Hello, everyone. Thanks so much for having me here today. Excited to nerd out about Google Analytics with you. So I want to point out that I have slides available for download because there is so much to, like, in Google Analytics, there's so much we could cover. So I can only touch upon so much. But I included a lot of links to additional resources for each thing that we're going to touch upon. So I encourage you to download the slides from that link there, stealthsearch.com, slash slides, so that you can follow up on and read more on each thing that we touch upon today. I'll show that again at the end if anyone missed it now. So why focus on improving your Google Analytics skills? I think this quote sums it up very nicely. The goal is to turn data into information and information into insight. What does that mean? What's the difference between data and information and information and insight? Data, as you can see here, is essentially a bunch of letters and numbers, a bunch of characters, in not necessarily any organized, meaningful way. So data becomes information when it is organized in a structured, meaningful way, as you can see in the second block there. So what's the difference between information and insight? Information becomes insight when you can use it to create actionable takeaways and answers to questions. So for example, looking at this information, if the question was do organic search visitors engage with our site more than paid search visitors, once all that letters and numbers and gobbledygook is transformed into a structured, logical, meaningful table, then you can look at it and get insight and infer, yes, organic users do engage with this site better than paid search users. And even further insight, conclusion, or actionable takeaway from that would be we should invest more in SEO. So when data becomes information and then information can become insight, it creates actionable takeaways that are very meaningful for business and for helping you make money. So how do we do that effectively? How do we turn data into information and information into insight effectively? I think there's three things that are important in order to be able to do that. And the three things are that your data needs to be accurate, comprehensive, and segmented in a way that can answer the questions that you need answered. So that's what we're going to go over today. Here's a quick run through of the agenda. We're going to go over ways to make your data more accurate, more comprehensive, and how you can segment it to answer questions. So to make your data more accurate, we're going to go over checking your tracking code, implementation, removing self-referral issues, cross-domain tracking, removing internal traffic, and removing referral spam. To make your data more comprehensive, we're going to go over conversion tracking and event tracking. And to segment your data to answer questions, we're going to go over using secondary dimensions, custom segments, custom reports, and custom dashboards. So there's a little sneak preview of what we're going to dive into.  So in Google Analytics, you can have a single account that has multiple properties, which are typically used for, not always, but typically used for multiple websites. So one account can track, like one login, can track multiple websites, a.k.a. properties. And each website can have multiple views, where the data is filtered in a variety of ways. Or it's always a good practice to have one completely unfiltered view for each website. So one account can have multiple websites, a.k.a. properties, and each property can have multiple views. And so if you go into the admins section, you'll see those three columns, account, property, view. And it can get a little confusing, because you can see some of the same options under each one, like you can add users under each one. So it's helpful to understand what you're doing and where when it comes to account, property, and view. Does that make sense? OK, great. All right, so let's dive into how to make your data more accurate. That's obviously of utmost importance if you're going to learn anything from it. You don't want to be looking at incorrect information. So first and foremost, always, always, always check your tracking code implementation. One of the most common reasons for inaccurate data or problems with the data in Google Analytics is from incorrect tracking code implementation. It seems easy enough to just set up a Google Analytics account, take the tracking script, and slap it on the site. But if you do that in the wrong place, it can result in an accurate data. Or sometimes what happens with plugins is you may have had a web developer manually add the code in at some point in time and then someone else uses a plugin to add the code again, and you end up with two sets of tracking code. That can cause problems as well. So first of all, make sure you only have a single copy of the tracking code. You can right-click, view page source in your browser to see and search for your account number or search for the UA dash, even if you don't know the account number. Make sure you have a single copy must be placed before the closing head tag. Ideally right after the opening head tag, but it should be OK as long as it's within the head tag, so before the closing head tag. Definitely don't want it after the body tag. Quick or easier way of checking on this is tag assistant, which is a Chrome extension. Makes this super easy. You can install the