Uncovering the opportunities of Google Analytics 4 together with June Li, CEO ClickInsight Corporation.

Hello and welcome to E Commerce Growth Stories. This is your hostess with the mostest, Juliana Jackson, aka the CLV lady. And also I am the Chief Evangelist for OmniConvert.

And this is Valentin Radu, CEO of OmniConvert. Thank you for tuning in today for a new episode of E Commerce.

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Great, great, thank you. Thanks for having me on. This has been a great podcast to follow and I'm honored to be here with you today.

I am so excited to have you and I have a million questions to ask you. But first of all, just because this podcast is also video audio, it's on LinkedIn, it's on YouTube, it's on Everywhere. For the people that do not know you, please tell us who are you and what are you doing with Click Insight?

Sure, sure. So, Jun Lee, founder and CEO of QlikInsight ClickInsight is a specialist in digital analytics and we're located in Toronto, Ontario, Canada. We've been here since 2005 and we've just morphed and changed with digital analytics all the way from when it was still called web analytics. We consulting, training all the way from strategic to the end. And what we like to say is that data analytics definitely has great potential to unleash what might be hidden. And we're here to help. We're here to help as your guides in this journey. And it is certainly a never ending journey that's always changing for us. Not only do we do consulting, but we also provide training as well.

And it's so needed. Right. When it comes to data.

Yeah.

Thank you for introducing yourself. I met you while crashing your clubhouse room back. Welcome, everybody. I don't even know how long it was because I swear, like everything just flew by for me in the last period. I think it was maybe like five or six months ago. When you were on. Yeah, yeah. Then you were on Clubhouse with Steen, with Jim Stern, and with Paul for voy. Yes, yes, yes, Paul. And I was like so struck with, you know, with how you. How I. I mean, obviously you have an analytical mind. Right. But for someone that just focuses on, I guess, a different layer of data and looks at data in a different way. Because at the end of the day when we talk about data, everyone is super subjective and we're using it to justify our decisions sometimes. Right? Yeah. So when I met you, I was very struck with you. So I'm happy to have you here. I want to ask you a lot of questions, but I think we can easily. Yeah, I'm about to grill you with the questions right now. I was waiting on this. So, Jun, the question on everyone's mouth right now is what's happening with privacy? I feel like everything in digital marketing right now has taken a crazy turn. I mean, it was expected, but what are your thoughts about what's happening right now?

Yeah, and actually the session on clubhouse you joined was on privacy. That's how we got started. We got meeting and privacy is. A lot of people are concerned about it, rightfully so. But it really is something where time has come and everyone who uses data and works with it analytics needs to be really careful and treated data privacy rights respectfully. So it is something that everyone should be spending time to understand and also the differences globally, whether it's Europe or North America or South America, wherever it is, it all different. However fundamentally it is that we should be transparent about it and not try to hide it and not try to get around it. So it's a growing area. It fits very, very well into the area of data governance. And if we do want to use it, it is also, and this is part of what was discussed back then, and I think it was February, January, whenever we got together the first time, that it actually is an Opportunity for organizations, whether it's brands or the analytics companies that work in using data to make it a competitive advantage, to be transparent and actually help people understand how it's being used, how they might get into trouble if they're not managing their profiles appropriately, and actually how to question the folks who are working with the data in a way that makes sense. More and more, the language of consent is something that is getting to be more prevalent. It's still very difficult and I think it's confusing. Right. And it's definitely a good topic that we are looking to have more conversations with our clients and understand as they evolve through their journey, understand what consent really means. How can we make this easier to understand not just them, but also their clients? Because it really shouldn't. Marketing should get involved in this. It's not just the purview of lawyers and it shouldn't be every time a privacy issue comes up or oh, it's over to the corporate lawyers like it. This should be discussed by marketing as well. And I know we're going to get to Google Analytics 4, but that is one area that Google Analytics has and Google have made a platform that inform consent and with that in place, then if there is less data in certain places, interpolating what's missing, that now gets to be a really, really interesting challenge. And one that with Google Analytics 4 they're going to be using machine learning to try and fill the gaps. So it's certainly going to be a bit. It's definitely a new and different way, but certainly something that marketers need to understand. The limitations but also the opportunity of that feature.

