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- [00:00]
A
This is the kind of thing that most businesses have never really looked at. A site like Amazon, they're doing it. Shouldn't you be doing it as a business?
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B
Next presentation will be going through all sorts of different visual ideas based on data that is going to translate into better conversion rates. But now getting into more of the meat of it. These are some of the most important metrics that you're ever going to want to look at. You're listening to Perpetual Traffic.
- [00:26]
A
Hey, quick heads up. If you're marketing to marketers, this is where you want to be. Sponsor Perpetual Traffic and get seen and heard by thousands of seasoned marketers, CMOs and agency owners. We get hundreds of thousands in downloads every single month, all to marketers. So go to perpetual traffic.com to apply for a spot on the show in Q1 or Q2 of 2026. Hello and welcome to the Perpetual Traffic Podcast. This is your host Ralph burns, founder and CEO of Tier 11. Very excited to have back for episode number two. I don't know why we waited so damn long to have him on, but Ned McPherson who runs all the CRO here at tier 11 and if you haven't listened to or watched the first episode, we will leave that link in the show notes here. You definitely check that out where we talk about the philosophy of CRO in 2026 and what makes what we do together really, really different. And just as a quick recap, a lot of people think CRO is sort of this designed based methodology of changing the color of the button and all of that. Yes, that has something to do with it. But today we're going to get into a little bit of a sneak peek, open up the kimono a bit as to how Ned thinks about all of this and we're actually going to do a screen share of model and how we utilize data to figure out one of my favorite expressions that you ever say, Ned, is the metric on fire and figure out how that affects businesses. So really excited to get into today's show. So anything you want to add before we get into the screen share? Obviously we're pretty damn excited to have you on of show two of a three part series here on what makes your CRO methodology so different than everything else that's out there.
- [02:13]
B
Yeah, no, thanks for the intro. The the only thing I'd add is that a lot of folks will ask me, they'll be like metric on fire. Is that like a good thing? Like on fire and a good or is that bad? And like, just to clarify it's it's not good. It means like the metric that is literally on fire that's holding your business back. Not to say that every brand has only one metric. Usually it's a confluence of issues, but more often than not, we'll see a brand has one particular like interfrontal metric point that if we were to solve that, improve that dramatically, it has a disproportionate impact on the rest of the conversion rate for the site, the average revenue per user, et cetera. So that, that's generally what we mean by. By metric on fire. Yeah, metric on fire.
- [02:48]
A
So not the fire emoji, but the fire as in like the like, oh
- [02:51]
B
my God, 911 emoji.
- [02:52]
A
Yeah, right, right. Related to that, you were pretty much on fire in the emoji sense. A weekend or two mentalness. Ned is actually, I don't know how you describe it, but you were at. I'm sort of an F1 fanatic because of the Netflix series. I'm a recent, you know, disciple of that sport. But you actually are a. Like, what exactly do you do? Like, I know from what we're talking about pre record, it's unbelievable the stuff that you're doing right now. Yeah, the F1 was in Miami. And you actually raced at F1 Miami just a few weeks back.
- [03:30]
B
Yeah, yeah, I did. In what's called the support series. And so, you know, very similar to, you know, say you would go to see a band play, some big name band, they'll have support bands that'll come on, you know, before it kind of warms the crowd up. You see this in like comedy shows. Do you see it in a lot of entertainment based things? So it's no different in motorsports. Right. So when you have like the flagship event, like a Formula one race, you'll typically have what are called support series, which could be like Porsche Carrera cup, you know, Sprint Cup. In my case, I race for McLaren. So this is called McLaren Trophy North America and it is a multi stage series where we compete at different well known tracks. And in this case, yeah, we were at Miami Formula One. So quite literally like on Saturday, Formula One had their sprint race. They came off 30 minutes later, I'm on track, right. So same crowd, same day, same track. Pretty wild experience. Right. To see that many people in the stands and we could probably show some pictures or what have you. But yeah, so that's how I get my adrenaline rush out. Is going about 180 miles an hour around a racetrack.
- [04:25]
A
Pretty amazing. And how many people were there on that Saturday Like I think they said
- [04:29]
B
attendance, the, the event attendance I think was 275,000. There's not sure exactly how many were in the stands the moment, but I mean it's, it's a pretty visceral experience. You know, you're coming around turn 10 at 160 and boom, this just like stadium of people appears in front of you and you, it actually distracts you at first because you got to stay focused on your lines and your breakpoint and all that and all you just see the sea of people with their phone out recording you. It was, it was quite an experience. Sounded, you know, signed a lot of autographs, had a lot of fans up, which was a lot of fun. So, yeah, so crazy.