Chrome extension and it will just give you a green smiley face if you're good to go and it's implemented correctly or a red sad face if it's not. So that's even quicker, easier way of making sure. And it actually checks that the data is able to be sent, not just that the code is in the right place, but so if you have a missing character in your code or some error in your code, this would tell you that as well. That's where the page. Well, it's for the page that you have loaded currently when you load the extension. But good point, it should be checked on every page, or at least every kind of page. So if you run the tag assistant, run it on your home page. But then also check your blog pages, check your inner pages. Make sure you've checked all the main sections of the site. All right, next up, as far as making sure your data is accurate, you want to look for and resolve self-referral traffic issues, which means that a self-referral traffic is when you have traffic listed as incoming new traffic under your referral sources, but it says it's coming from your website. So that's not correct, like you can't have external traffic. Like referral traffic is supposed to be reporting on external traffic. You can't have external traffic from your internal website, and if you see that, that can mean that your sessions are being double counted, and conversion data can get messed up from that, too. So you definitely want to double check this and make sure you don't have it. So go to acquisition, all traffic, referrals in the menu in Google Analytics, and look for, look through that, and make sure you don't see your own domain name in there. If you do, luckily it's an easy fix. You go into the admin section under tracking info, which is under the property column in the center, and go to the referral exclusion list and add your own domain name. You may even just want to do that proactively to make sure that you don't encounter these issues. And so what this is doing, though, it's not, I've had people ask me, well, I don't want to delete traffic from within my own site. The referral exclusion list is not going to delete the data, it's simply, you're telling Google to unify the session, so that if they see somehow, for some reason, there's incoming traffic from the same domain, they won't delete it per se, they'll just unify it with the previous clicks from that session. So it's just saying, you're telling them, don't double count it, just single count it. So don't worry about using this and having data deleted, it's just going to unify the double counting. Ideally, though, if you do see this, you should dig into why it's happening. Sometimes if a site switches from having the dub-dub-dub in it and then from not having dub-dub-dub, and then they add that, and there's some internal links that still have it the old way, things like that, like structural changes with URLs, and internal linking is one of the most common causes for that. So it's best to resolve the actual issue, but it's also totally fine to use the referral exclusion list to tell Google to unify those sessions. So another thing you want to look for and use the referral exclusion list for is external payment gateways.  Sometimes the visit coming back can be looked at as a new visit, referred by paypal.com, and that can mess up all sorts of things, because if you're trying to analyze the effectiveness of any advertising that you're doing or whatever, and you're seeing all these sales being attributed to PayPal, you're not going to know where they really came from in the first place. They may really have come from a Facebook ad you paid for or something, and you won't know that if your sales get attributed to paypal.com. So to avoid that with paypal.com and similar checkout gateways, Eventbrite or Shopify.com, this happens with sometimes if you're integrating with Shopify in any way. So just add those third-party payment gateways to the referral exclusion list, and it'll do the same thing. It won't delete the session, but it'll unify the session with that user's prior activity so you can see where that user actually came from. Another accuracy issue to watch out for is cross-domain issues. So if you have multiple websites, multiple domain names, that users tend to interact with and come back to and from. So for example, if you had rsite.com and rcareersite.com, and it was common for users to go from the main site to the career site and then come back to the main site, that same kind of thing can happen with sessions looking like brand new. But it's not as simple as using the referral exclusion list to unify them in this case where it's crossing back and forth a lot. So you want to set up something called cross-domain tracking, where you're actually going to use in order to do this. You're going to use the same property on multiple domain names. And you add a little bit of script. There's a link there for more. We don't have time to go into it in full today. But if you need to do this, check out that link. You add a little bit of script to your Google Analytics code and then you use the same property code on each of the different domains. And that really helps unify everything very nicely. So typically, like I said, properties are used for different websites. But in this case, you would use a single property for multiple websites if you have this particular situation. That link will tell you more about exactly how to set that up and how that works if you need to do that. All right, another