I agree. And in a way, you know, like I'm thinking is the, is really the e commerce world ready for this type of consent? Because I don't know if you noticed I was saving this question for you. I haven't been posting about it because I just wanted to save this for you so I can repurpose it afterwards. But honestly, honestly what I do every day, I see a lot of websites, right? E commerce websites and you know that everyone is asking you for your email so they can give you the 15% off, whatever. It's extremely rare, if not like you probably can tell where I'm going. I never see people being asked when they give their email to agree with the terms of services of the website or their privacy policies. And a lot of websites, you know, e commerce website that we give our credit cards, you know, to and our personal data, they don't even function with the privacy policy on the website. Where it's very clear what is their data used for. So what do you think about that? Isn't that crazy?

Yeah. And that's why there is an opportunity for marketers if they are the ones who are now informing the customer, like helping them to be more aware of what their rights are, how they can protect themselves and help them to gain that knowledge. Right. That's a different type of relationship with the customers. But you're right. And there are no. Yes, there's legislation and there's a lot to manage though, Right. I don't think that it's unlikely that any government body will be able to enforce it. And when they do, they're going to go after those who the big organizations that have the deep pockets. And that's what's happened here in Canada as well. The Canadian anti spam law. There are some high profile big brands that have been fined many millions of dollars. I think the risk though to privacy are all the smaller sites, right, where they haven't adequately performed the risk assessment and have taken the steps to guard against that. And even the informing customers about the fact that maybe multi factor authentication, where it's texting a code to your phone that's actually not that secure. It might seem to be secure, but that can be interrupted. And a lot of people think, oh well, I've got that text to my phone set up when I log into the site and I'm protected. And in fact that actually makes it easier for someone to hijack your info. So it's that kind of informed consent, but education as well, that is a big challenge out there. Right. And so many stories about ransomware and everything else, like the individual person is also at risk for that.

It is, it is, it's a very big problem. And honestly like people do not talk about this enough. And I just, and this is one of the reasons why I'm not a fan of SMS marketing because as you know, I guess the termination of the sms, you know, from a no perspective, right, you might be receiving the same, from the same phone number, different texts that are for different purposes. And you're like, you have to ask yourself a question like what is happening? Because I received the code from a store for a discount, but I also received my Twitter recovery from a password on the same termination and the same origination of the sms. And this is, I mean I'm not saying email is the safest place to be, but I think it's safer than SMS because there's a lot of fraud and a lot of, you know, security issues that occur with sms and I don't think there has been enough education in the market when it comes to SMS marketing. I just think people are using it because it's promoted, you know, that much. Yeah, but enough about sms. I'll fight that battle.

That's gonna be another topic to chatbots. That's definitely another topic for discussion.

Yes, yes, it's insane but I promise the people on LinkedIn because people are watching. Let's talk about GA4.

Sure.

I am not aware about anything regarding with GSO. I just want to tell everyone that's watching I am a noob. I didn't have time. I'm suffocating in first party data with what I do. I just want you to give me the crash course on Google Analytics 4 and tell me what does it bring, how it can help us do better.

