a stock quote. So I can tell you it's making a difference in my world, but you tell me like, does this matter? Like in the marketing world?
Rebecca Colwell (11:18) Yes, yes. Yes, absolutely. I mean, on a personal level, I had a similar experience, you know, ⁓ showing chat GPT to my folks, and it felt like a magic trick for them, right? Like, Whoa, it's writing, it's doing all of these things.
⁓ Now they figured out how to unlock a couple of little use cases that they really like. I think about it in terms of the impact on marketers. And I think a year ago, marketers were still a little skeptical about how much of our time and energy to invest in AI search, right? We were like, yeah, lots of people are using it. It's interesting. They're still kind of dipping their toe in the water, but we're going to stay focused and obsessed about Google and our proven playbooks. There was a tipping point at some point this year, thinking more in the fall, where everyone really set up, took it very seriously. I think it was tied to the sharp decline in web traffic. When they actually started to see the numbers play out, And they were like, whoa, AI overviews are stealing the click. People are starting searches in LLMs. I really need to think about this and figure it out. That's, think, when it became really real for marketers.
Anthony (12:36) Yeah, from the insight side, like those are conversations that I have to have with businesses all the time. I used to get X number of impressions. I now get Y number of impressions. used to, my traffic looked like this last year.
My traffic looks like this this year. And in a pre-COVID world, it was fun to have these conversations because every year everybody was getting more, more web traffic. Not the internet was young, but like more web traffic, more reservations, more restaurants, more whatever. especially direction clicks on Google because people were going places. And then those conversations began to be less and less fun, right? COVID happened and where fewer people are clicking at directions across the board. And that's not a problem with your business. That's just the world's changing. And it feels similar now where people are looking at these traditional metrics like impressions or clicks. And you might be getting fewer in the places that you historically have cared more about. And it's nice that that conversation is getting up to the actual marketers because we need to make sure we're looking at the right things. I can tell you boots on the ground, things are changing, but if your number one KPI as a marketer is X and that's no longer hittable, then we have to start looking elsewhere.
⁓ The adoption conversation about these models, you mentioned having these conversations in traditional or not so traditional places. It's interesting because, you have two trains of thought. have the person maybe like you that's, that's, comfortable going AI AI AI AI AI AI. And then get back to Google. Maybe not comfortable like making a conversion in AI yet, but you've had the entire conversation in Google. Sorry, in AI. I gave a talk in Paris at a breakfast briefing last spring where we were looking at like, you know, A search journey a year ago. Question one, romantic restaurants in Montmartre. Question two, best burger in Montmartre. Question three, is this restaurant open? Question four, make, make reservation. And what used to be five searches in Google and then one click in Google is now, you know, romantic and best and, and open and burger. Those are all AI things.
Rebecca Colwell (14:37) Thank
Anthony (15:01) And the last one is make a reservation. Let me go back to Google. our clients were seeing what used to be five impressions in one click is now one impression in one click. Good news, think historically the marketer should have cared more about the click. Bad news is I think a lot of marketers are still judged on how many times somebody actually saw your piece of content.
Rebecca Colwell (15:11) Exactly. There's a big mindset shift that needs to happen. And I'm curious because, know, as the person who leads our analytics team, data, you know, you've lived the data every day. I think marketers have been maybe a little overly reliant on the metrics. I sometimes think that the numbers allow you to kind of step back and avoid thinking at a deeper level about what might be going on. ⁓ And that crutch is being taken away, right? If Google was growing year over year over year, Maybe despite the decisions we were making, we get a pat on the back for doing a great job, but were we really? And now the reverse is happening where we might be doing an amazing job, but the numbers aren't showing that because the environment's changing. I'm curious how you're having conversations with people about how to, how to like detach personally from the emotional response they're probably having to the fact that their numbers look terrible.
Anthony (16:15) good news, bad news. It's, it's, it's about good. It's about looking at the right metrics. and an impression was always fluffy, right? These different traditional search engines, right? To be honest, one month, they could just change the way they count impressions. Right. You were judged on getting a million impressions a year. And all of a sudden they say half those impressions were duplicates because, know, somebody moved the map.
Anthony (16:45) And when they move the map, the page refreshes and when the page refreshes, you got another impression. like historically the way we counted impressions were like a nice pat on the back for marketers, but even a marketer's boss would say the goal is to get more revenue. Right? So how do we tie? How do we tie this, this thing that we're doing as marketers into the thing that matters and the thing that matters is getting somebody in the door or getting somebody to buy something or getting a lead or getting a whatever. And that hasn't changed.