- [05:00]
A
Anyway, we'll leave, we'll leave some images or some links to, to what you were doing, show notes just for people because this sort of stuff is incredible. I mean you and I are sort of car guys just to begin with, but I mean you've taken this to the whole next level, which is absolutely insane. And this is in your backyard because you live in Miami. But are you going to go around? Try like you're not going to go
- [05:21]
B
to like Dubai and like. Yeah. Oh yeah. You're racing a bit in Europe this summer actually. Yeah. So it's funny you mentioned Dubai. The actual. I believe it's next year, one of the world championships for series I'm in will be in Jeddah. And so if I make that a qualify for it, we'll be shipping the car and the team over there. And it's actually analogous to what we're going to be talking about today because a lot of the keys to, to getting faster on the track is measuring telemetry, right. Understanding data. So you basically compare things like your, your break point, your steering angle, your acceleration points, your threshold of acceleration, all these var. There's potentially thousands of variables that you compare up against a different driver like your coach or someone like that, or even a hypothetical ghost car, all in the name of trying to figure out where do you optimize to improve time. It's actually very similar to what we're talking about today, which is in this mess of data, what do you, what are the core one to two, three things that if you focus on are going to have a disproportionate impact on your business's conversion rate, revenue production, etc. The other interesting thing that's actually quite analogous is when you're on the racetrack, the variables are changing sometimes by the minute. Revolution, humidity, tire degradation, brake Degradation, traffic around you, all these things change. So you actually have to change how you operate a little bit. And it's similar in digital because a lot of their conversion rate, they'll get it dialed in and then what happens? You say, well, we want to grow, so let's open the proverbial top of funnel. Let's go find some more exploratory traffic. Let's get into TikTok. And all of a sudden you drive a huge new amount of traffic in which, oh, doesn't convert very well. So now not only does it bring your overall conversion rate down, that oftentimes sends a signal back to ad engines or other things that you no longer convert at that rate. So the variables have changed. Exogenous variables have come into the equation. So what do you do? You go back to the drawing board, right, and you, you iterate, you test different things, you experiment and you understand how to optimize for, for that current state that you're in. So, yeah, actually a lot of, A lot of honestly analogies when you data in a, in a race car and also data with your business trying to optimize for, for conversion rate.
- [07:23]
A
Yeah, there's a lot of similarities. And then you mix in a healthy dose of adrenaline into that.
- [07:27]
B
Oh, yeah, yeah. A little more intense in the car,
- [07:30]
A
but yeah, a little bit more intense. Yeah. There's the life or death situation going on there in CRO. It's not necessarily the case, but you know, life or death of a business to a certain degree or, you know, acceleration of a business, I don't think people realize that in F1 there's hundreds, literally hundreds, maybe even multiples of hundreds of people that are actually analyzing data and assisting with the driver. Like the driver is just the figurehead. There's all the people that are behind the scenes and a lot of it's driven by all this data analytics stuff, which we're going to be talking about here today in a very different way, but the same kinds of things, like if you increase one quotient, like just a fraction of a second and another one, and another one, another one, all of a sudden you have an advantage over the competition, which is a lot. There's a lot of similarities there with what they're talking about here today.
- [08:15]
B
No, you're absolutely right. Yeah. I mean, literally thousands of people work on those organizations, so the drivers tend to get all the credit. But the car is, you know, I mean, a famous line said is you could take the best driver on a grid, put them in the worst car they're not going to win, period. You could take the worst driver on the grid, put them in the best car and they'll probably win. So it's like it's a team sport. So to your point, like a lot of these brands, folks listening to this, you likely have departments. You know, you've got the life cycle department, you have those focused on search optimization, those focus on paid media acquisition, web maintenance, web conversion optimization, creative influencer affiliates to the degree of which there is a synergistic effort across all of those. Right. Where like the CRO team, be it maybe an agency or internal, is sharing learnings with the affiliate team, the influencer team, it could spark ideation to better perform there. So I mean you hit the nail on the head like if the team confides this empirical backed methodology on how to scale the brand together, you're generally going to see outsized growth and success with your business.
- [09:13]
A
Yeah, well let's get into how you actually do it here. So we're going to screen share just to tee this whole thing up over on Perpetual Traffic Forward/YouTube. Check out our YouTube channel. You click on that link, we'll leave that in the show notes and Ned's going to go through. Just an example of the initial audit this is and this is typically done by one tool. I know you guys use a tremendous amount of other tools but this is sort of pre. So if you do want this service from us over@tier11.com CRO this is the type of audit that you'll get. And I think this is the kind of thing that most businesses have never really looked at in this depth even at the initial stages. And we're actually working with your team right now to increase the CRO efficiency and the conversion rate of tier 11. And it's amazing to me being the client, air quoting how deep you all go.
- [10:16]
B
And this is just the first part
- [10:17]
A
of it, which is deep enough. I remember our first conversation with the members of the CRO team and it was just amazing. All the different ways in which I was like, man, I know never knew all of these things and I've been doing this for 20 years now, so. So yeah, so this is obviously a screen share. We're going to be going through this here today talking about this analytics model and how it really does differ from the sort of design centric model which is the way the sort of traditional CRO is done. So check us out over at. So check this out over@petpetual traffic.com forward/YouTube. So take it away then.