Yeah, yeah. And so why is it Google Analytics 4? So Google Analytics itself came into the Google universe when Google bought a company called Urgent. And Google Analytics 4 is the fourth major upgrade. And it's a significantly different version from the third version, which they don't refer to it as Google Analytics 3, but it's called Universal Analytics. And the difference is that Universal analytics evolved from the times when websites were mainly pages, right? It's a page view and as you click you go to the next page, the next page and the next page. So that's the way that Google Analytics, Universal analytics, the version that's in use by most people, that's its legacy and its background. So Google Analytics 4 is a completely new, built from the ground up product and it is very, very different and it is intended to be more. Take into account that it's not just someone who sits in front of one device, it could be multi device and it's a user who might have three or four different devices. It's not pages anymore, it's mainly interactions. For many, many years we have hacked, if you want to call it that, Universal analytics and previous versions of Google Analytics such that a page view can be anything you want. Now that is being called an event. It's really an interaction. You configure it to be what you want and it should stitch together how a user is behaving on multiple different devices, whether it's tablet or phone or laptop. And it allows those who have come up with some sort of user parameter, client id, member id, whatever it is in the back end of someone's customer relationship management, CRM system use that ID to stitch everything together. So what that helps is that if there's attribution that is like people, the question is how is my marketing performing and where should I continue to spend my money? And for this particular campaign that was so, so successful, what were the changes that led to that being successful? So that you can inform what strategy should be taken the next time around. Right. So analytics should answer questions, right? And it should be decision making support. It's really not about the data, it's about making business better and improving performance. So the difference with Google Analytics 4 versus previous is that there is much less baked in that's out of the box. It's much more customizable and can be configured so that it's more driven by the way the business is actually operating. You're not forced into the view that what universal analytics is, there's a plus and a minus there. I mean, one of the great things with universal analytics and previous versions is that someone who does not really understand how to configure digital analytics, there's a standard set of reports that you can start with. But the problem with that is that then as you evolve and it doesn't quite work, then your amount of customization that you need is not really there and you start to have to hack the system and get around certain things. So the great thing about Google Analytics 4 is that it gives you more freedom with a well designed system. It should last you a lot longer. It should have good data governance, good quality, and Google's using the term that it's durable analytics and that it's not just for now, but it will last and it will scale and will grow with you. And you can configure it so that it's flexible to the way your business is actually set up and run. So with all things that are more configurable, you then need more expertise because if you don't configure it properly, it's still not going to fit. Right? And that's where we always say you got to start with strategy and understand the types of questions you need to ask, the way you're going to segment your data. And for E commerce, for example, if there are certain attributes, how you look at your product line, not just one linear way that you do it, but you segment it by brand name or the audience type, the use and you might have seasonal sales and all this other stuff that are going on, promos, you have to think about all those different ways that you're going to slice and dice your data and then set it up so that it can do it.

I love it. I Love it. I mean for me to be able to track everything, like when it comes to E commerce you want to see how much time people spend to look at a product, how much back and forth they did between different filters or if they use search. But you know, what's the problem in E commerce? It's very hard to find companies. I mean I think only the larger brands have data people or work with someone that really understands data or get consulting from people with data. So the mid size layer is very underserved in terms of data. Even though they have a lot like there's a lot of brands that make great amounts of revenue and they acquire a lot of customers but they don't do data driven decisions at all. Because usually the way they use analytics, the classic analytics that we have right now is just on the checkout page for instance. They don't have search parameterized, they don't do enhanced E commerce and it's very hard to if, I mean I'm not disrespecting Google in no way but it's very hard if you use their own FAQs to try to set up your analytics, especially if you're not good at it. So will you know analytics4 come with a better support for people that are just entering where it's not for people that like if you don't know analytics good, you should just stay with and you know, pay you or someone else to help?