Anthony (17:14) Unfortunately, attribution is always tough. So for example, the really easy example I gave was impressions versus clicks. Impressions that were always fluffy. Clicks happened. Right? Like I don't, I don't care how many times you got seen. do care how many times somebody, I keep using traditional search, get direction, fill, call website, click. How often they did the thing. Right? Because doing the thing is the closest we get to spending money or becoming a lead or becoming a quote or becoming a whatever.
Rebecca Colwell (17:15) It is. Anthony (17:45) The is that that is still pretty traditional because as a marketer, what do you actually want? It's the thing. It's not did they make a phone call? It's did they make a reservation? It's not did they get directions? It's did they buy the new bag in the store? So the...
Rebecca Colwell (18:04) We lose that predictive ability though, right? When we can't see all of the things that lead up to it. We've got these frameworks and these models that say this number of impressions historically drives this number of clicks, which will then result in this number of reservations. And I think that's the thing that's making some people just feel so uncomfortable about this change.
Anthony (18:23) Yeah, it's, ⁓ it's very, I think uncomfortable is a good word, because there's a lot, it's lost between the thing and the money, right? The phone call and the money, the impression and the phone call, the impression. And that's an attribution question. And I'm sure as a marketer, everybody's job is 90 % to do that. How do, how do you make sure that you are conveying that the world isn't falling or that the sky isn't falling? It's you look at two things.
Rebecca Colwell (18:31) Yes.
Anthony (18:51) Our impressions are down 80 % year over year. Is our business, right? And if the business is actually holding steady or going up, then the answer is the impressions weren't the thing you should have been tracking all along. It's not easy for folks in the marketing world to wrap their head around because, you know, they've historically pegged their entire career maybe to that one big number, but you can make people feel a little better by saying something's clearly changing because this isn't This number isn't what it used to be, but that doesn't, we're still getting the same number of sales or more. So what are the other channels? And, you know, as soon as these, these AI models and these large language models start giving us better data, maybe we could tell you that, you know, there's this new channel worth this, this amount. And, ⁓ and here's what it's driving, but until that happens, I think everybody has to be comfortable being uncomfortable by saying, ⁓ just because our impressions are down, doesn't mean this isn't working.
Rebecca Colwell (19:50) Exactly, yeah. Get comfortable being uncomfortable. Anthony, I know you've been working on a project around a different way to measure visibility and impact. Can you walk us through, first of all, why this is such an important initiative? And then let's kind of dive into how you've been thinking about it.
Anthony (20:14) Sure. historically it's been very easy for us to tell our clients how often you do or don't show up. We've, we just talked about this for 15 minutes. You did get this. You now get this. ⁓ when things go down, they want to know two more things. They want to know how often could they show up. Right. And they want to know how well do they show up. Right. So how often could they show up is difficult, right? The example I use all the time is telehealth during COVID. You know, being the number one telehealth company in the world was very important. Right now being the number one telehealth in the company in the world is much less important. So if you're doing everything right in telehealth, you're still going to get fewer impressions, clicks, conversions, whatever. So knowing how often you could show up is an important thing to tell you. Are my numbers good or bad? But what we really want to look at now is not how often could you show up, but when somebody's looking, are you there? Are you there? Right? So not, they searching more or less for this thing, but when they do, how well do you show up? So what we get for that is rank, right? Historically, we could tell you that when somebody searches for burgers near me, you are number one or you are number two, or you are number three, or you are number four. And the development of scout at Yext has really helped us, ⁓ paint a picture of that, right? Because now our data, especially my data, when I'm looking at this for clients, is not limited to the X clients.
The data I get to look at now is we ran scans, we ran searches, right? We're essentially teleporting to these different places around the world and running a search for burgers. And we're writing down everything that comes back. So when, you know, we might not work with any of the people that come back in a certain search, but we still get that data. So I can tell you that in this postal code, when I searched for burgers, here's the top 40 that came back. I can also tell you everything that they're doing. Right? So the lists and the things. So for example, I could tell you the first person that showed up had a hundred photos and the second person that showed up had 50 photos. I can show you that the first person that showed up at the thousand review is the second person that showed up had 2000 reviews. And that's interesting. So let me pull this back to say what people want to know is how well do you show up when somebody searches for burgers? That sounds easy when you have rank.
Rebecca Colwell (22:28) Bye.
Anthony (22:39) Right. You, sir, you showed up first or you showed up second, or you showed up third. I have a problem with that because rank is really contextual. Right. Did you rank one because you were next to where we searched or did you rank one because all of the memory that our search engine might've had when running it or did you rank one? Whatever. Right. I think rank is like, it's a really good point in time. I don't know, flag, but it's not entirely descriptive.