- [10:55]
B
Yeah. And one thing I want to just preface is what the slides we're going to be running through today are the, the first half of the the audit style presentation. Right. So if you listen to the first podcast review that we had, you know that that came out, we talked a lot about and you references today not following a design centric approach. To reiterate the way most people approach CRO individuals, internal teams, even agencies unfortunately is they'll take a look at your brand site, they'll look at competitors, look at brands that are maybe not competitive, but are tangential. Right. They're adjacent to what you do and they'll say, you know, look at how they did their sidecar, look at how they did their navigation or look at how they built their hero section. That looks nice, let's try that. And that is a dangerous approach because that is a design centric impetus behind the ideation where for all you know those things that you're replicating are the weakest performers they have on their site. Right. So you've got to come at this from an empirical lens. So we're going to be walking through today is some of the data slides, you go even deeper than this even on the initial complimentary audit. But some of the data slides that start to uncover clues, so to speak, that start to point us in the direction of where we need to focus to get out size success again. One thing I'll reiterate is the success that we've had as a CRO organization over the years is really predicated not on us coming up with these like crazy zero day ideas that no one's thought of. Yes, we have some good ideation, absolutely. But the real genesis of it is, is focusing at the right section of the website to the right audience at the right time. Like mobile users, top DMA cart to checkout rate. And we say that point in the user journey is the weak point. That's where we need to focus and we just run experimentation there. We move the needle. That's the elixir to success that we've had, so to speak. So we're going to walk through some data slides today. I do want to preface on our next presentation we'll be walking through some of the visualization, so kind of pique your curiosity on that one. In the next presentation I'll be going through all sorts of different visual ideas based on data that is going to translate into better conversion rates. But nonetheless, let's kick off here starting with always looking at super high level where's traffic Coming from, what does it do when it first lands on the site? Most particularly, what are new users doing? So there's a couple metrics here I'm just going to harp on again. We're going to do just a little snapshot that give me a good understanding of base case. How good is the brand doing at its first job, which is getting somebody not to leave right away? And that may sound basic, but the reality is most brands experience enormous abandonment rates and exit rates on landing from new users. Take a look at this brand as an example. Their initial engagement rate here is just a smidge over 50%. So what that means is nearly 50% of their traffic does not engage. From a new user perspective, not engaging means they don't click, they don't scroll, they don't navigate and they leave within 10 seconds. That's basically someone who ends on your site, maybe reads or maybe just looks and says, no, this isn't for me that's a problem. So the next thing we have to look at is, well, what is the landing event? Let's assume it's homepage. The above fold section is going to need disproportionate effort in this case with this brand. I know right out of the gates because of the poor engagement rate. Generally speaking, we want to see most brands at at least 60% or higher here. Ideally, if your brand's in the 70s, you're doing really well. Anything high 70s or even low 80s is world class. Like exceptionally good position of your traffic. Keep that in mind too. Right, so if you have a brand that sells products to dog owners, right? And you drive a bunch of cat owner traffic to the site, that's going to be a problem. Most of them are likely going to abandon. The site isn't structured well because it's just the wrong audience. So it's not always the fault of the site. It can be traffic related. But nonetheless, this is a clue. Totally down to understand what is potentially going on here.
- [14:33]
A
Hey, quick heads up. If you're marketing to marketers, this is where you want to be. Sponsor perpetual traffic and get seen and heard by thousands of seasoned marketers, CMOs and agency owners. We get hundreds of thousands in downloads every single month, all to marketers. So go to perpetualtraffic.com to apply for a spot on the show in Q1 or Q2 of 2026.
