Coldsmoke Founder Series: Kevin McLaughlin Co-Founder of Slide Rule Tech

We are happy to share with you the highlights from our founder series guest, Kevin McLaughlin, Co-Founder of Slide Rule Tech.

Slide Rule Tech is a technology consultancy specializing in Google Analytics and Tag Manager, CRM Development, and general technology audits.

They help you use technology to reach your core organizational goals efficiently and effectively.

Kevin McLaughlin, Co-founder of Slide Rule Analytics, highlighted the significance of data analytics in enhancing the profitability of e-commerce stores. He advised store owners to begin by asking simple questions to develop their data analytics skills, such as analyzing the number of orders in the previous week and understanding the conversion rate from visitors to paying customers.

McLaughlin emphasized the critical role of accurate data, stating that trustworthy data is essential for making informed, data-driven decisions. He discussed the range of tools offered by Slide Rule Analytics to assist Shopify and e-commerce stores in obtaining better data and making improved decisions.

One notable tool mentioned is their Google Analytics integration with Shopify, which is highly rated in the Shopify app store. Additionally, Slide Rule Analytics provides an AI chatbot in beta that enables users to interact with their Google Analytics and Shopify data, facilitating easier access to information.

In summary, McLaughlin stressed the importance of starting with simple questions and gradually building up data analytics skills to enhance the profitability of e-commerce stores. Slide Rule Analytics' tools, including the Google Analytics integration and AI chatbot, are designed to help users obtain better insights and make more informed decisions.

For more details, you can explore Kevin McLaughlin's LinkedIn profile and visit Slide Rule Tech’s website at

Mark: Kevin. Hello. How are you?

Kevin: Good. How are you?

Mark: I am great. It's good to see you. Thanks for coming on and joining the dozens of people who are excited to listen to all of your expertise around analytics.

Kevin: Oh, happy to be here. Thank you for having me. 

Mark: Excited. Thanks everybody for joining.

We'll let a few people trickle in here and there, but I'm excited to talk about how we can turn your data into dollars and how to actually use your data from your ecommerce stores to improve your store's profitability.

And one of the things that I see all the time when I'm talking with clients or perspective clients is that they don't trust their data.

And this is obviously a really important issue because, as folks are trying to be data driven it's really hard to make data driven decisions when you either don't have data or you have data and you just know that something is wrong.

You're not exactly sure what it is, but you know that you can't trust it and so you're kind of flying blind. 

So I'm excited to have an expert here with me to shed a little light on some quick tips and tricks and sort of the importance of all this stuff.

But I'll let you, Kevin.

I'll let you introduce yourself. 

And then we can jump in and get some, get to the meat of things.

Kevin: Absolutely. Sure.

So, I'm Kevin McLaughlin. I'm a co-founder of Slide Rule Analytics.

And we build a bunch of tools for Shopify stores and ecommerce stores. to help them get better data, to use that data, to make better decisions and really, you know, just try to help them answer questions about what's going on their store and, hopefully, how they can make it more profitable.

Mark: More profit is always good. 

Kevin: That's what we're going for.

Mark: Yeah. 

Can you, you know, not to jump right into a sales pitch. But you're the co-founder of Slide Rule.

Can you tell us a little bit about what Slide Rule is, what it does, how it works? 

Kevin: Yeah, absolutely. So we have a couple of different products.

We have a Google Analytics 4 integration with Shopify.

That's one of the, either the best or the always one of the top three. 

If you Google in the Shopify app store for Google Analytics 4 you'll see that it's a Shopify app and it integrates Shopify with Google Analytics 4, so it takes your Shopify data and sends it to Google Analytics 4, that has, you know, thousands of installs and great reviews, thankfully from a bunch of different stores. 

At this point I'm sure we'll dig into a little bit about Google Analytics 4 generally, and you know, some of the issues with Shopify is built in integration and some of the issues with Google Analytics 4 generally and then also have, in beta, an AI chat bot for chatting with your Google Analytics 4 and Shopify data hoping to try to make it easier, more accessible for everybody to actually get some answers out of their data. 

So if you know, if you've dealt with data before, either in Googling or looks for, or in Shopify or any of the dashboarding tools out there, you know, that it kind of feels stale and limited and hard to, to get the answers of what you're really looking for.

So that's what that products were, like I said, it's in beta. So if anybody's interested in trying that out, it's free while it's in beta and we'd love to love to chat about that too. So.

Mark: That's so cool. Being able to chat with your data. It's pretty novel concept. That's sort of an advanced topic.

Why don't we start with something a little bit less advanced and just talk about generally, the power of data and analytics? 

So how does somebody get started with data analytics within the Shopify ecosystem? 

Kevin: Sure. Yeah. So I always encourage people to start simple, right. I know a lot of times when people think about the kinds of questions when they're trying to be data driven, they often want something that's like a really general answer. 

