Unknown Speaker 0:10
I’m Brooke Are you with us?
Unknown Speaker 0:15
Unknown Speaker 0:16
There we go. Welcome. How are you doing today? I am good.
Unknown Speaker 0:20
I’ve got my coffee. I’m ready to go. So,
Unknown Speaker 0:24
all right, let’s do it. So everyone Next we have Brooke, whose last name I always get wrong. So I will let her say that in a moment. She joins us from the snowy North right up northern Minnesota with an aptly named brand nordik. Click. So please welcome Brooke to get into tracking and reporting that ecommerce sales.
Unknown Speaker 0:46
Thank you. Alright, I just want to make sure you guys can see my screen. Okay.
Unknown Speaker 0:52
You’re all good. Take it away.
Unknown Speaker 0:54
So thanks, everybody. like Brad said, my name is Brooke Asmussen, it just looks a little bit harder to pronounce than it actually is. But I have been with Nordic click for about four years now. That’ll be this summer. But I’ve been in paid media and the e commerce space for a little over eight years. So I’ve seen a thing or two. And so we’re our agency is located in the Minneapolis area. I’m a little bit farther south from that in Rochester. But you think Mayo Clinic that’s that’s where I’m at today. And so I have been doing PPC long enough. And I’ve you know seen a lot of different types of clients ranging from e commerce lead, Gen, b2b, b2c, you name it. And I have run into many different tracking issues, if you’ll call them that may just make you want to bang your head against the wall. And what’s worse is when clients feel that there’s tracking issues, and really starts to question that data. So I wanted to go about this this way, in terms of finding like three common myths that I’ve come across, and really how to combat them and have these conversations with your own clients. So Myth number one,
Unknown Speaker 2:04
Google Analytics tracking
Unknown Speaker 2:05
must always match Google ads, or they’re recording directly from the e commerce platform, or something is bound to be wrong, right? That’s absolutely false. And I bet you’re thinking about this and your white shirt voice right now.
Unknown Speaker 2:19
Because I always
Unknown Speaker 2:19
think about that what I say false. So I’ve got a few tracking tips, you know, to really talk about what what might be the differences and why and why they might not necessarily be wrong. So my first tip is a kind of go back back to basics, but it’s often overlooked, is to really review and confirm all settings, whether you’re looking at Google ads, or Google Analytics. So there’s a few settings that you might want to look at in terms of discrepancies. And I say discrepancies in quotations because neither of them might necessarily be wrong. They’re just all different examples of how data is presented in a different way. And it’s really up to you and your client to determine the best way to interpret this data. So a couple examples that we’re going to look more in depth today has to do with lookback, windows, some attribution settings, multiple ga views potentially linked to your Google Ads accounts. And there’s a couple more that I’ve listed here, we won’t go too in depth with those today, because some of those, frankly, would require their own separate conversation. So but I just wanted to give you kind of a list to you know, check those boxes, when you’re looking at different things where some might just be different settings and attributions where some of these other ones, you could have filtered views in Google Analytics, where of course your data is not going to match because you’re taking out, for example, IP addresses or anything like that. But nowadays, the the more important one is, people could have browser preferences, preventing Google Analytics from loading. Nowadays, people are getting smarter about not wanting to be tracked. So there’s always going to be some differences there. Which is why I use the word discrepancies with with a grain of salt. So this example, we’re looking at, look at look back windows. So this shows a client in Google ads for q4. We’re looking at October through December of last year. And I just wanted to note, you know, their conversion value shows about 447,000. Right here, their conversions are a little over 7300. So just keeping those numbers in mind. Now when I look at that same exact data in Google Analytics, the revenue is actually stayed at about 470,000. And the conversions are just above 7500. So the key differences here, conversions are different by about 249. Noting Google Analytics is higher revenue is almost $24,000 difference with analytics being higher within the conversion rate is actually about 20 Almost 21% difference where Google ads are actually showing higher. So it really does beg the question, which one is correct. And really, it depends on what type of model portrays the most accurate depiction of your business. So a couple of different things here is it really is all in the settings. This is a specific client that I pulled, they have their own their goals imported from Google Analytics. And just a side note, that’s my own personal preference. Instead of having Google Ads conversions versus Google Analytics, analytics, it keeps it a lot cleaner. But the key differences here are how Google Ads settings are different than the analytics. And I’m pointing out a couple, because they’re really important to understand why your data might not match what Google Analytics is reporting. So the first one is how Google Ads is counting a conversion. You’ll note here that this is showing one conversion, meaning if a user comes back from the same ad, or multiple ads, and they make multiple purchases, Google Ads is only going to attribute one conversion to that person, where Google Analytics by default shows every conversion. So that could be a big difference, especially if you have a product or different products where a user is, is going to buy more than once, if you have a luxury brand, where you you might only buy it once in your lifetime or once a year, that’s a different story, you’re not too worried about it. Now, the second piece to look at is this attribution model. Google Analytics shows that we are tracking this based on a position based model where Google Analytics is by default, last non direct click. So there’s a few just small key settings that could actually make a big difference in determining, you know, which which data you want to look at. And I did highlight their their conversion window does match what Google Analytics is, by default is 30 days. So really just looking at these two key differences. So even going further than that, you know,