- [14:57]
B
Another metric that's important to look at is sessions per new user. So this is a fun metric because you don't want this too low, nor do you want it too High. If you have this at around 1, that basically means the typical new user is going to come to the website and they're one and done. They're going to look at the site and they never come back, right. Alternatively, if that was like a three, that means new users on average come to the website a bunch of times before they actually do a thing, right? They actually fill out a form, they actually add to cart, they actually complete a transaction, which is equally problematic why they need so many visits to the site before they did that. As a general rule of thumb, we like to see brands hovering in the like 1.4 to 1.6 ish range. So call it 1.5 on average, this brand's doing excellent. This is a really solid session per new user metric. It's not quite too low, not quite too high. In my experience that's kind of dead down the middle of the fairway. Another key thing to look at is engagement time. So over here, this brand with a 50.87 engagement rate, solid sessions per new user, decent growth year over year and session and user count, 55 seconds, it's pretty low. 55 seconds goes by awfully fast. And keep in mind for those that complete utilitarian sides of the site, like adding to cart, reviewing cart, entering information on a checkout, all of that contributes to this. So the reality is, is when we look at engagement time, we're often associating that with browsing time, which it's not true. Browsing time is going to be less than this. So if you have a 55 second total average, that means your browsing time is somewhere probably in the 40s. 40 seconds goes by real fast. Now if you're a single SKU product with a very clear call to action and a very clear unique selling proposition, hey, we are a product for this person to solve that problem at this price point. And it's really easy to understand in rare circumstance it's acceptable to have a low engagement time. But if you've got multiple products, especially like maybe a fashion brand or any brand that has like an aesthetic aspect to it, if you've got a brand with multiple products, right? Or lines of products, and that's a dead giveaway as to like basically shallow scroll depth, people are not browsing the site, they're certainly not spending much time on any one page, they're certainly not reading very much. So again some telltale metrics there, that should kind of be a red flag so to speak as to typical top of funnel you new user behavior. Now getting into More of the meat of it. These are some of the most important metrics that you're ever going to want to look at, which are intra funnel behaviors. So in this case you've got a mobile and you've got a desktop that's broken out here. This is a very basic funnel. We go quite a bit more in depth on this. But what we're trying to identify is of the website chunked down into its incremental parts, which of those parts is disproportionately lower or outperforming compared to one E. Comm in general, two brands in your industry and maybe three brands that are competitive that we have unique access to. So as a general rule of thumb. Right, general rule of thumb for typical average order value products as an example. So we're talking $60, $80, maybe low 100. Right. Average order value products that are in the direct to consumer space, maybe cpg, maybe accessory things of that nature. Add to cart rates are generally speaking going to range between 12 and 18. Fashion as an example, to give you an example why it might change, will index higher, especially female fashion, you'll typically see add to cart rates 15 to 26%. And part of the reason for that is a lot of shoppers on fashion sites will use the add to cart button as a wish list and they'll do that even if there is a wish list feature. We've noticed by the way. So you've got to always measure yourself up against what is normal in your industry for your average order value. If you've got an average order value that's like $700, you're not going to have a 15% add to cart rate. You're going to be more in the like 3 to 7% rate. But nonetheless, we're always trying to baseline you to see where are you at. So starting with this, let's talk about mobile first. So view item. What this is basically telling us is of the traffic that landed on the website, who engaged what percentage of it actually saw a product view. Now if the majority of your traffic lands on like a bestseller collection page or maybe the homepage, this is a very telling metric as to how much progressed into a pdp. If the majority of your traffic lands on a pdp, you have the opposite problem because it's going to inflate this number. It's going to give you effectively like a false positive cue, right? Where you're going to look at that and be like oh man, we do really well at navigation. It's like you don't it's because you landed so much traffic on the actual product pages themselves. So again, there's always nuances to this, but nonetheless, this brand actually does have traffic that lands on its homepage. 74.7% is excellent. That's a really great. Oh, okay.
- [19:42]
A
I would have thought it was opposite actually. But anyway. Yeah, go ahead.
- [19:45]
B
Yeah, that is a tremendous rate right there. Right. A lot of brands are 40, 50, 60%. That is tremendous. Mid 70s. So I want to highlight what exceptional performance that is. Now note on desktop, much weaker. So immediately there's a telltale sign. Why? Why is desktop traffic we weaker there? Is it because of a landing event? Is it the composition of traffic or is it the actual view that they have in this case, I can tell you one of the things that this brand did is they had a bestseller navigation right on like the top most prominent bit of navigation on mobile. They didn't include it on desktop. That was one of the telltale signs. I know it sounds simple right away. That boosted desktop homepage navigation into product views. So we're finding clues here. That's what I'm trying to get to is interesting. Do really well on mobile, not on desktop. What's different? Well, let's compare them. Let's look at mobile, let's look at desktop. Let's get some people together. What are the differences between these two? Moving down funnel. Now we look at add to cart rate and what's super interesting here is you see some major shifts in that behavior. Right. Add to cart rate on mobile, 11.4%. That's statistically low for the brand. The industry that this brand's in. That 12 to 18% marker is absolutely where they should be. So they're not even at the lower end of that threshold. Right. So we know that the add to cart rate on mobile is a metric on fire that we need to focus on right now. Desktop does considerably better. One of the things that we found with this brand is the actual education they had on desktop was in my opinion like 3x the content they had on mobile, they had on desktop, infographics, us versus them charts, all this beautiful. Mobile was way more condensed. It looked pretty, it looked minimalist, but it didn't provide the education. And what do you know it look at the differences in the add to cart rate.
- [21:32]
A
So it's the sale, it sounds.
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B
Yeah. So you can already hypothesize. You're probably already saying in your head, well, I could probably fix that. It's like pull some of the infographics, pull Some of that richness of data, that content from desktop applied to mobile. Exactly one of the first things we recommended. So this is a good example of the idea that we came up with is pretty elementary. Move some of the assets you have from desktop into mobile. Why didn't this brand figure this out for years before we came in? Because they didn't look at it through the lens of an empirical experiment. It was design. Well, it's so minimalist, it's so beautiful looking, it's this and that. Yeah. But you're losing like 50% of your potential add to cart rate at that step. So always keep that in mind.