So they want to be like, they want to answer a question like, what are, how are my users using my site? 

That turns out to be a very complicated question. The internet is a big place and you would be shocked. 

Like if you've ever watched just like screen recordings of people using the Shopify store, you would be amazed the different customer journeys and different paths that people take on your Shopify store. So it's really hard to get like a clear picture of a general question.

That's just like, what are people doing on my site? Really? We want to start with the basics that sometimes aren't so basic. Really? We want to know first things that. We really just want to know, like, how many orders did I have last week? That's often where we want to start and the best place to start there is just looking at Shopify data. 

But often the next question is things like conversion rate, right?

So am I getting traffic to my store? And how many of those people are converting? 

That can be a tricky question sometimes, but the place to start there, most people, what they'll do is, they'll install Google Analytics on their Shopify store either with an app like ours or with Shopify is built in integration and they'll get data going into Google Analytics 4 so that's kind of the tracking piece is just getting and capturing that data.

Right. And then they'll go and look in the Google Analytics 4 reporting interface and try to answer some questions like: What is my conversion rate? And that's, that's a great place to start, actually, because, like, so much of our Shopify questions really come down to conversion rate, and there's like, thousands of reasons why your conversion rate might be going up and down every day. But it's a great number to look at to the start to start with, and it also will inform if you do have tracking problems that maybe, like you said at the top,.

You can't trust your data. So like I was looking at to give a concrete example, I had a really big store. So this is a very sophisticated Shopify store. And they had a question where they said, why did our you know, the number of sessions in the United States just absolutely spiked yesterday, right?

It was like, and we need to know, like, why is that the case? So what's interesting about that is their conversion ranked tanks. Because the number of sessions went way up. So we went in and we looked and we were like, why are sessions going up? 

In that case, it turned out that there was a bot that was just hitting their store a thousand times yesterday and, and that's a, that's a tracking issue. 

Not really a tracking issue, but a reporting issue that you'll have to work through as you start to analyze data, you're going to have things come up like that, where you've got to be like, Oh you know, now we know we got a filter bot traffic out of this for us to be able to get accurate conversion metrics. That's meaningful.

The meaning there was nothing meaningful about that increase in traffic yesterday. It was really just Google Analytics. You know, some bot found their site and was Google Analytics was reporting a lot of sessions, right? So that's like an example of a great place to start and really like a thing you will continually circle back to is just like, what is my conversion rate? And breaking it down by, traffic source, landing page, things like that. 

Mark: Yeah, it's a good example. That's a good example of where like there was no action to take with the data other than filtering out the bot.

Do you have a good example of like where data analytics can significantly influence decision making? Because everyone's, I said it at the top, everyone's talking about making data driven decisions. Yeah. What are some good examples of that? 

Kevin: Yeah, I've got tons of good examples of how data can help you make positive decisions.

Those are always the favorite. Lots of times there's also negative decisions like you find out that, maybe your Facebook ad campaigns are just not converting at the rate that either Facebook or potentially your ad agency tells you that they're converting at. Right. But like some good examples, I think, you know, starting simple again, yeah.

Really common to just go and look at like landing pages, the conversion rates of your different landing pages and seeing like, Hey, this landing page is just converting at a way higher rate than everything else right now that obviously could be because that's your most popular product, you know, that's a starting point for looking at the data.

But oftentimes you see things like, you know what, like that landing page just like has some significantly different has like a. new call to action that we put on that site. And that's like the biggest difference between this and another landing page. And you can convert there one. So that's like an actionable item you could take as like, we need to make the other landing pages look like this one.

For a specific example that I always really like is we were looking at another client, they're a large backpack store they sell lots of not just backpacks like bags and they found looking at the data. We put in some tracking to track when people looked at their at their size guide. 

So they have this really nice size guide that they developed. And so we just put in some simple tracking just to track when do users look at this size guide. And then we created a segment in Google Analytics 4 just compare. of the users who look at the size guide and the people who don't look at the size guide, what's the difference in conversion rate, right?

Cause we come back to that metric a lot. And it turned out that like people who were looking at the size guide, we're converting at like 10 X the rate of people who were not looking at the size guide. So they took that information back to their development agency. And they were like, we need to put this size guide, like front and center.

We need to like make this size guide like something, you know, big, a big call to action that people want to click on and this is my sort of favorite part of data and analytics is like we're starting the question with data.

We're like, wow, that's like a really cool insight that like. People who find the size guide convert at 10 X the rate often I will say, you know, you're, you're rarely going to find something that just like screams that large, right? 

We're often talking about increases of, you know, 10%. Increase as in like 10 percent of whatever the existing conversion rate is, that would, that would be a good conversion.

So 10 X is big. Right. But what I always like to say is like, that's the start because like, from there we go. 