Well, I think they Google any tool and it's not just Google Analytics, digital analytics, like any tool that you have, whether it's a car or a microwave or something like that. Right. If the use doesn't match the tool that you bought, it's not going to work. And whether you're buying a car or microwave or anything like that, you don't want to over buy or else you end up like the car for example, you buy a car and it's not good for if you have to regularly haul a family or their friends around. If you have a nice little Porsche or something like that, you can't quite fit in the back. So what we advise of those, whether they're big brands or mid size is to really think of the strategy and what is it that you're trying to get this analytics tool to do for you. And for the most part analytics on its own data itself shouldn't be the end state. It's really what's the performance objective even if you take a store and yes, you're focused on the fact that you need to convert and get revenue from it but think of a physical store, it's not just the checkout and the flyer, there's the whole merchandising of the physical store itself and how you get people to walk through. And in the physical store a lot of time is spent trying to figure out what makes sense on where you place things. And there's all the science about counterclockwise, clockwise. You start with the vegetables and you end up at the dairy or whatever it is. Right. So each E commerce store needs to figure out what that customer journey is. And if they don't understand the customer journey, there is no best way to merchandise a particular product. All the facets that you search on how that works depends on how your customers shop. And we think that whether it's mid size or big brand, they should focus on trying to understand that journey and what are the key things you need to learn about behavior and then collect the data, not the other way around. Because what we find is that if they think, oh, there's lots of data and the issue is we don't know how to analyze it. It's really the question first that you need to think about. So if you take a physical store and the objective is to have revenue increase in the store for the people you're bringing in, what products go together and so therefore you merchandise them together and when they come in for one, buy other things and that's how you increase your revenue. And that's where online, like the recommendation, people buy this, also buy this. Does that really make sense all the time? Does it change seasonally? Does it change depending on the segment? And that's where your analysis, for example, you said you're trying to put together all of that. If they have a hypothesis or something like that, then see whether that information is there and then run a test. Right. That's the thing, is that whatever you see offline, you don't know for sure it's going to work. That's one of the big things that we see is that with all the tools evolving and flexibility is that the test and learn whether it's AB or multivariate or whatever it is, that actually is what's needed to really have people ramped up. So it's not really the data itself. You start out with what the outcome is. You want the kind of performance increase you want. You understand for the, the customer who's on the site, whether it's E commerce or information, how they make their decisions and then be there at the right time. Run some tests. If you think that it may not be merchandised correctly and then move on to other aspects. With GA4, what's great is that there's more and more things that are built in this time around than Universal analytics one, for example, that's fairly popular, that's built in is scrolling.

Oh, that's so cool.

Yeah. And so it's no longer like Bounce Rate. It used to be like someone comes and lands and leaves and with previous like Universal analytics out of the box, no customization. If someone lands like a blog post or something, or even they come to check out a product and they sit there and they hit the multiple tabs, they read the reviews, but nobody's been collecting that data. There's no difference in when someone comes and lands on the page and leaves versus someone who spent 10 minutes reading all the reviews. You need to have collected that data. So more of that is built in into Google Analytics 4 than previously.

I love that.

Yeah. And because of that though, when someone is moving To Google Analytics 4, we advise that to implement in parallel and still have Universal Analytics. Is that what people have been looking at before? Because it's quite different. It's almost the same as if you were using the feet and inches way of measuring distance. And then you go to metric. Right. The numbers are not the same or ISO versus whatever. And so there needs to be a bit of a change management. People need to think differently. Like Bounce Rate is not a a metric that comes baked into Google Analytics 4 and the way duration time on site, it's different. So if you're trying to compare to last year or something like that, and you haven't been running in parallel, that's going to be a bit of a jarring experience and people won't be able to evaluate what's going on. Because analytics and data, its main benefit is tell you, are you improving or not? Are you on the right track? Do you need to make changes? It needs to. You need to maintain that continuity somewhere. And that's why we recommend that if you're going to go to Google Analytics 4, like start now and run it in parallel and have all that discussion about like why is this different? And try to understand how to use it. Because if you do want to check back to last year's holiday campaign and you just switch and there's a break, how can you compare how you did last year if now you're on a different basis? So it just run parallel and with the use of Google Tag Manager or some other tag management tool, it's not that much work to actually fire it to both at once. You don't have to paste two sets of code onto the page.

I know a lot of cases like that.

Yeah. And you should be using a tag management system anyway because there's tons of marketing campaign pixels for tracking view through and all sorts of conversions that some sites might have 20, 30 different marketing campaign pixels and that just increases the page weight, slows things down. So if people are still doing that, definitely look at a tag management system to try and speed that up.