- [22:11]
A
So I assume this is a mobile first site, like the portion of traffic I can't quite read it on the screen.
- [22:17]
B
Yeah, I mean you could see it's considerably more traffic on mobile. Right. So again that's a point to carry. But again look at the difference there in the two. I mean dramatic like it is true, desktop generally will out convert mobile. So like for instance if this was a 13 and this was 11, I would not sit there and be like man, desktop so much better. I'd say that's a normal distribution but like double the conversion rate, not normal. So that was a clue that again led us to figuring out an idea that won.
- [22:42]
A
Right.
- [22:43]
B
Right now next, and this is why I love showing this example because I'm showing you all these interesting things from the same brand based on two device types, cart to checkout rate. So this is basically identifying of the people and then this is saying what percentage of those people. Then clicked into checkouts. You get a little sidecard view, you review your order, what percent hit the progress button into the checkout. Mobile 28.6, desktop 41.5. Both of those are low. So metric on fire once again for both device types. Now it's higher on desktop. But remember desktop typically converts better than mobile in most instances. And so in reality when you adjust for that, this would be more like a mobile 3528. So not a huge delta there. What we did in this case is we said foundationally it's not a device type problem. But foundationally, what are we missing on the actual step here? Right. What are we missing in the cart experience or what are we doing in the cart experience that's causing people to balk? And one of the things is this brand had a auto triggered shipping insurance feature. So when you got to check or to cart and you added your $90 product all of a suddenly you saw it was like 97 and it's like, why did it go to 97? You just told me on the last step it was 90 bucks. And on the cart there is this little widget that automatically added $7 in shipping insurance on both desktop and mobile. So again, what's the idea? Keep the feature default to toggle off and let them toggle it on. Right. That's not a mobile specific idea. That is a agnostic to device type idea. Which is why we were able to grow the cart's checkout rate on both of these with the same idea. So you just saw how we went through three different steps of the funnel, looked at it with totally different insight based on mobile desktop or, or basically agnostic. The device type, three completely different ideas, all of which were work success there. But the bottom line of the slide is basically to identify like, where are we weak? Where do we need to focus on? And if you'll notice one thing I do not harp on here is the check of the transaction rate. And the reason why is 70.9 and 79.7 is like really strong.
- [24:52]
A
Seemed pretty good.
- [24:53]
B
Really, really good. So good that it makes me wonder, are we priced too low? So when I see this, it's like you convert so well. It's kind of like the classic negotiation tactic where you know, you're doing a deal and you propose the price and then the seller basically says, okay, that's fine. You're like, damn, I went in too low because like they should have haggled with me a little bit. Right? Same exact idea here. When you see rates are so good, that indicates that you might be leaving money on the table. Or maybe your audience is so price inelastic that they would justify a higher price point. So I would say that this is so good, it's bad in a weird way, right? Because it's like you might be losing some margin on the table. Consider increasing prices. And again, that's a big idea. We'd have to break down more. But that's generally what I'm seeing here when I see those rates.
- [25:38]
A
Right. What I love about this is there's you're taking the data unemotional. It's not I like the data or I don't like the data. Hey, our mobile site looks better and it's cleaner than our desktop site, but that doesn't really matter. Human emotion and opinion doesn't matter when it comes to this. So you have the analytics insights. Then there's a level of experience based upon similar industries, similar clients, like 2, 3, 4,000 of these things that I'm team have done here. Like, there's a breadth of experience which is so wide and then it's a hypothesis and a theory based upon what you're seeing. And like the purchase thing, I would be like, oh, I wouldn't even touch the purchase. But you're like, well, maybe we've got an inelastic place model, which basically means, like, if we raise the price, it's not going to affect your conversion rate any because you built up so much value. There's so much like there's layers of this that you sort of uncover. Which is one of the cool things about when we were describing this, like this whole analytics model. Yeah. Because it's based upon analytics, but then it's also this level of experience, what you've seen in other industries and what you would expect based upon all these multifactors.
- [26:53]
B
Yep, you nailed it. You nailed it. And that's why, not to sound cliche, but there's really no such thing as a test that loses. It either wins and you get the lift or you get some interesting learning by. Because like when you test, you're taking what's called a null hypothesis, which is the base case on the site or the card or wherever you're testing. And then you're applying an alternative hypothesis if the test loses. It basically says your alternative hypothesis was false to some or a total degree. That's really cool. Right? So you're like, okay, so we know that's not true. Now let's try the next one. So again, I know it, it sounds a little, you know, cliche to say, but nonetheless, I mean, you really never lose a CRO. You're either getting a to of interesting learning and validator invalidating hypotheses or you're getting literal revenue lifts. It's, you know, obviously impartial, but it's a win. Win.