Okay. Why is it that people who view the size guide, is it just because people who view the size guide are just like much higher intent buyers? Or is there something about the size guide that's actually helping people convert?

In this case, when we added the size guide, we saw conversion rates go up. And. So not 10 X across the board. So some of it in that case was like, Hey, people who are viewing a size guide or high intent buyers, but some of it was just like, there were a lot of sizing options. And, and like having the size guide, like comforted people enough to like make the decision to buy, like to turn that corner into from browsers to buyers.

Right. So I love examples like that because like we start with like, we see this like big difference in the data and then we can start to use that to actually try to make our store more profitable to try to increase our conversion rate. Right? 

And we also get to theorize about why that's and how can we take that explanation and apply it to other parts of the stores?

You know, maybe Maybe we need to reduce the number of variants of sizes because that's intimidating people, right?

There's lots of, lots of theories you could draw out of that. So that's always a good point too, is like data, data is really valuable when it starts a conversation and gets you thinking about what you can do on your store to, to become more profitable.

Mark: Yeah. And it doesn't necessarily have all the answers, but it can be directional as to, like, what do you want to start testing and creating hypotheses around? 

Kevin: Absolutely. When we talk about being data driven, I think a lot of people think what that means is the data is going to make your decisions for you.

It's never going to do that. It's never going to make the decisions for you. It can help, right? It can occasionally put things to bed, right? It can. 

We can say, Like the classic example there would be like we're running this Facebook ad campaign and we don't know if it's working well, we can go and look at the data and we can say like that Facebook ad campaign is, has a negative ROAS, right?

So sometimes it can just like put things to bed for us. That's good too.

Cause we can divert our energies and our money into other things.

But oftentimes. You know, we're not going to get like a perfect answer where it's just going to say like, ah, do this and your store will be better, but it gives us ideas of things to test.

That's what we're often looking for with data. 

Mark: Yeah, well apart from sales, what other areas of the business can analytics have an impact? 

Kevin: Sure. Yeah. I break it down into. You know, the things that we're often focused on are things that I think of as like the e commerce profitability formula, right?

Which really are like reducing customer acquisition costs, right? That's one way you can increase your profits. You can increase margin, right? 

So you can increase the margin on your products that's often not the web analytics usually aren't focused on that part. That would be something that's more about shipping and logistics and things like that.

And you know, going all the way back to, like, reducing your product costs and things like that. But you can increase your margin. Right. And then you can increase your customer's lifetime value. Right. So if we took customer lifetime value times margin minus your customer acquisition costs, that would be a good high level framework to think about, like, what are the dials we can turn with data?

How should we think about this to increase our store's profitability, which is what we're trying to do here.

So a couple of things that I think are interesting, you know, we are going to increase sales. That's like one big thing. But we can reduce customer acquisition costs, right? 

So reducing the cost of some of our Facebook campaigns right can be a great way. Now that can happen either by increasing the conversion rate of those campaigns or shutting down campaigns that maybe aren't giving us the return on ad spend that we'd like to see there. 

So an example there. Is we were looking at some Facebook data for somebody and I'm going to rag on the ad agencies here just in this one, but they do do great work, right?

This is a great ad agency. This was just a funny example where for some reason they had accidentally turned off the Facebook campaign. 

Some Facebook campaign that they were running for a week. Someone went and turned it off and then they turned it back on. And we noticed because there was a big drop in sessions to the site that that week.

So we saw this big drop in sessions. And we went and we investigated and we looked at the data and we saw like, Facebook just like went off the cliff that week. What happened? Right? 

But we took a closer look and we saw that the Facebook sessions went down. That's normally a bad thing. Normally, we get worried when the amount of traffic to our site goes down.

In this case, Facebook went down and purchases remained exactly the same. So we didn't increase sales at all. But what we did was we cut out a campaign that was just not working, like this campaign was just not driving revenue. So that's an example. We're not increasing sales, but we are decreasing customer acquisition costs.

Of course you know, Shopify stores are going to be very familiar with that and know that that's like very critical to the success of your Shopify business. So that's that's one thing. 

In that case to go back to that conversion rate metric, conversion rate went up. Right, because sessions went down, conversion rate went up, right?

Session went down, purchases remain flat, conversion rate goes up. So conversion rates kind of that like catch all metric for that e commerce profitability formula is like if we have a good conversion rate, if it's going in the right direction, even if sessions are going down, You know, we might be doing better as a reverse of that though.

You might have a great email list, right? Or you might be capturing this. This happens to lots of Shopify stores as they grow is they're capturing emails as people are either buying products or becoming interested in our products, right? From ads and things like that. And in that case, sometimes sending an email blast will decrease your conversion rate.