inaccuracy issue is getting yourself and your teams traffic out of your Google Analytics, because that is not representative of your actual users, your target audience's behavior. So it can really skew metrics. Because the way you and your team use the site is not the same as what you want to be watching for in your target audience's behavior. So easy way to make sure your team is not counted in your own Google Analytics is the Google Analytics opt-out add-on to Chrome extension. So if you use Chrome regularly, like most people do, you can just put that on, enable it, and now you won't be tracked by Google Analytics when you're on your own site or any others. Technically, it blocks Google Analytics script completely. But it is the easiest way to make sure that your team's traffic is not being counted in your website's Google Analytics reports. There's another way to do it, which is IP filtering, which, A, you have to be careful any time you're doing filtering. You definitely always, always, always want to create an unfiltered view as a backup view. Because when you use filters in the admin section of Google Analytics, that data is filtered out forever. So if you accidentally make a mistake, just one character of a mistake when you're trying to filter data, that data is gone forever. So you always, always, always have an unfiltered backup. But when it comes to filtering out your office's IP address, this used to be a little easier prior to GDPR, which, as we know, complicates a lot of things. But one of the things that if you're tending to GDPR, you might be doing is IP address anonymization in Google Analytics. Long story short, Google Analytics doesn't ever show you IP addresses, but they do actually store them on the back end in your account. So you can't get to them, but they're there. So a GDPR best practice is now to anonymize that data, which makes filtering a little more tricky. Not impossible, but tricky. So to go back a step, the easy way, GA opt out extension. But if you want to look into how to continue to do this with IP filtering post GDPR world, you can read up on that. It's still kind of an option. All right. So that's getting yourself and your team out of there. Last, but definitely not least, inaccurate data that you want to get out of your Google Analytics account is referral spam. So anybody know what referral spam is? All right, so a few are aware of it. As spammers have figured out to do in pretty much every area of our lives, they've figured out how to spam you through your Google Analytics account. So now we not only have to deal with spam on email and text and phone and direct mail, now we are getting spam in our Google Analytics account. What they do is they ping your account with fake hits. So they don't actually visit your website. But they just randomly generate Google Analytics account numbers, just like people will randomly generate phone numbers for text spam or for phone spam. They just randomly generate Google Analytics account numbers and send pings to them. So they've never visited your website, but they pinged your account so that it looks like you have traffic. I'm pointing that way. You guys are looking at that screen. Like, you got traffic from that website, even though you didn't. And they do it just to get your attention. And it works because people go through the referral sources like, what is this website? I've never heard of that. And you check it out. That's they got your attention. So how to get this out of here? Oh, a couple of things to watch for, so to know if it's referral spam traffic or not. Typically, not always, but typically it will have an average of one page per session, zero seconds session duration, and 100% bounce rate. So it's obviously bot type fake traffic. Not always, though. Sometimes those numbers are they know how to manipulate them. But if you see that, that's a clear sign that it is referral spam. So how to get it out? It's, well, again, unfiltered backup view. If you're going to filter anything out, just have to reiterate that. Best practice, always, always, always have an unfiltered backup view. But once you have that in place, how do you filter it out? The answer is Carlos. I'll explain. Carlos is a guy who has made himself a business out of referral spam removal from Google Analytics. It's like literally you can pay him to do it. But he also has a blog that explains exactly in detail how he does it. And he keeps that updated. There's new spam sources that come out practically every week. So you absolutely don't have to pay him. You can just go to his blog and follow his instructions. But it is pretty techie and in-depth. It requires regex. It requires a lot of manual updating, practically weekly. But it's totally an option to do it. Take care of it yourself. Or he does have a very affordable service. Well, he'll just take care of that for you. And you don't have to think about it anymore. So check that out. But either way, Carlos is the answer because I swear no one keeps up to date with this stuff more than him. It's very helpful. Sorry? Go back? Oh, yes. Carlos Escalera. That's the man right there. I'm not affiliated with him. And anyway, I don't get paid to promote him. I just truly believe he's the best at this nitty little thing. So that's his website links right there. All right. So we talked about a variety of ways to make your data more accurate. Now we're going to get into how to make it more comprehensive. First and foremost, of course, you want