Rebecca Colwell (23:15) And it sounds like what you're saying is marketers may perceive rank as a foolproof, like absolute number. And in fact, it's, it's not, there are a lot of different things that could influence user to user or day to day what that rank looks like. And so it be like an impression of that moment in time, but it's
Anthony (23:35) Yeah, don't get me wrong. I'm a rank rank is important. However, if, we're searching for urgent care near me and you are the best urgent care in the world, but you're two miles from where we searched, you're not going to show up first because Google is smart enough or these engines are smart enough to say, my God, they need an urgent care. Let's give them this one. They're close to it.
So I think rank is really, really a nice data point to have, but I don't think it should be the be all end all. So one of the things that we're working on at Yax is coming up with a more. ⁓ I don't want to see predictive, pro projected rank. Right. It's a little harder to wrap your head around, but instead of where did you rank, maybe where could you have, or should you have ranked based on all of those things that came back with you? ⁓ I'm to take this into a sports analogy and then in a minute, take it into a board game analogy. But this, the sports one, I'm a big baseball fan. So in baseball, we have somebody hits the ball.
Rebecca Colwell (24:25) Thanks for the preview.
Anthony (24:35) And it either became a successful hit or it did not become a successful hit. Right. So your batting average is high or is it low, but you can do everything right. You can hit the ball incredibly hard. You can hit it, you know, 110 miles an hour and you can hit it on a line drive. But if somebody's standing there, was, it's going to look like you might as well have struck out. Right. And in baseball, we think of that as batting average or expected batting average. Right. So hitting a ball really hard and hitting it really low has an expected batting average of something. It's almost always going to be a hit, but you can't control for the fielder was standing there or you can't control for the wind. Right. I could just tell you what should have happened. So I wanted to, I and the data science team and the research team wanted to think about this, not with what happened on this random day at this random location, where did you rank? But maybe where should you have ranked? So I said, sports analogy, here we go to a board game analogy. We're starting to use something called ELO score. And it doesn't stand for E-L-O, it does not stand for anything. It's the last name of the guy who invented it. ⁓ But essentially an ELO score is most often thought of with chess for a rating system. So I am a 1000 ELO or I'm a 1500 ELO or I'm a 500 ELO. And what chess and college football and all of these sports that need to rank
Rebecca Colwell (25:51) Mm-hmm.
Anthony (26:04) teams or players or whatever have come up with is a scoring system that isn't just a point in time score. It's over time. So you might start as the number one ranked player or the number one ranked football team or the number one ranked burger joint. Right. But you're only as good as your next match. Right. On the next one, did you lose to the person that used to be number two? Not that big of a deal. Did you lose to the person that used to be number 20? Something's up. Right. And as, at Yax, as we continue to scan these locations and these areas and these keywords and all of this stuff, we can tell you over time, which, ⁓ which businesses have continued to win, which continued, which ones have continued to lose. And we can start to think of these as not actual ranking, but ELO ranking. So for example, that burger joint in, in, I don't know, central Pennsylvania.
Rebecca Colwell (26:48) Mm-hmm.
Anthony (27:02) Instead of saying that it was the number five ranked burger joint on this particular day, I would tell you that it deserved to be number two because historically it has beaten the people ahead of it or it has lost those people below it. And it's giving credit for that. So not to get too, too technical as I did, but the idea of these projected scores or the predicted scores or these ELO scores and ELO ranks that we're going to start to surface ⁓ in this YACS research is not How did you rank on this particular search results page on this particular day with these particular conditions? But instead, how should you have based on everything that we know about you?
Rebecca Colwell (27:41) So as a marketer, I'm a very pragmatic person. So I'm always thinking, OK, what am I supposed to do with this? So what I'm hearing is that this type of report no longer ranks me in a vacuum, but also helps me understand how I am performing relative to the competition. And I think that's much more reflective of a real world environment, because every consumer is making decisions with you in context of the competitor. So I really love that. ⁓ The second thing I'm hearing, though, is This is telling me basically, am I doing all of the things I am supposed to be doing to rank? So even if I might on any given day be doing well, if my ELO score is a little lower, it's telling me I need to roll up my sleeves and get to work and address some of the key factors that might be contributing to a lower score. Am I catching that right?
Anthony (28:34) Exactly. The baseball analogy again is if you go 0 for 4 in a day, but you hit the ball hard every time, is that really worse than just hitting a dribbler that found a hole four times in a game? Eventually, it's going to catch up to you. So as a business, as a marketer, you might ranked one or two or three in this search in this random day. But if you don't have any photos and you don't have any reviews and you don't have a good rating and everybody else is catching up to you in these operational metrics,
Rebecca Colwell (28:48) Right.