- [27:37]
A
Yeah, yeah.
- [27:38]
B
Very cool. So a couple other things that we look at here is just general consensus, right? So like, you know, and this is actually a different brand in this case. I wanted to show this because there was a lot of discussion around merchandising. Right. This is a women's. A women's brand. Women's fashions brand. And there's a lot of discussion around merchandising and where data can really point to it. So in this case you can start to see the like, relational understanding of items viewed to add to cart rates. Right. Now it's important to do this because a lot of times you can use a chart like this that makes traffic. Basically the agnostic variable, right, that you can remove it from the equation. Oftentimes you'll get misleading add to cart rates on like product pages because this one product page we drove, you know, 30,000 TikTok landing events to it. So it's like, oh man, the add to cart rates really low. It's like, yeah, but if you normalize for traffic, it actually might have one of the highest add to cart rates. So oftentimes we're looking and building out charts like this to basically merchandise properly. I can't tell you how many times I've worked with a brand where I'll look at their collection page and I'm like, you've got your worst sellers at the top of the collection page and your best sellers two scrolls down. If I literally just invert that, we're going to make like 5% more revenue just overnight just by doing. And it's little things like that can make a big difference. So I use this slide to basically emphasize some really simple things can get done with data to tell you things like again, the relational understanding on add to cart rate to product view rate. And this is a great X Y axis as an example within ga, by the way, to show that. Now this is another funnel, but this one, this is showing checkout to transaction rate. So there's a couple things here. One is, remember I talked about how good that checkout rate was on that former brand that I'd like the 79%. Well now we need to break down to be like, well, let's assume that number is 50%, 60, 70, whatever it is. The next question is, well, where on checkout are we losing them? Because if they land on checkout and don't even bother giving us their email, that's a very different abandonment rate than people who land on checkout, give you their email shipping info, type out all their credit card info and then they get cold feet at the very last step. Two very different scenarios there which require two very different approaches to solve it. So as a general rule of thumb, if you see a brand that's converting in the 80 or 90% range, when you right here, you generally speaking are on the. Let's just say like either the audience is a little more price inelastic or you maybe nailed the price point or for whatever reason at the bottom of funnel, people are not getting a lot of cold feet. If you're at like 40, 50, 60% here, you got the opposite problem, which is people want to buy it, they really want it, but they get to that last step and they're like, I got to think about this longer. That's too much money or that's too big of a commitment or whatever it is.
- [30:17]
A
So, hey, quick heads up. If you're marketing to marketers, this is where you want to be. Sponsor Perpetual Traffic and get seen and heard by thousands of seasoned marketers, CMOs and agency owners. We get hundreds of thousands in downloads every single month, all to marketers. So go to perpetual traffic.com to apply for a spot on the show in Q1 or Q2 of 2026.
- [30:40]
B
Maybe price justification tactics are going to be your winning formula there. Now, on the contrary, if you see a brand has really bad conversion rates at the top of Funnel, right. Only 2/3 of people even bother entering their email. Now remember, these are people who went through, reviewed your site, went through a collection page, probably went through a product page, read something, thought about it, selected their size or variant or color or whatever, added the cart, reviewed cart and then made its checkout. So for them to go, ah, no, I'm just going to walk away is an odd one. Right? That's kind of a head scratcher.
- [31:09]
A
Yeah.
- [31:10]
B
Oftentimes when we see bad rates here, it is indicative of an urgency problem. Meaning think about it for a second. If you're on your mobile device and you're browsing, you find a cool product. Browsing is fun. It's fun. There's no harm, there's no foul you can add to cart. But when you get to checkout, I know this sounds crazy, you actually have to put a little work in. You have to type out information or in your computer, you have to do it. That's just enough friction to induce procrastination. So often what it is, it's not that people don't want the product. They're just like, I'll get to this later. And it lives as a tab that collects dust on their browser. They go check emails, they go check Slack, they do this and that. So how do you engineer urgency? Well, like, here's a little tactic. We had one brand that consistently had this problem. They introduced a free gift. So if you ordered more than like 50 bucks, they threw a free gift in. So what we did is on checkout, we said, hey, that free gift, that's today only. So we engineered scarcity. Now, we made that the proverbial perception of losing the free gift was a greater motivator than the friction of entering your information checkout like that. We saw the checkout rate go up. So there's an example of an idea that could work. If you hypothetically see the very top of funnel on checkout having disproportionately low conversion rates like insights and then action.
- [32:25]
A
I mean, obviously on the, I mean the previous. All things being equal, I know this is a different client here, but all things being equal, you're focusing in on the thing that really matters the most. And on this one right here, on this client, was that the thing that you were that. Was that the first metric on fire that you identified or was it secondary? I'm just sort of more curious.