Unknown Speaker 7:00
I just talked
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on attribution. Another tip is to start reviewing these models to really understand a holistic picture. And to have these conversations with your clients. I’m going to go through a couple examples here. This shows Google Analytics default of last non direct click, versus a position based model, which we just discussed. So in this example, the position based model paid search is actually under valued compared to Google Analytics. And I know that this specific client example is due to a lot of brand spent and conversions, which completely makes sense, it typically has a higher percentage of last click attribution. And that’s validated here. So this is just one example of how you’re looking at things from a channel perspective. I like to go more in depth in terms of which parts of paid media are we looking at? Are we looking at Google, Bing, LinkedIn, Facebook, any of those Pinterest, you can get down further in that based on just overall the channel. But this is a good place to start when you’re kind of evaluating all channels. Now, the second model that I’m looking at is the last interaction versus the first interaction. And this is really important, when you have a lot of brand building efforts to so you’re spending a lot at the top of funnel here. This is another client example that’s different from the previous one. You’ll notice here that there, if you’re looking at last interaction, it’s about $370,000 of conversion value. But if we’re looking at first interaction, that jumps up to almost 485,000. So when you compare the two, you know, it really helps prove or validate some of those top of funnel efforts, as you can see that paid media increases over 27% compared to that last interaction. So just a couple examples of you know, how you should be at least having those conversations and starting to, to look and build models for your clients that that work well for them. And what makes the most sense. So another one of the, you know, discrepancies that we talked about is this could potentially be an actual tracking issue, if you have multiple Google Analytics views, links. This is a screenshot of a client that we got brought into, and we were looking at their data and what is what is actually happening. And we found we’ll see six different Google Analytics views linked and they all had goals imported from Google Analytics. So this would actually be an example of, you know, some duplication issues, and it’s really best to get those cleaned up ASAP. So the first two examples that I showed were really it’s not necessarily which one’s right or wrong, but how are you? How are you talking about those efforts into a whole story where this example is more of a Oh, you should really understand which view you’re looking at which is the most accurate and cleaning some of these up
Unknown Speaker 10:00
Alright, so Myth number two. This one’s fun when people say it is all about the revenue. And we know that is simply false as well. So we we know that revenue is obviously like, what keeps the lights on, if you will. But there’s so much more as marketers that we need to care about. And and we do and it’s more so to get the client to understand what we should be caring about as well. What we should be looking at is, what causes revenue to happen? Because then, you know, what can really affect that? Like, what, what efforts are you putting into your marketing strategy that causes an eventual conversion. And I’ll go into a couple different things later on in the slides to kind of put it all together. So the number one thing here is tracking any engagements that you have outside of revenue. So when we look at this, you know, we know that the traditional expectations of a one and Dawn session are gone, you take a look at just this depiction. I feel like one of these is me, we all have so many different devices, I’m on my laptop, and desktop throughout the day, I’ve got my phone over here, I’ve got my kitchen, iPad, when I’m making dinner, and I like on all of these different websites all the time. And one of those touch points with the brand is likely going to influence a potential purchase for me maybe later in the evening. But this just goes to show that it’s a lot more complex than it used to be. And we need to take all of that into consideration when we’re looking at, you know, just the revenue piece like what what type of messaging and content Do you have that’s going to influence that eventual purchase. Another thing to think about is that users just really aren’t as loyal to brands as they used to be. And it’s going to take multiple touchpoints. For example, even I know this is about e commerce, but we have a b2b client, where we did an analysis of the last quarter versus q2 last year, and their average touch points to eventual conversion almost doubled, compared to the previous quarter. So it’s not even just ecommerce. It’s its overall kind of user behavior, what we’re looking at. So a couple different engagement tracking examples that I’m going to go through, because this will roll up to the point that I’m trying to make is, what can you track outside of just revenue? A couple of different examples would be email, signups, you know, kind of a no brainer. But I think that’s often overlooked in terms of like, what could that really add value to? If you have sample requests on your website, I go, I always go back to this example, I used to work for a wedding invitations company before I went agency side. So that was a huge brand building effort that brought in eventual conversions. If you’ve got catalog requests, like any of these different touchpoints, or chat, initiate, are they are they engaging on your website with somebody or your chatbot, I think these are all important to look at the bigger picture.