But the key is that's not paid acquisition. Right. That is a free acquisition to you. Now sending that email doesn't cost. There's no marginal cost to sending that email to your customers and so in that case, conversion rate can actually go down on those days. 

But purchases can go up. That's still a good thing because the CAC for those orders or the the cost to acquire those orders specifically was zero.

So that's another counter example. It's not, yeah. You know, we're not looking for one thing necessarily. It's not that if conversion rate goes down, that's always bad. 

It's why, why is it going down is, is the key question. And so that's another big lever. Like you said it's not just about purchases going up.

It can be about decreasing CAC is also valuable. So the other big part of it is increasing lifetime value. So often people will look at average order value. Now that's sales right? 

You are going to see that come out in sales numbers eventually, but if you think about kind of orders as this big customer places in order, that's a very important moment, obviously, in the customer journey, right?

For us that's not the end of the story. We want them to come back and purchase again. That goes into the emails, right? We want to increase the order value of the first order. 

We would look at average order value to see that things like that. So that's not increasing the number of orders, but we want to increase the total revenue from the customer.

Mark: Yeah, I think one of the key insights there is like, you, you got to know the why and that email example of conversion rate went down, but a bunch of purchases happened that wouldn't have otherwise happened and cost effectively zero. That's a good thing. 

Kevin: Yeah, absolutely. Yeah. It's always about why.

So that's why we're always wanting to. You know, ask questions of the data, right? Like we want to dig in deeper. There's no like one metric I can just give you that's like optimized for this. And you know, that's that, right? 

Mark: It'd be nice if if there was.

Kevin: Yeah, turn it over to chat GPT and we could all go home 

Mark: Soon enough.

Yeah. So do you have like a baseline tech stack or like set of tools that you recommend and how do they all play with each other in terms of one of the things that is always on the top of our customers minds?

Is there can sometimes be a discrepancy between what you see in GA4 and what you see in Shopify and what you see on Facebook.

What's that tool stack that you recommend and then what do you like to look at as the single source of truth? 

Kevin: Sure. Yeah. So great. Great questions. You know, the tool stack that you're going to look at. Obviously, you've got Shopify, right? 

It's important to think a little bit about your Shopify set up too, if you have different apps, that's going to affect really how well these other tools that we're about to talk about play with your Shopify store. 

So the big ones are upsells is super common. It's just the way Shopify implements upsells. We can talk specifically about this, but it can wreak havoc on some of these other tools. 

And then subscriptions these tools so the big tools that everybody, basically everybody's going to use to some extent or another Shopify, like we said, right?

Google Analytics, for now people are definitely still getting used to it. But Google Analytics, broadly Google Analytics 4 specifically. And then the other ones are your advertising platforms. So that's Facebook, Google ads for basically everybody. Klaviyo as the super common email one, obviously, right?

But really, wherever you're sending information to to an advertising platform where you're going to be spending money on those ad platforms. 

I always like to draw a distinction. between the different uses between why are we using these different tools, right? 

Google Analytics is an analytics platform. The only reason to implement Google Analytics on your site or to use it is to ask the sort of questions we've been talking about. What's my conversion rate? What? How many orders did I have last week? Stuff like that. Just analytics questions. That's the reason to send it there. 

We really send data to Facebook and Google ads and whatever else for different reasons. We send those so that way Google ads and Facebook can optimize our ad spend on their platform with as accurate data as possible. 

So we want to give those platforms. We always want to get as accurate data as possible across all of our platforms. That's sort of it. Obvious, I think. 

Mark: Right. 

Kevin: Right. But the intent is different in Google ads.

If we're off by if we're consistently off by 20 percent on measuring something and it's consistent, we could still use Google Analytics to, you know, analyze trends. That's what we're going to do. Right. 

In Facebook, if we're consistently missing all of our subscription revenue or all of our upsells, then Facebook is not able to optimize our ads in their big machine learning algorithms that has nothing to do with anything we're doing in Facebook.

Is Facebook itself is not able to optimize our ad spend and who it's showing ads because it's missing a big segment of our our customer base. Right? 

So if you're missing Kevin Facebook is not going to optimize for everybody that looks like Kevin went in its ads. So it's really about getting accurate purchase information into Facebook. 

I know some people get a little frustrated with the difference between the attribution models between the platforms. That's something that comes up a lot, which is like Facebook's taking credit for, you know, 1000 purchases and Google Analytics says they only converted, you know, 300 of them or whatever.

Right. And like, while I think that You know, that example would be big, and we want to investigate that. Really it's about getting accurate data into Facebook. Accurate and complete data. 

So those machine learning algorithms can go do what they do. We're not going to use Facebook as an analysis tool like we use Google Analytics. 

In terms of some of the common issues with tracking across platforms. Yeah. I would just say it's never going to be perfect. The internet is a very complicated place. So I think a lot of people have this intuition back from maybe the early 2010s that you just kind of take a snippet, drop it on your website and you'd be done right.