It's insane how many people do not use Google Tag Manager. I'm sure.

Adobe anything analytics use Launch. Yeah, yeah.

Or Enhanced E Commerce. It's a total different conversation. I just want to unpack a bit what you said because I think what you said, you know, like comparing the in store experience, which is a journey of course of decision making and cognitive dissonance and you know, back and forth is the same thing in E commerce. Right. I love this example because it's so easy to understand, you know, like, I guess a lot of companies forget that even though it's a different background, we're still the same people and we still have to, you know, get to a decision. And I think it's brilliant how you put it. I'm going to quote you on this. And also another thing that you said that I, that I like is that you have to have some sort of objectives or you want to know what you desire to monitor or to track before you just use analytics tool to inform your decisions because oftentimes you just use it because everyone tells you that you need to have analytics and you're just sitting there like how I sit in there. I'm trying to see how many people came on the blog and clicked something so I can see if it makes sense. And I think it's a big problem because even though there's so much information on the Internet when it comes to analytics in general and data, I just think that all of these amazing events that you and Steam and Jim and everyone is doing or Simo with his whole, you know, awareness, it's just all these things are not seen because unfortunately in E commerce things that are not, you know, like, I guess, I mean they're important but they're not sustainable for a business to function long term. They just go in the back and we just see, you know, TikTok videos and bullshit. Sorry, but it's so frustrating and I think it's a very hard sell for us in the space of data and strategy because especially for me because I'm the liaison from data to marketing. And it's so hard for us to have this sell. Thank you so much for how you put everything together. I think it was brilliant. Going back to GA4, you were mentioning something about the machine learning layer. Tell me more about that because I'm fascinated with machine learning.

It's Google, right? And the cloud platform. A lot of investment there and that's what they're bringing forward within the tool. So part of the challenge with privacy and we talked like iOS 14 and so on, where the attribution information may not be there, people may actually not consent to be tracked and therefore there's going to be holes in the data. So what Google is spending a lot of time in investing in GA4 is to try and interpolate and fill in the blanks, if you want to call it that. And they are not investing in that. For the prior versions like Universal analytics, that will not be there, whereas it will be in Google Analytics 4. Therefore the audiences and trying to interpolate that. And it's not getting around privacy, it's working with the data that you do have to fill in the blanks. In some ways it's like predictive analytics, but more in real time. The machine learning will be looking at data however, at the same time. What that means though is that analysts still have to check it's not going to be perfect. And like the self driving car that sometimes does the wrong thing, machine learning will not be perfect. And it's learning all the time. There still needs to be somebody there who's looking at it. And if it doesn't make sense, then don't take action on it. And I think that's the difference in the role of digital analysts is understanding that, understanding what to look for and instead of working in the data as much, also looking at the integrity of the data and whether or not it makes sense. So there's ways to leverage it, make it work more effectively and efficient for you. But also there's a bit of caution. Like this is still early days, right with with machine learning. And there's all sorts of crazy stories about like if you talk about people trying to train data and trying to train an AI to learn how to identify sheep. I don't know if you heard this story where most sheep are in the middle of pastures, right? So when it started saying there are sheep, where there were no sheep, it's because it got trained to identify grass. It identified grass as sheep. So when it was showed pictures with grass in it, it said there were sheep. So those kinds of things. You just have to be aware that weird things can happen. And so because it is early days, the analyst, the digital analyst now needs to be aware of that when they are choosing to use some of the machine learning that's there. So you can't ever park your brain? Never. Like analysts cannot. Definitely cannot park their brain, but it definitely is more interesting, I would say it's very, very exciting to have the opportunity to use this. And if it does work, then it takes some of the number crunching and having to interpolate that data by hand. It would be very, very difficult to try and manually fill in the blanks. So the fact that Google is using that knowledge, try and do that at scale across all the accounts and the data they have, that's very, very exciting.