Unknown Speaker 13:02
So then even going further than that, not only tracking these different pieces, but really assigning value to these engagements to get a better understanding of how that influences your return on adspend. I’m going to use those same, those same examples here to just try to show a little bit of a story here. So our agency actually, we’ve gone through these exercises with clients to help understand how to assign value to to some of these smaller, smaller engagements. So if you have questions, again, I’ll be on the panel later on today as well. But feel free to If you have any additional questions, just let me know. But this is an example here to just how we’re going to track additional row as so for example, an email signup you save, each one is worth $2 to your company could be more could be less, it’s all going to be dependent on your business. Those sample requests, say they’re worth $10 to you to your company. catalog requests are worth $5 and chat initially, it’s worth $5. So again, these are caveat, just examples, you really want to go through these exercises to understand that true lifetime value of that person for your company. So then I’m gonna give an example of what this looks like, you’ve got monthly paid media costs of about $50,000. And when you’re looking at last click revenue, your goal is 150, which brings your blended return on adspend to about 300%. And that’s just looking at the revenue that you’re bringing in. So then let’s take a look at adding in all of these different engagements that we talked about. So I put a table together of say you’ve brought in 1000 emails that month 450 sample requests, 100 catalog requests 375 chat in the sheets, and then I put the value assigned based on the previous slide of what that would look like. So it brings in in additional of almost $9,000. So now you go from a 300 return on ads. Then to a new kind of blended return on adspend of almost 318%. So really, those incremental goals and tracking, in my opinion, they do go a long way. And it really helps build that and that justification of what are you doing to influence that eventual purchase. So when you combine that 300% with this, the, you know, the justification is there, this real dollar value in engagements, so you do want to track them. And then, you know, when you’re talking to your clients about this, it’s also helpful justification for, you know, potential decision makers that are influencing your marketing budget, and they need to understand that full picture, this is a great way to help easily visualize what those incremental pieces would be to track the overall revenue and engagement of your user. Alright, so Myth number three, each PPC campaign should have the same return on adspend goal, by now you figure it out, that’s probably also false. So this gets into the actual how we are looking at return on adspend. From a campaign perspective, which does roll up to how we’re reporting and tracking those ecommerce sales. So you really should be looking at different row as targets based on how your campaigns fit into the different funnel. So for example, you know, I just gave this if you have a blended 300%, return on adspend goal, if you’ve got awareness tactics up here, whether it be YouTube ads, you’re doing some Pinterest awareness, you may only see a 50 to 100%. And, you know, when you if you’re looking at just last click, that may look like you are potentially losing money. But what really happens is you make up for it down the funnel. And if you’re only focused on this, these two buckets down here, my one of our partners, Adam, he has a great saying, you will likely die a slow profitable death if you only focus on here. But it’s super important to understand how these efforts really influenced the funnel to get down to your blended goal of 300%. So when you’re tracking and reporting on your ecommerce sales, you know, having realistic expectations based on how these tactics and platforms can help, you know, really understand the top of funnel efforts and how they contribute to that goal. So for example, some PPC keywords would be great traffic drivers, but maybe unlikely to see a conversion right away. So thinking should they automatically be paused? I would say no, not without the data. And it’s really time to determine that trade off of what will happen down the line, if you pause those campaigns, just because you’re not seeing that last click revenue. So for example, you pause those and you see overall performance decline in the future, that’s likely, it likely shows that those campaigns did play a bigger part than just what last click revenue shows.