And that was nice back when that happened, but the internet is just a much more complicated place. That's really what's kind of driving the discrepancies between all of these different platforms and the like big increase in a complexity of implementing, just implementing these tools has gotten a lot more complex.

You know, obviously we have smartphones today. So we've got problems with device tracking. We have iOS. 14, which officially is called app transparent app, transparency, tracking wreaking havoc with Facebook's ability to track conversions. Although it's gotten a lot better recently,. We have server side tracking people have heard of, of course, GDPR.

Some of the California regulations, right? And then just single page apps is another thing. 

Some Shopify stores are headless, and that can cause problems with Google Analytics and things like that. So the internet is just a much more complicated place than it used to be. 

And that is why implementing this tracking is getting so much harder.

So if that's happening to you, just know like you're not alone and it's like not your fault. It's just like this is getting more complicated in terms of some of the things that are specific to Shopify for discrepancies. What I always say is session numbers are going to be different across platforms.

The trends are what we care about. If the trends were pointing in the same direction on sessions, then We often say like, Hey, you know, better is the enemy of good enough here. Are we able to use the data to make decisions and answer our questions? 

Then that's what we need. It's not going, you're not going to see one to one from Shopify to Google Analytics on sessions.

It's never going to happen. Right. 

They have different sessionization models for one thing. But where I always start with is are the sessions trending? 

Are they trending the same way? And are they not? It shouldn't be an order of magnitude different, right? We shouldn't see 100 in Google Analytics 1000 in Shopify.

That's a problem, obviously. And then the other thing I always go like to go look at is the purchases. And you brought up source of truth there. So I always like to point out. The way we think about it is there is no one source of truth for the reasons I just described. We're going to have discrepancies across platforms and you're never going to get your history to be correct for sure.

Even if we got perfect tracking implemented tomorrow, it will only be implemented from that day forward. Right.

So if we want to ask questions about like, how was the difference between Black Friday performances? Black Friday is coming up. We want to compare this Black Friday to last Black Friday. Most people didn't have Google Analytics 4 set up last Black Friday.

And if they did, lots of them wasn't working maybe as well as it should have been. Right? 

 So in that case, we need to go to Shopify as the source of truth in this, for this question. And Shopify is the source of truth for your orders data. If an order did not happen in Shopify, it didn't happen.

Right? Whereas if it didn't, if it's not in Google Analytics or Facebook or whatever, well, yeah, there's lots of reasons.

  In terms of sessions. Yeah, it's just one of those things. Unfortunately, there's no source of truth for what your sessions is. Sessions is a matter of an opinion. What a session is.

So yeah, but we do you know, we try to look at Google Analytics 4 compare it to Shopify. That gives us a sense of what our sessions is. And as long as we're able to make decisions with that, we often will say it's good enough. That is true for much of the middle of the funnel. So sessions, page views, product views there's no metric in Shopify for that.

So you often just have to go look at Google Analytics 4 and add to cart and then begin checkout. 

Those are the big ones no source of truth, unfortunately for those. But yeah, we do the best we can. If Google Analytics 4 is matching Shopify on purchases to within 10%. Excluding things that we don't expect, like subscriptions, right?

A recurring subscription didn't take place on the website. We usually don't expect that to show up in Google Analytics 4 if that's the case, then we often say that's good enough and we can use it to make decisions. So it's a long answer to your question there, but there's a lot to unpack.

Mark: No, yeah, there is. So it's safe to say that. You can kind of decide the source of truth based on the metric, purchase Shopify, page views or sessions. 

Kevin: Google Analytics 4 gives you, gives you the trend. Another great example would be ad spend. Okay.

Facebook is the source of truth for your Facebook ad spend, right?

Klaviyo is the source of truth for how many emails you sent, right? 

Stuff like that. And what we do what you can do when you're Yeah,

I would say that this is like advanced techniques. So we're not going to too much what you will end up doing when you want to create a data set that is combining all of your sources of truth is you'll send all this data from Klaviyo, Shopify.

Google Analytics, Facebook into a data warehouse, and you'll do custom analysis on it, right? So, in that sense, your data warehouse serves as your source of truth for you know for your data, because it's pulling from the true sources of truth. 

So that's another technique you can use although, like I said you know, not, not justified until you know, we're really not able to answer the questions we need to answer in Google Analytics or the platforms individually. 

Mark: Yeah. So speaking of one of these sources of truths you alluded to some potential frustration with it earlier, but Google Analytics 4,

I heard some rumblings why is it so terrible? Or I guess I should be more less, less of a, of a layup and, or just say, you know, why is it causing so much frustration?