to track all actual conversions. And I'm going to get in my soapbox a little bit here. This is a pet peeve of mine when I see things tracked as conversions that are not conversions, such as viewed contact page or stayed on contact page for five seconds. Did you actually convert them to a lead? Did they give you your contact information? Are you able to follow up with them and sell to them? No. Actual conversions are when someone has either purchased something and given you money or submitted their contact information in some way, shape, or form. It might just be a white paper download where you've got their email or something. But they've inserted themselves into your sales cycle. They are now a lead, a prospective customer. That's what conversion tracking should be reserved for. There's a whole variety of interesting things that can and should be tracked. But those should be tracked as event tracking if it's anything other than someone actually converting to a lead. So if you want to set that up, you would go to for form fills like contact forms or ebook downloads or whatever, the easiest way is to set up a goal using the destination goal method. So just make sure that your contact form redirects to a thank you confirmation type URL, a separate URL, then the form is on. And then set up a goal to fire any time that somebody hits that thank you page after submitting a form. That's the easiest way. There's other ways to set that up, too. But to touch upon the easy way for e-commerce tracking, so if you're tracking sales and you happen to be using Woo Commerce, you're in luck it's super easy to use the e-commerce tracking plugin for Woo Commerce to set up e-commerce tracking in Google Analytics, plus you. So that you have actual transaction data, revenue, order numbers, you get a lot of information in your e-commerce tracking section of Google Analytics if you use that plugin for Woo Commerce. If you're on another system or you just want to do it the hard way, there's two links there for setting up the form fills the more advanced way and setting up the e-commerce tracking. Both of those are with Google Tag Manager, so don't have time to go into that full right now, but those links will tell you exactly how to do that if you want to do it that way. There is a little bit of a benefit to the form fill tracking that way because you can set up submission validation so it can not just make sure. If you do the thank you page method, someone may have accidentally stumbled upon your thank you page somehow, especially if you forget to de-index it from Google. So it's not the most accurate way. The Google Tag Manager way is harder to set up, but it does offer the option of form submission validation so you could be sure that form actually went through for real. So if you want to track other things that are interesting to you but are not technically conversions, not people submitting themselves into your lead cycle, things like video views and how much someone interacted with a video, how long they watched it, whether they paused, played it, that's available as event tracking should be implemented as event tracking, not conversion tracking. But there's links there for YouTube and Vimeo setup, again, using Google Tag Manager. Also, you might want to track clicks on things like mail to email addresses, click to call, outbound links, social sharing buttons. Those are all interesting things to track to see how people are engaging with the site and are not tracked by default in Google Analytics. So there's a link there for instructions on how to set that up. Similarly, clicks on images or other elements. Just anything that's not tracked by default, you can pretty much set up to be tracked in Google Analytics. You can even do phone calls if you use a separate call tracking software and integrate that with Google Analytics. But all of those things, if it's not someone, a phone call might actually be a conversion. But all any other things like watching a video or something should be tracked as events, not conversions. So I'll get off my soapbox about that now. All right, last but not least, segmenting your data to answer questions. We'll talk about a couple of ways you can slice and dice information when you need to know something specific. So my favorite nifty little trick for this is called secondary dimensions, which is kind of a techie word, but just think of it as secondary columns. So it will add a secondary column to whatever report you're looking at to break down the information further. So for example, if I was looking at our information about our careers page or just looking at our top landing pages and thinking to myself, hm, our careers page is one of our top landing pages, I wonder where all that traffic is coming from. Why is our careers page getting so much traffic? So it would be nice, you can do this in a variety of ways, but it would be nice to just add a second column real quick and see a breakdown of where the traffic to that page came from. And so that's what secondary dimensions allow you to do. Again, think of it as just secondary columns. You find that dropdown, and you pick whatever it is that you want to break down the information by and add to that second column on the report. In this case, I wanted to know the source and medium of where the traffic was coming from. So I clicked into the careers page report and just did secondary dimensions, source medium. And now I can see exactly where all the traffic to that page is