Unknown Speaker 17:59
Alright, so now I’m going to get into some just advanced techniques, examples reports that I take a look at that I really helps drive conversations with clients based on some of these these myths here. So the first one is to start reviewing the in depth transaction reports for additional context. So we have all of this data at our fingertips. But how do we use it to really look at behave, you know, behavior from a user standpoint, that influences overall conversions? So a couple that I like to look at, you know, going back to basics are path length to transactions, and how many interactions does it take a user to convert? The second one that I’m going to take a look at is the time lag report, and how many days does it take to convert? So this first example here, this shows the path length Report, I’m looking at the default 30 day look back window of an ecommerce client. And this is this is an important thing to note. This is only looking at revenue, it takes out any additional touch points like the email, signups and samples, because I want to understand, you know, the actual revenue piece, how long did it take to get there? So you know, not surprisingly, the top, you know, top four make up a large percentage of those conversions and conversion value, but you look at 12 plus interactions, and that’s about 13% of your revenue. So there’s justification in there in terms of what are you doing from a brand building perspective to influence and eventual conversion from e commerce. Now, I did that same report, I look back 60 days prior to the conversion, and you can see I’ve only got revenue, I’ve only got revenue that I’m looking at. And that 12 plus interaction jumps up to over six over 16%. So that actually shows some of these other pieces of might take a little bit longer, and being able to play with that look back window really helps tell a better story. Have some of these things that are just influencing that brand awareness to the overall conversion rate. So the second report that I’m looking at, again, is the time lag report, looking at the default setting for 30 days, in this other client example, in the 30 day, look back window, the 12 to 13 days here at the bottom is actually a higher percentage of sales than on day zero. And this is exactly why attribution is so important when you’re, you know, investing in paid media efforts and looking for that justification. You know, last click, it just is not a great example anymore. And this is a, this is a prime reason why we should be looking at your data in a different way. So even better for this specific client, I go back again, 60 days for consistency. And now the 31 to 60 days in this example, is overpowers that 12 to 30 days. So again, just looking at this, having those conversations with your clients, and it’s a lot easier visual way to show them here, you know, here are the things that we’re doing, you may not see those efforts really come to fruition until X amount of days. So this is super important to look at. Alright, so getting out of some of those user behavior reports, you know, I think, from an e commerce standpoint, you absolutely should be implementing enhanced e commerce to get those additional insights. So, you know, it’s not enough to just have the traditional e commerce tracking anymore, you really should go that extra step to implement enhanced e commerce. And this is just an example of all the additional reports and insights that you can get. So some of the benefits that we’re going to get into is really that understanding of a shopping behavior of a user before they even get to check out, you can look really quickly at performance by category on your website, where users are dropping off at checkout, if you’ve got internal promotion strategies, so you’re testing out promo codes with your paid ads or your emails, you want to know what’s working and what’s not. And then lastly, transaction specific data. And this can actually include refunds, taxes, everything that you’re not going to see in just the traditional way of looking at things if you don’t have enhanced e commerce. So this is an example of that shows the ecommerce behavior overview, it does give a good snapshot of entry to exit. In my opinion, this really also helps identify larger trends in the company or business, or gaps in performance that may need to be addressed that are actually larger than paid media. So for example, the amount of sessions here that drop to actual product views could indicate the user experience gap on either category page, or even the homepage. The second report here that I want to get into is the product list performance. This is one of my favorites. And I kind of call it a goldmine because it breaks down the overall product category revenue, and additional metrics to determine like,
Unknown Speaker 23:00
Is there anything that we could improve from that experience? If you’ve got campaign PPC campaigns broken out by this structure, it helps give a guide on each category performance to identify trends can also help guide expected rollouts conversations as well. You can determine average order value on these categories. But then also each product report and transaction report automatically includes that data. So this report, again also helps identify overall trends that could determine larger changes that are not necessarily just, you know, if we get paid media, like we need to cut here, this helps identify like this might be larger than paid media, this one that I have listed here, it has a very low product list, click through rate. So that can help spark conversations with your client on what do we need to change on whether it’s that category page? Or what if there’s something broken, or it’s just the user experience really poor? I think that’s all really good information just outside of the e commerce piece that gets into that user behavior and experience experience. All right, so I know I’m coming up on time here. So just to recap, you know, some of these, we could have a lot longer conversations, but we’ve got just a limited amount of time here. So again, I look forward to your questions. I’ll be on the panel. But you know, really recapping, when we’re putting a tracking on ecommerce sales is, you know, don’t buy into the outdated methods and myths that I like to call them myths. And we need to understand and know when and why it’s okay when your data doesn’t match and really how to talk to them. Tracking more than just your revenue add even for your assigning value to those efforts. And then assigning different rollouts KPIs to different campaigns based on intent, intent, excuse me. And then lastly, you know, utilizing the attribution reports for a more more holistic picture, and enhanced e commerce. It’s just a must you definitely You should be doing that you’re going to get a lot more insight than you typically would in just regular ecommerce tracking. That is all that I have. And I will go ahead and stop sharing.
Unknown Speaker 25:16
Thank you, bro. I love the blended slide and row s, I think that is probably not talked about enough as robust targets for keyword versus, you know, a counter campaign. So that’s great. We have several questions. We are at a time. So we’ll come back to those because a lot of our good debate questions when you get into what’s your favorite attribution model? That may be a debate as much as an answer.
Unknown Speaker 25:39
I actually refrained from putting in and it depends me because it’s everybody’s favorite and worst
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