So this is awesome. And you were mentioning predictive analysis and I remember someone sent me a Link with how Analytics4 will do predictive analysis on customer lifetime value based on RFM segmentation. And remember, this is my favorite topic. So at the moment, the way I do predictive analysis based on RFM is not by using behavioral data from the website. I use transactional data. So historical data. So Analytics 4 doesn't have access to the historical data from Shopify. For instance, if you're a Shopify store, you have access to some goals and some things you can put that Google can track, but you don't have access to all the order average price and the price that you sell, the discounts. Like there's a lot of, you know, questions, so how will you get what I'm saying? So like when it comes to customer lifetime value, you have to take everything into calculation to be able to do it. So I was very curious, how can Google, I mean, it's Google. But how, how like technically speaking, from like structurally speaking, how they can do the predictive analysis on RFM without, you know, having the full picture of the, of the situation. I think it's fascinating. But how, how will they do it?

I think the work you do offline will identify the key things that influence that decision. Right. So there's always been ability to import data.

Yeah.

So there are two ways to do it. There might be a way to actually import it into Google Analytics and do it that way. Or you take the behavioral Data for Google Analytics 4 and you pipe it into BigQuery and then in the cloud platform tools where you got TensorFlow and all that other stuff, you do it actually in the cloud platform so you could upload your data into the cloud platform tools like BigQuery and then you also pipe in the behavioral Data for Google Analytics 4 and you use the user, whatever your clients might have as far as identifying the person, to stitch everything together. So now you have timestamp behavioral data that you can then merge with your transactional data as well. And if they do have enhanced e commerce or some sort of e commerce collected. Right. Then you can reconcile that the actual financial information is the final version of the truth. One area that our clients, even with universal analytics, have found useful, even though the financial system is the real version of the truth, the online e commerce data, even though it's not 100%, it's directionally correct most of the time and they can make it helps them make decisions quicker. So it's not accurate, but it's precise enough. Yeah. And Even if there's 10% or 20% that don't allow cookies, which is what universal analytics needs, if something's changed 20% or is down 15%, you're looking at percentage changes. The absolute number really doesn't matter. The more important thing is that it's down or up. Right. And that typically is correct. And so that's good enough information for them to make decisions. Now we have had some clients who typically have relied on offline data from the back end system and they've gotten around the fact that it's not accurate, but it's directionally correct in changes. And so they are able to make much better, much more informed decisions around their marketing and changes in offers and things like that, much more, much quicker and they're much more agile in that respect. So that's where digital analytics pays out. It's not the 100% accuracy and it's whether it can be a better decision making tool so that they can get more out of what's happening in the marketplace right now. But you're right, there's all sorts of missing pieces and it's never 100% right. But you do what you can and then you learn from it. And oh, I might have to go back and collect this data. So next time you get it and you move forward. And in doing that the predictive cycles can get. The learning cycle is a lot faster. Yeah. And the important thing is to learn and progress. Like it's not gonna ever be perfect, but like 80% that you take action on is better than trying to get a perfect, perfect model that you never get there. Right. And by that point the opportunity is gone. So the great thing about digital analytics is that it's something that can help you learn and take action a lot faster than.

I think. A mixture of both is very important because right now for me is a pain because I have to do the segments and I have to make sure I do the scores and then I upload them in analytics and then I have to make sure that, you know, I follow the, you know, the goal line. And we, I mean, we were thinking about doing this integration with analytics, the classic universal analytics. But then we were like, ah, you know, like if, you know, Analytics4 is going to do this predictive analysis, this might be more accurate because it's, you know, it's a, I guess it's a fuller picture, you know. And I'm just curious how they do the rfm. Like I was fascinated, I was reading and usually like we do, you can do it either by netting or by biting. So I'm super curious how they will do it would be on the same model as IBM. IBM has this tool, I forgot the name right now of it. But they have this tool when they do rfm, but like way better than we do it. But it's a quarter of a million, that tool. But it's really amazing. So I'm excited. But let's go back to. I don't want to talk about customer lifetime value.