Anthony (29:02) Congrats on your number two for today. But like, I wouldn't expect that to continue. So I would much rather talk about these, you know, predictive things.
Rebecca Colwell (29:12) Super interesting. think about it too. The analogy I use is me training to run a 10k. So I could do everything possible to be as fit as possible for that race, right? Eat right, ⁓ get all my practice runs in. But if I show up at that race and I am going against Olympic athletes, there is no way, even if I run my best, that I'm going to win that race. And so that might be the case for my ELO score too, right? I could be doing my best and still lose. So maybe that's where I focus some of my paid. ⁓ In other cases, maybe I slacked and I didn't train, but I'm going against, I don't know, some people who didn't train at all. Maybe I have a shot.
Anthony (29:58) Yeah, it's fun because in platform in Scout, we show Delta to benchmark, right? Or we show your numbers in the benchmarks. And I think it's really fun to scroll through as a business and find two different locations that have the same number of photos, right? So for example, you look at a location that has a thousand photos and another location that has a thousand photos. And for a restaurant in Iowa, A thousand photos is like twice as many photos as you need, but for a restaurant in New York city, it might not be enough. So just, just like the analogy of, of getting ready to run your race, you need to know what you're up against because I can't as, as the, almost the agency with some of these, these clients, I can't say your locations need to get to 500 reviews. I can't because some locations, 200, some locations, five reviews might be enough because. None of the restaurants or none of the retail stores or none of the providers near them have any reviews. And some 500 is not even going come close to the mark. So it's really fun in our platform because we contextualize and localize the benchmarks and your peers to say not how many photos should you have, but how many photos of the folks that are around you have. ⁓ it's interesting, especially when you look at something, to double down on this, but when you look at something like completion rate. Right?
So we have the ability in our platform to look at the core fields within a Google listing and say, do they have their name and their address and their phone number and their hours and however other many things. ⁓ And we can say that you should have a hundred percent completion rate. And it's really fun to see two different businesses that have a hundred percent completion rate. One says that's ⁓ net even with the benchmark because it's an area where everybody's putting in the work. And one says that they're like crushing the benchmark. And when you look at something like a percentage, you're like, can a hundred percent not be really, really good? That's because in certain industries or in certain locations or areas or neighborhoods, it's table stakes. ⁓ and it's, yeah, you've got to know who you're running against.
Rebecca Colwell (32:05) Absolutely. It's the nature of competition. It's the number one thing as marketers is to understand the competition and how you differentiate against them and how to stand out. Anthony, this has been a fascinating conversation. We are running short on time. I'm curious, just one final question. ⁓ As you have been looking through the data, was there anything that really surprised you?
Anthony (32:30) Uh, plenty. Is there anything that I want to say publicly that has surprised me? Um, it's a good question. Uh, I won't, I'm not going to give you a big ticket thing yet, but I would, I do want to say is some of the ways that we're looking at this, we're starting to look at this, um, slicing and dicing in ways that we never really have. So because we have brands within these results page, I've been rolled those up to a parent brand, right? How many Dunkin donuts is we're there, right? And because we have the entire, you know, nation of brands, I can tell you how many locations does Dunkin' Donuts have generally that have ever showed up.
So what's really interesting and something that we're starting to look into is both in this ELO score and in ⁓ actual traditional rankings. How hard is it for the big brands? How hard is it for the small brands? How hard is it for the midsize brands? I think we all have different, ⁓ I don't, maybe I don't know the exact answer to these questions yet. We're looking into it.
But like, I bet we all have hypotheses about the number one ranking spot and whether or not oftentimes that's an enterprise scaled company or whether or not that's a mom and pop bakery. Um, and now that we have a data set, that's, you know, hundreds of millions of companies large with scanning 2,500 postal codes with a hundred different business types. Uh, now that we have all this data, we can start to do that type of analysis. So don't, maybe I'll tease what we're working on. I don't want to give a big tech at number until. until we're ready, but it's really fascinating to say businesses that are really close to where we're researched and businesses that are really far from where we're researched. Businesses that have a lot of locations and businesses that have a single location. And just extrapolate from there. But yeah, we have some pretty exciting stuff happening.
Rebecca Colwell (34:16) spoken like a true data scientist. I love it. We would love to have you back ⁓ when the findings are in a place where we can share them more publicly. ⁓ And we'll keep an eye on the X research. know that that's the place where we generally get this out into the world. In the meantime, Anthony, enjoy the time with your son. It goes by really fast. ⁓ And thank you so much for joining us today and we'll see you again soon.
Anthony (34:41) Cool. Thanks Rebecca.
Rebecca Colwell (34:44) And thank you all for tuning in today for the Yaks Visibility Brief, and we'll catch you again next time.