coming from. Quick and dirty, if I needed to look at that on a regular basis, we'll talk about other ways to do that. But if you just have a quick curiosity as you're poking around, secondary dimensions, a.k.a. secondary columns are awesome for that. Another awesome tool that I use all the time in Google Analytics is custom segments. That allows you to view all of the Google Analytics reports through a temporary filtered lens. So we talked about permanent account level filters before. Now we're just talking about temporarily, again, to use the careers section as an example. If I wanted to look at every single report in Google Analytics, but only for traffic that went to our careers section, I can set up a custom segment. So to do that, at the top of most of the reports, any given report that you're in, most of them you'll see at the top, an option to add segment. So you can click on add segment, name your segment, click on conditions, and just pick the conditions you want to see. In this case, I wanted to see pages that contained forward slash careers and slash in the URL. And so I set that up, and then I have a career segment that I can poke around any given report in Google Analytics and see only the data about the activity on the careers section of the site. You can even do this with two segments at a time. Actually, I think it's up to four at a time you can do. But this example shows two, where I did one for the careers section, and then I did one for the resource center section of our site. And I put them on both at the same time, and I can quickly compare the performance on any given report. As I click on Google Analytics, I'll get those two rows there, and I can quickly compare two different sections of the site and how they perform differently. So custom segments, very, very helpful. But if you find yourself using the same custom segment all the time, for example, we have been with this whole Google PageSpeed algorithm update coming up. We've been measuring United States desktop speed versus mobile desktop speed for all of our clients all the time. And so custom segments become a little bit too manual at that point if you need to check a lot of different reports with the same segment all the time. You may just want to set up a custom report. Just like the default reports in Google Analytics, you can just create your own with some mix of dimensions and metrics that don't exist by default. And I'll explain how to do that and what a dimension is and what a metric is. But that's kind of the use case for when you would create a custom report. So if you find yourself using a secondary dimension or a custom segment over and over and over again, and it's a little too manual, you just want to save it that way forever. So to create a custom report, you go into the customization section, and you'll first select your dimension. This is a common point of confusion, like dimensions, metrics, where, what is all this? Dimensions are basically the first column, whatever you want to see in the first column. The first link dimension, first column. Metrics are numbers. So whatever numbers, actual digits you want to see in the following columns, that's a metric. So just pick your dimension what you want to see in the first column. The metrics, the numbers that you want to see following that. And then if you want to filter it, like in the example I just gave, United States desktop traffic, you can set that up as a filter so that the custom report only shows those metrics for that filtered view. And that's basically it to create a custom report. And they can be scheduled just like standard reports. You can get them emailed to you automatically, export to PDF, all the regular options that are available on the regular reports. You'll just have your own custom one now that'll live in the customization section. Last little tip about slicing and dicing data and seeing things quickly and easily to get insight is custom dashboards. Custom dashboards are great for quick visualization of a hand-selected group of metrics. Just something you want to spot check every time you log in and see it on your dashboard every time you log in. Custom dashboards are the way to go for that. You could create a dashboard by going to customization dashboards. You can start with a clean slate and just add anything you want. Or there's a gallery of pre-done dashboards that you can actually have pre-done custom reports and pre-done custom segments too. But you might want to, especially for dashboards, it might already be something very similar to what you're looking for. But someone created, you could just import from the gallery. And you could always tweak it if you started with an import, you can tweak it, or you could start with a clean slate and add whatever you want to it. Either way, dashboards are great for that quick peek at things to always be in front of you when you log in. So to recap, to effectively turn data into information and information into actionable insight, your data needs to be accurate, comprehensive, and segmented in a way that can answer questions. And there's a link to the slides again, and I'm happy to answer your questions now. Wow, everyone understood everything perfectly. For questions, I'm going to pass them out. We have the beach ball? OK. After you install all of these modifications, how do you undo them? If you install the modifications that we talked about, how do you undo them? Like if you change the filter, how do you undo it? OK, so with