Kevin: Sure. Yeah. There's a lot of reasons, right? Some, I would say just like right off the top. Universal analytics had been around for I think it was 12 years, but it might have been 15 years by the time

Mark: People knew it 

Kevin: People knew it, people knew it and they were comfortable with it. Not only that, it was a very, if anybody's ever built a software product, you know, the first release always has issues, right?

And I would say, the Google Analytics 4 it did get a lot better over the past year. But it is still missing things that are just obvious, especially to an e commerce store. 

Mark: Yeah, conversion rate. 

Kevin: Conversion, right? It's just ecommerce conversion rate is not in Google Analytics 4 reporting interface.

You're not crazy. It's not there, right? That's nuts, right? 

That's just like totally crazy from an e commerce store. And so it just has problems. It has problems in the reporting interface. It has tracking problems. Lots of people, regardless of how they implement Google Analytics 4 if they use Shopify's built in integration, if they use our integration, if they implement it with Google Tag Manager, they will get a lot of just missing attribution for orders.

So the order will just say not set, doesn't know where it came from. Looks like it came out of nowhere. We don't know, and Google apparently doesn't know why that is happening at a higher rate than universal analytics.

So those are right off the top, those are just some of the common frustrations.

The other stuff is, I think people are not used to the reporting interface. 

So part of that is just getting in there and playing with it and getting more comfortable. I do actually think Google Analytics 4 is reporting a custom reporting interface where you build your custom explorations is what they're called in GA4.

That is much more powerful than Universal Analytics was. So Universal Analytics. Did have some custom reports was very limited but they had lots of built in reports that people were used to using. It's kind of reversed with GA4. The built in reporting is basically useless especially for e commerce stores.

It's just like, they just didn't think about it or I don't know that it's just, you could throw it out, basically and I always go and just build custom explorations in GA4. So I would say if you're trying to get over the hump with GA4, that's a big one. Just get used to building explorations.

Don't even try to get it out of the reporting interface, right? And the other big thing with GA4 is the underlying data model changed with GA4. 

So universal analytics was what we would call a session based data model. And all that means is that if you went and you looked like actually under the hood at the actual rows in the database that Google keeps, you would see every row in the database was a session, and so that means like things like the e commerce stuff and all that stuff was kind of tacked on to that because it came from the late, it was like 2008 or something when it came out. It was a long time ago in Internet time. 

GA4 is an event based model. So every row in the database is an event. Every modern analytics platform that has been built since universal analytics has been an event based model. And so that was definitely the right decision technically. But it does add some confusion to reporting in particular, like you don't have session based metrics anymore.

You can still, they're kind of piece together but there are some advantages to that. One of them is that with GA4 you can export for free the data to a big query instance. And what's cool about that is you can get the raw data. Right? 

So you can get the absolute like row level. Like I was saying, every event that firing on your site, the session IDs, the client IDs, everything that was like super hard to get out of universal analytics. You had to pay like 100, 000 a year for universal analytics 360 to get that into big query. 

It's free now. I definitely recommend that basically everybody set that up because it doesn't backfill.

So even if you don't use it today, you might hire an analyst like me or somebody else six months from now. And if you set that up today they will be able to go and look at that raw data and answer those questions like conversion rate.

Well, you can answer the question with the raw data for sure and you can combine it with your Shopify data better with the raw data. 

So setting that up today is. Really behooves people because like I said, it doesn't backfill. So it's not going to go back and fill your data from the past six months. But if you set it up today, you'll have that raw data forever going forward.

Mark: And that's free to do. 

Kevin: The connection is free. You do have to pay for the big query costs. Which are very inexpensive for all, but like a truly massive e commerce store it's just not that expensive to store data. So, you know, we're talking about for most stores, I mean, I have.

A really big e commerce store doing it. I think it costs them like 10 bucks a month or something like that, right? So yeah, we're really talking about it. A pretty insignificant cost for most users of that. 

Mark: That's really interesting. 

The other day I was talking with someone and I was like, let's just build a report in Google data studio.

And they were like, what's that? And I Googled it and I couldn't find it. And I was like, Oh my God, I can't believe I'm crazy. I just invented this thing. But it turns out it was rebranded to Looker studio.

Is that a tool that you recommend for e com stores? Like, is that something that you use? 

Kevin: Yeah, definitely.

We use Looker Studio for dashboards. It's a dashboarding tool. And it's pretty good. It's free. That's what's great about it. It's free. 

You can get connectors to it that cost money. So you can get Shopify data into it and things like that. And you know, if you're handy with it, you can do some pretty sophisticated stuff with it.

But it is great for dashboarding. And I always draw this distinction. A lot of the questions that we've been talking about. Why did my conversion rate go down? The sort of why questions, those are analysis questions. Those are things that we're going to start with a question. We're going to like dig into it.

We're going to play with the data dashboards. I think a lot of times people want their dashboards to be able to do that. But dashboards are static, right? 