- [32:47]
B
Yeah, yeah, I believe this one was primary because if you do that just like kind of cumulative calculation, their checkout rate's pretty low. Right. I mean they lose 33% and another 37%, then another 20%. So like, you know, there was a lot of room for growth on checkout. And like this is, this is the idea that ended up working for them. Right. That, that really unlocked it. But you could see how I, the thought process of how we got to that idea that I just walked you through is the key to the whole thing. Right. That's the idea behind it. And so the idea itself isn't that crazy, it's not even that novel. But you could see how if you took a design centric approach, the chances of you getting that one right are slim. Right. You've got to take a data centric approach. Hence why I'm going so much on data today. And this is so good.
- [33:30]
A
Like I said before, I mean it's the insights plus the data and the experience all, all sort of mashed together and then sort of what you've had, what you've expected and what you've experienced in the past with similar types of clients. Obviously the industry, the price point, there's so many factors here where the traffic is coming from. You know, is it coming from Google search, is it coming from TikTok, is it coming from Meta, which is a whole other thing like intent based versus sort of interruption based, like where you're sending the traffic. Is it a collection page, is it the homepage, is it a PDP page? Like all of these factors are like entered into your decision making process when it comes to these metrics on fire. And then a decision and a test is designed around all of those factors.
- [34:15]
B
Yep, exactly right. You nailed it. Exactly. And that process is the key to the whole thing. Exactly right. As an example is sometimes brands will come, like I remember this just side story, I got invited to a like it was like a round robin tournament for CRO where they brought A bunch of CRO experts in and they just gave you a website and they'd have like two people compete who had the best ideas. And I know this sounds a little dramatic, but I opted out of the whole thing because I'm like, that's not how CRO works. Like I can't just look at a soul to look at the design because then it's just my design opinion versus someone else. Like that's why I need to see the data or else just guessing at the end of the day. So just to really put an exclamation point on, candidly how off base the industry often gets with this, you know. And so you got, again, you preach this one, I think enough, but you kind of get the point and it
- [34:58]
A
says a lot on your competitive nature too, to be able to off that.
- [35:00]
B
Yeah, yeah, true.
- [35:02]
A
There's no way I can impact this because you're looking at the wrong.
- [35:05]
B
Exactly. I was like, this is just fundamentally false, you know. Yeah, yeah. So a couple other things here. Top measurements, right. So what I'm looking for here is oftentimes not a condition conversion rate when it's a AOV win. So in this case, most brands index more towards mobile, most are going to drive more revenue from mobile, most will have slightly lower conversion rates. This is very normal to see. That's not to say that both of these metrics should not be improved. They absolutely should. But at the end of the day it's typically normal, you know, especially depending if you've got an AOV that's in the hundreds of dollars, you're going to see conversion rate a bit lower on mobile. To me, this delta is totally acceptable. Now what is not acceptable is to see the AOV like $60 lower on mobile than desktop. Nowadays there's no reason why mobile should ever be underperforming on AOV compared to desktop. So while it's true conversion rate has a little bit of a hall pass to be a bit lower than desktop, it is not the case for aov. So I look at this slide and the first thing I'm looking at is what is the delta and is it comparative here? And if this doesn't match here, immediately, I know we've got an issue there. So as an example, this brand I can kind of share did a phenomenal job with recommendations to sets. It's another brand that's in the accessory and kind of fashion spray space, right. Desktop had this beautiful, like you were shopping for this and they'd be like, complete the outfit, complete the set and you can kind of see how people would buy an ensemble. Mobile really didn't do that. They had that feature buried under a carrot. So like most people just didn't even see it. And even the ones who saw it had kind of had this random assortment of products. So that ended up being the hypothesis to be like, okay, well we've learned something from desktop there. How can we apply that to mobile? Turns out to be kind of a winning variant there where you can propose a higher aov. So moral of the story is while some metrics give you a, give you a acceptance criteria to be lower, don't fall into the trap that just because it's mobile you're allowed to have a lower aov. You shouldn't, you really shouldn't. Right now the one trade off to that would be for extremely high purchases, right? So like as an example, I worked with a, a car brand for quite some time that everyone would know the name if I said it. The average order value is like $46,000, right? I mean people are buying cars. In that case, the predominant behavior is people will spec the car on mobile and they'll jump over to desktop. So they saw very little conversion rate, so low it was almost statistically insignificant on mobile. All the conversion data happened on desktop, but all the browsing behavior and configuration happen on mobile. So in cases like that you can have a little bit of a, I know it's kind of an outlier, but generally speaking, again, AOV should be equal on mobile and on desktop. Now this is one of the last slides to go through here which is really interesting, which is demographic distribution modeling. So I've pitched you all sorts of things here about, oh, we got to dive into data, but a lot of CRO gets blanket applied to all users. That's a common mistake. The follow on question, a lot of the ideation I would provide and a lot of the analysis is to whom do we