Oh, it's definitely. No, it's important.

But it's one of the most important things for me from an E commerce perspective. I think being able to do this from a behavioral perspective and mix behavioral data with transactional data. Yeah, like, I'm sold. That's the only thing I saw. And when I saw it, I was like, okay, this is cool. This is the future of E commerce. This is the future of online commerce. And it's amazing. I think, you know, everything that's happening right now, June is kind of forcing people to go in a very holistic, fundamental space. And all this, you know. Three. Yeah, we need it. Right. And I mean, a lot of people don't have any foundation to go back to because, you know, is the entry point. The barrier of entry in digital marketing or E commerce is very low at the moment, unfortunately. And it's very easy for everyone to call themselves a digital marketer. But in a way, I look at it as an opportunity for a very nice cleanup in what happens on the market and allow people that actually know what they're doing to be able to help the companies that need their hope instead of everything for everyone that's happening right now. So you were telling me that you have an event About Google Analytics 4. Tell me what Is this event about and how can I sign up for it? I'm going to sign up for it.

Let me know if this came through. Did it come through in the chat?

Yes, it did.

We had issues before with a redirect URL. So let me know if this causes you trouble and I'll send you the raw URL. But because it is mapping through. So what this is is about using the information in Google Analytics 4 to create audiences that will then leverage the machine learning capabilities that will be coming with GA4. So explaining what that's all about. It sounds, I know, like a lot of very, very technical information. But the session is intended to, it's interactive. So explaining a bit about it, how it can help marketers as well as analysts. And there will be a live Q and A. So it's, it's. Anybody can attend who is interested. It's 2pm Eastern next Thursday.

I just signed up for it right now.

Oh, you did? Okay, great.

I'm very fast. I'm very fast. I saw Building new audiences with machine learning. I'm sorry.

Sold.

Because this, this audience thing is always like, you know, it's funny I'm gonna say it, I don't know, I'm gonna upset some people. I'll just say it as it is. You see that look alike audiences are trying to get like, like a brightness to them. Like it's something new right now in the market. But you know very well that this has been done for so long. But right now everyone is talking about look alike audiences. Like it's something that is never unheard of. So in a way I'm really happy for people that call it out and just speak about it. It's just an audience. But learn how to build it and you don't have to reinvent the wheel to add different names to it when it's just using better data, cleaner data, durable data. I like that you can use that to lower your acquisition cost and bring better traffic to your website and better conversions. I guess, you know, this is the season of, you know, cleaning house, cleaning data and you know, I guess Customer Journey is going to become the next thing. Yeah, yeah. That everyone needs to work on. Thank you for being here today.

Oh, my pleasure.

I love talking to you. Tell me, how can people reach out to you if they need to? You know, I'm hoping a lot of people are reconsidering their analytics right now and probably they have it two times on the website as I've seen most of the. On every page. How can people reach out to you and your team.

Yes. So through our website, clickInsight CA so C L I C K I N S I G H T CA and if you Google me, June Lee in Canada and Toronto, you'll probably come up with our site. Our information is very good there and what we aim to do is really understand the potential and the technical comes later. We mostly start with the customer, the customer journey and the outcome that our clients are trying to achieve because that's really more important and the data is there to serve getting that outcome right. The data is not important if it can't do that. And all the data in the world won't serve if the Strategy isn't there.

100%. June, thank you for being here. And also she's linked to. She's on LinkedIn but don't send her sales pitches, please. I had complaints. I had complaints. Don't forget to sign up for the event for the 24th of June. Building new audiences with machine learning using Google Analytics 4. This was a great session. Every time I talk to you, I get smarter. So thank you so much for everyone watching YouTube, Facebook and LinkedIn and for YouTube for being here. This was amazing. I'm excited for the future. Thank you everyone. See you guys next week. Bye.

Thank you.

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