They're, they're dead, right? Like they're static. They're this thing.

So they're really useful for two things in particular. 

There's some metrics that we just feel like we should know. . We should always go on Friday. I always go and I look and I say, What was the conversion rate this last week? 

Give me the conversion rate by Facebook campaign or whatever. How much revenue did we bring in? How many orders did we have? And for those kind of like spot checks. 

Yes, put those in a data studio dashboard, build that dashboard so that way you're not going in and wasting 20 minutes and Google Analytics being like now, where did I click to find that data right?

Put that in a dashboard. So it's front and center and then you can send it to your boss anytime they ask a question about it. 

You can be like, here it is. You can look at it anytime, right? So definitely it's useful for that. And then it's also useful for kind of spot monitoring things. So if you're just going to look at the data, and go, okay, you know, orders didn't go to zero yesterday. So like our tracking isn't broken. Right. 

That would be like another use case of that. So I definitely recommend it for like your basic dashboards and stuff like that. But you know, just know it's limitations, like any tool. 

It's not an analysis tool where you're going to be able to ask like really in depth questions about it.

Mark: Great for dashboards though. 

You know, the space that you're an expert in is ever evolving, you know, we've got the GA4 transition. Another area that is evolving quite quickly is just the overall landscape of data collection. A lot of privacy concerns obviously iOS 14 and everything that's going on there.

How is that evolving and like, how is that making data collection more complex, you know, obviously it's become harder, but you know, what's really complex about It? 

Kevin: Yeah, great question. So, you know, with stuff like GDPR is probably what most people think of right off the bat with privacy and then probably iOS 14 after that.

IOS 14 theoretically doesn't impact your ability to do analysis too much with GA4 data.

You can still, because most Shopify stores don't have their own app. It was really a chokehold on like games and stuff like that. Their, their ability to track stuff like almost just like went to zero for a while. It did affect Facebook's ability to track conversions on your site.

So like very specifically what happens with ATT, with App Transparency Tracking. Facebook is an app when an ad is shown on the Instagram app Facebook, it's their app. They know that I, Kevin, cause they have my email address, saw your ads for your jeans, right? If I click on that, Facebook definitely knows I clicked that app, right?

Then what happens is what pops open for most people is going to be a Safari browser, unless they change it to Chrome, right? And so now it's been transitioned over to Apple. Right. We made a transition from Facebook's domain where they're allowed to do whatever they want over to Apple, where now Apple Safari is like, Hey, Facebook is third party data on your Shopify store.

It doesn't matter if you want to give Facebook the data we're telling you, you can't tell Facebook, cannot know what Kevin, they know. I clicked the ad. They don't know what I did on your Shopify store. So they don't know if I converted. There are exceptions to that, if I allow app tracking Facebook does get to know, their server side tracking where Facebook is not allowed to know that Kevin made a purchase, but they're allowed to know that like 50 purchases were made in this window.

And that seems to change based on how Tim Cook's feeling in the morning. And all that stuff. So Facebook, being a very sophisticated company, seems to have gotten over the hump on that issue where they are. It's not as good as it used to be, but they are able to reasonably track the conversion rate and return on ad spend to the ads that they're showing, which means they're able to provide you a better return on ad spend and target customers better because they know that kind of Kevin, you know, bought those jeans, right?

Like they don't know, know, but they like kind of know, right? Yeah. Glossing over lots of stuff that I'm sure Facebook is doing very complicated things to figure that out. 

So that was one thing, the GDPR stuff just makes tracking ,you know, I'm not a lawyer but I've talked to several of them.

They all of course have a different opinion on what we're allowed to do here. Right. Google, I'm not sure is a hundred percent clear on what we're allowed to do here, but the basics are in GDPR and these privacy regulations seem to be rolling out not just in the European Union anymore, but like in lots of different places it does seem like for Google Analytics 4 basically Google Analytics 4 still relies on a cookie.

It relies on a first party cookie, not a third party cookie, but it relies on a first party cookie. And basically until the user opts in. For GDPR on the cookie banner, if they don't opt in you're not allowed to store the cookie. The big problem there is most of the time what we care about is attribution.

So if there's like a cookie banner at the bottom and the user lands on the landing page, the only opportunity we have to know on the Shopify store where that user came from, did they come from a Facebook ad or did they come from Google ad or whatever, right? 

The only opportunity is if we capture a page view on that landing page, if we missed the landing page.

They're gonna look like direct traffic and we're not gonna know where they came from. 

Mark: Meaning if they advance from the landing page, if they moved.

Kevin: Yeah, exactly right. Because the UTM parameters that everybody know about the little thing in the URL or the document refer, which is buried in the HTTP request, but that's the other thing that's only available on the landing page.