apply these ideas to, to maximize the impact. So one of the things that we're looking at is demographic distribution model, right? So in this case you're going to look at traffic patterns by age brackets here. So like this is the 18 to 24 mark right here. This is the 17% is going to be 25 to 34, et cetera, et cetera, all the way up to the 65 up. So it's one thing to know what percentage of traffic is broken into these different demographic buckets. The next question is what percentage of transactions attribute to those same buckets and are they even indexed? So as an Example you can look at the 25 to 34 year olds here. They're about 17% of traffic, but they're actually only 11% of transactions. So that is under indexed in terms of their performance. Right. Remember now on the flip side of that, you can look at the 35 to 44, they're 26% of traffic and they're 29% of transactions. And do note see that little orange sliver there? That's the 18 to 24. So you may be looking at these three. Just note that this 7.2 corresponds to that dark orange, but nonetheless that 29%. So they're what? Over index. So the question becomes to whom do we apply a lot of these ideas to? And the answer is, well, probably the audience set that is most underperforming when it comes to transaction volume, right? Not all users are going to react the same. A 65 year old Midwestern living, you know, woman may not respond the same as a 21 year old new York City living male. They're totally different demographics, right? So you've got to chop this down into demographic views as well in same thing when you get into like gender distributions as well. You know, in this case, obviously females are dominant on this brand. But if you take a look at overall transaction volume, you're going to see the 16 of males produces virtually no revenue. So it's like, do we want to even compress this down? Or maybe the question is, is there a way to convert these a little bit better, right? Maybe they're buying gifts. Depends on what it is. So long story short here is you want to use demographic modeling too to help guide your CRO program. And very last thing here, just before we wrap, just as a reminder to just hammer home to make sure everybody's heard this a thousand times. There is a step by step process to CRO. It is not just looking at design. You audit first, ideally identifying the metrics on FHIR, the metrics that matter, causal versus correlations, etc. Based on the data like we walked through today, you ideate ideation is unbridled. It's my idea, it's your idea, it's customers ideas, it's key stakeholders, random strangers, whatever, get a whole ton of ideas. Then we prioritize and nominate. We take 20 ideas, we narrow it down to the two that we think are going to absolutely move the needle, then we execute, launch an A B test, multivariate test, whatever it is, and then you analyze and rinse and repeat. That's the secret to it right there. Right. So that is the whole CRO loop and growth methodology all wrapped up. And that is my last slide.
- [40:50]
A
And it never really ends. I mean, technically, I mean, this is ad infinitum. I mean, I think, you know, in a lot of cases, you, since we've been working together, people are like, well, that's good enough for me. I mean, all you guys have like increased my conversion rates and the like based upon my mix and everything else and all the insights that you have here by X percentage. It's almost like it's a never ending process.
- [41:13]
B
Process, it really is. I mean, think of some of the biggest brands in the world. Think of, you know, the Airbnbs of the world. I mean today, why we're recording this, they probably have a department of two dozen people running experiments to improve repeat rates, conversion rates, referral rates, viral coefficiency, you name it. So, yeah, I mean, a site like
- [41:30]
A
Amazon, I think is running a thousand split tests at any given moment.
- [41:33]
B
Exactly. So there you go.
- [41:35]
A
So they're doing it. Shouldn't you be doing it as a business?
- [41:38]
B
Yep, exactly.
- [41:39]
A
This is, this is tremendous, man. I have so many questions. But the good thing is if we've got a third episode that's going to come, we can even go deeper into all of this. Obviously, if you want this for your business, Ned and his team are on standby to help you out and do this audit here. This is what you get in a condensed form in that initial audit. Like, this is incredibly valuable. I remember when we first started working together. I remember the demographic split on a client that we first started working on together. It was so skewed. I was like, oh my God. Like I've. I never would have understood that that was just one thing. And everything along the way, I mean, the card abandoned rates like the mobile versus desktop. With the differential, your AOV analysis, all of this stuff, like every little bit makes such a big difference. And it does start from this. This kind of basic, but not really basic. This is really in depth audit. You can get that over at tier11.com forward/CRO. So Ned, looking forward to episode number three here. I've got pages of notes to ask you even more questions, so maybe we'll make that one even a little bit longer. But obviously great to have you on here.
- [42:51]
B
Perpetual traffic.
- [42:52]
A
It's been a way too long. And just for showing us really that the differential between like how you and your team do all this versus what the industry standard is and the industry norm is insane to me. And that's why you guys are the best CEOs on the planet, there's no question.
- [43:08]
B
Appreciate you having me on as always. So looking forward to the next episode.
- [43:11]
A
All right, well, everything that we mentioned here will be over in the show notes over@perpetualtraffic.com make sure that you do watch. Listen to the first episode of myself and Ned. And looking forward to the third one. Make sure that you're looking at your your podcast feeds whenever that comes through. And of course, you can watch this@petpetual traffic.com forward/YouTube. And on behalf of Ned McPherson, until next show, See ya.
- [43:40]
B
You've been listening to Perpetual Traffic.