filters at the account level, those can't be undone. So that's why I harped on, like, always, always, always create an unfiltered backup view, because filters can't be undone. Everything else we talked about can, though. So the custom segments, the custom dimensions, the custom reports, I'll actually go in and do a little. Yeah, let's do it together here. That's screen sharing that, right? OK. All right, so if I, let me try to replicate the example from before, actually, it was landing pages. So if I was looking at the landing pages and I noticed that the career section was one of the top landing pages, and I wanted to know where all that traffic was coming from, this is how I would do that with that secondary dimension. Source medium, it adds that second column. I only have seven days worth of data here, so this isn't showing much. Let me open that up a little bit more. So it adds that column, and that's very temporary. In order to undo that, you just hit that little x right there, and it's gone again. So secondary dimension is very, very temporary. If I wanted to do a segment, I would hit Add Segment, New Segment, Careers, Section, Condition, Page. There's a lot of things with Page in it. I hate this finding this. There we go. Contains, Careers. This little thing, I'll update and test it and tell me I've got 6.4% of users that match that condition, so I can make sure that I did it correctly and then save. So these stay available there all the time, but you can delete them. So right now I have the Careers, Section, Segment enabled. So any report that I go to, whether I'm on landing pages or if I go to one of my other favorite spots, Acquisition, All Traffic Channels, this is showing where the traffic is coming from only for the Careers, Section, because I have the Careers, Segment there. But I can just take that off at any time. That doesn't delete the segment. It just removes it from being viewed right now. I can always just go grab it again if I can type right. There we go. Apply. And actually, I've got both All Users and Careers, Section on right now, so I'm going to see them both. If I wanted to see just Careers, I can take off All Users and then I can undo it again. But by taking this off of my current view and hitting Remove there, I'm not actually deleting it. It's still available in my Segment List here, unless I just delete the segment from there. And Custom Reports, that lives here in Customization. I've got just one example in this account here. So this was a report about something that doesn't exist in our account anymore. Yeah. Most of our Custom Reports are in our client's accounts. But you can just delete a Custom Report there. So the only thing that really can't be undone is the filters at the account level. So if you went into Admin and you went to Filters, these are all the referral spam filters right here that Carlos handles and updates for us. We've also got a couple of different things beyond here. But that is the one thing that like, yeah, I can remove the filter, but the data is already gone forever. So that's why we always have a backup view. So I don't know how big that is if you can see that. But our master view is the one that has all the filters on it. And then there's the unfiltered view, which has no filters at all. So if we did accidentally filter data out of our main view and it's not recoverable, we can at least go here and find it in our unfiltered view. No, it's kind of confusing, but does that help at all? Question about that. On the unfiltered view, is there a way to filter it after? So you can just show the captured data? So I think I understand you're saying on the unfiltered view, is there a way to filter it after the fact? Yes, sort of, by using custom segments or custom reports. So these filters in the Admin level are ad removed permanently. So the unfiltered view can't really be permanently at the core filtered again, and vice versa. If you use it as a backup, right? But if you're using it as a backup and then you add. Yes, so if you were using it as a backup, you're like, oh crap, we lost this data. We filtered it out by accident. It's completely gone out of the master view, but you use the unfiltered view as a backup. Yes, you can go in and filter temporarily just to see what you were missing. So if I go into our unfiltered view here and we were like, let's see what example do I want to use here. Let's just keep using the career section if I was like, oh crap, I accidentally filtered all careers traffic out of our master view permanently. I can go into the unfiltered view and then just another quick dirty tool is this little search box here. Just search by careers and find it that way or just that segment that I had before. I can apply to the unfiltered view. Right, you just apply the filter to the unfiltered view. Yeah, apply the filters to the unfiltered section. Or I shouldn't say filters. Apply the custom segments to the unfiltered view. Right, yes. What was the other point? I was just going to make while I was in here. With the unfiltered view. Oh, the custom segments actually kind of belong to your account.  