If they go to the second page and we're able to track them on the second page, it looks like direct traffic. So when the banner is at the bottom and the user is clicking around at the website and then they click accept three page views in, we're not going to know where they came from. The banner is right up front and they clicked accept, depends how your tracking is implemented you have to fire another page view when they click accept and then, you know, you'll be able to track attribution better.

Mark: Does that double your views? 


Kevin: No, because you didn't, you weren't allowed to fire, 

Mark: You didn't fire. 

Kevin: Yeah, you weren't allowed to fire a page view when, before they accepted the cookie banner. Right? So all that's to say, like if you're in a GDPR country you're just going to deal with missing you're going to, you're, you're going to be missing lots of attribution.

Yeah, and and probably lots of orders and stuff like that as well, you're going to be missing those. So if you're going into Google Analytics 4 and you don't see any purchases and you're using Shopify's native integration if you go and you look at that and you see a bunch of missing orders.

Well, if someone like me would tell you, you know, like there might not be a lot we can do there. 

What we want to do there to answer those data questions is what you are allowed to have is first party data and so far as everything I've read on this is your Shopify data is your first party data. Still, that is you own it. You can. You have the right to know the customer's email. I mean, you have to be able to send them emails and things like that. 

So that's your data. You can use that to analyze things to your heart's content. And so that's that's usually what we try to make up the difference. But often Google Analytics 4 is You know, is, is gonna be limited in what we're able to track.

Mark: Got it. 

I wanna end here with, either I'll let you choose. Going over some common analytics mistakes so that folks may recognize them and, and, and get some value outta that. Or like a top three, like tips or something that folks who are watching can, can go and put into action right away.

Whichever you think would be most valuable. I'm gonna 

Kevin: Yeah, well, maybe we'll mix and match a little bit here is I would say the biggest mistake .

The biggest mistake that people will make is they'll get obsessed with the data not being perfect. So they'll go like I had 41 orders in Shopify and I had 40 in Google Analytics, right?

And it's like 40 is plenty good to use it to make decisions like don't sweat it. Right. 

We're looking for trends here, so it's just very easy to get obsessed with getting perfect data across your platforms. That's not the goal. 

We want to analyze and make decisions. Focus on the prize, which is being able to answer questions, right?

So that's the biggest mistake. 

The biggest thing that I think people can go put into action today is I would say go take a look and get a sense for what your baseline is. Get a sense like there are numbers like we talked about with dashboarding. I think you should know. You should know how many orders you have roughly.

What's your daily orders, right? How many orders did you have? How many orders do you have a week? You should be able to answer that question because you want to know when it changes, right? We want to know quickly when it changes. You want to have a sense for how many sessions you have and things like that.

You want to know that. So go get a baseline. The best way to do that is go look at Shopify for orders to get the real order and revenue counts. Do that, know those numbers, feel comfortable with what those are and how they change. Look at them over time. That's the easiest chart to look at is just go pull it up.

You don't need any advanced tooling to start. Pull it up in Shopify, look at it, get a sense for on weekends, do orders go up and down on the weekend? 

It's going to depend on your store. You're selling office supplies to probably go down if you're selling. I don't know, like an impulse purchase, right?

Maybe they go up on the weekend. Right. So. Get a sense for that and look at things over time, see how they're changing. Get a sense for those trends. That's true of basically every metric. So just look at how it's been changing over time. I think you will find some interesting insights right off the bat.

I always do. I just go look at the last three months of data for a store and I just like, like, oh, we had this big spike. So. 

As a like concrete example, we went and we looked at a Shopify store and we saw this like big spike on this, like last week, just like in this one day, like what the heck happened there and that you start digging into it.

You'll be like, there'll be a little itch in the back of your brain. That's like, why did that happen? Right? 

So go and we looked at it and their newsletter. They sent out their newsletter that day. So there's this big spike. So then the next question is, does that happen every time they send out a newsletter?

So we went and we looked. No, this newsletter was way more effective than their last new newsletter. So you will get an insight like that in the first 10 minutes of messing around with your explorations. Just look at it over time. Just look at orders and sessions over time nine times out of 10, you'll find something interesting right off the bat and you'll dig into it and then you'll be a data analyst from there.

You'll, you'll be addicted to it. 

Mark: There you go. Easiest step to become a data analyst. Just look at the data. 

Kevin: Look at the data. That's where we start. 

Mark: Awesome. 

Cool. Well, Kevin, thank you so much for sharing all that. I learned a ton really, really valuable and really appreciate you coming in and spending some time with us and sharing the do's and don'ts of data analysis and analytics in the ecom space.

So hopefully folks can take all this information and turn those analytics into dollars and into profits. 

Kevin: That's right. That's the goal. Thanks so much for having me. Really appreciate it. 

Mark: Yeah, take care. Thanks everybody. 

Kevin: Thank you. Bye.

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