You can now go use it. They actually can't because it's tied to your login, your account. But you can share it with them. Under Actions, you can hit Share. And it'll give a link that you can send to them and they can then import the custom segment that you made. Same thing with custom reports. Everybody going to use the beach ball? This is fun. There we go. First of all, great presentation. Thank you for that. You talked about tracking things that aren't conversions. Just a quick tip. I just want to give a shout out to the Pixel jar guys. They've created a plugin called Click Ranger Pro for WordPress that lets you set up that tracking on different elements super easily. That's really good to know. What's the name of the plugin? Click Ranger Pro. Click Ranger Pro. OK, so if you couldn't hear, she was saying that this allows you to set up event tracking very easily, visually. I will have to check this out. But I actually have an actual question. I just wanted to give them a shout. But I've noticed that the load time, the page load time reports and analytics always seem to be a lot higher or longer than from speed testing tools. And I was just curious if you had any insight into why that might be or what they're doing differently to track it? Yeah, great point. That has been something we've been grappling with quite a bit. So the site speed metrics, let me pull that up. In Google Analytics, if you go to Behavior and then Site Speed, and there's a variety of reports in here, I'm just going to go to Overview, you can see reports on your site's average page load time. And so what she was saying was this very often differs, is often higher, than the speed testing tools that everyone's using now to prepare for this algorithm update. And why is that? First of all, the speed testing tools are what Google is referring to as lab data. So it's simulated. It's not real users. It's a tool that's simulating a real user under certain conditions that you pick. But this data in Google Analytics is what they call field data. It's real, actual user data. So not every single user reports page speed to Google Analytics, they sample it. So you'll see in these reports that it says somewhere in here, right there, 52 of page views. It never tells the total page views. I don't know why it's glitchy like that. So it's saying 52 of however many page views we got in this time period that I'm looking at, sent page load time samples. So it's actually a really small sampling. So first of all, it's a small subset of users and what they actually experience. So there's a lot of factors that could have come into play. There's also, this is averaged over time. So when you're using a speed testing tool, it's just that very second that you ran the test. As you can see here, something happened and we had a spike on June 5th that totally threw off everything else. So it's now saying my average load time is 9.14 seconds over the past five weeks or so that I have this report up for. But really, it was less than a second for most of the time. Maybe here it went to 3.52. So if I slice and dice this date range differently, I'm going to get a totally different average page load time because one small user for some reason had a 45 second load time on one page view. So that's some things to keep in mind. Also, this is for all regions. And as we know, region counts with speed, which is why we have been looking at mobile United States. I am logged into the wrong account because I don't have my segments here. We have a segment for mobile United States and desktop United States so that we're looking at page speed just for, because most of our clients are only dealing with United States, so that we're looking at page speed just for the ones that are closest to us and we're trying to rank in the US. So there's a couple of reasons why it might be different. Thank you. No problem. I already got about three, a little less than three minutes left. So the question back there, I'll just repeat it if we don't want to, or you can try to hit him in the head with the ball if you want. Over. Right, right, yeah, gotcha. I'll repeat for the sake of the recording or I'll summarize. Basically, we're talking about tracking funnels. So in Google Analytics, when we're tracking conversions, you can actually set up a funnel so that it shows the whole set of actions that led to that conversion. So like, viewed the product, added the product to the cart, went to checkout, finished checkout, et cetera. But his question was if you're on the AJAX type of a checkout where you're not taking the user to separate pages for all of that, then how do you set up that funnel? The answer would be with event tracking. So using, typically Google Tag Manager is the best way to use event tracking. You can track pretty much any action. So even if they're not going from page to page to page, if they hit the next button in step three, you can fire an event to be tracking Google Analytics at the time, step three. Yes, so that's how Google Tag Manager, you set it up. You say like, so this button that is, you could do a class, you could do an ID, you could do a certain set of JavaScript. There's so much, so many ways that you can fire a tag. And so it could be like the ID of the button for step three is ID equals three. You can set up, no, GTM would send it to GA. You'd use GTM to fire the tag to send to GA and then in GA use that data, like click step three as an event to set up the funnel. It is confusing and on a system where it's you're not actually changing pages, it can get complicated, but in most cases it can be done somehow, some way using Google Tag Manager to fire the event, send the event to Google Analytics and then set up the funnel from there. All right, so I think we're done then. Thank you guys so much for hanging out with me.


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