How to Use Edgemesh to Analyze and Optimize Google Ads Campaigns Part 2

[00:00:08] John Moran: And then I get to go back in time and benchmark that landing page and see regardless of the campaign, did I start to push more what the search terms look like different and what we're working better and what was working worse and all the other stuff. So yeah, we're going to get real deep into this and then overlay AI so we can do predictive indicators.

[00:00:25] John Moran: Oh, it's gonna be so powerful.

[00:00:32] John Moran: but FA, what we're going to be doing is removing these three products, not the first one, just removing these three products and making our own standard chopping campaigns. For these three with the daily budgets that are whatever we spent on the monthly budget of those products divided by 30. 4. So this would be, 98 73 costs in the last 30 days, 98 73, what was called 10 G's of 10, 000 divided by 30.

[00:00:58] John Moran: 4. This campaign is going to have a 330 daily budget on Rosemary. If these metrics hold inside of edge mesh, then we get to push. We just scale as long as my conversion rate holds my initiated checkout, my active car, my engages, as long as it's holding to these benchmarks. I'm going to be continually pushing because this is a snapshot in the future.

[00:01:17] John Moran: Seven to 10 days of revenue, both inside in app and also on Amazon. I don't have to push, wait seven days, get the, loss of attribution. Maybe they click brand, maybe they bought on Amazon. Maybe they, maybe we ran an email. Maybe they came back organically. Maybe we didn't spend too much of all that stuff goes away.

[00:01:32] John Moran: But I can say if they're getting to the site and they're staying, within these benchmarks metrics that they have for the last 30 days, I'll continue to push cool, right? We get to use our actual benchmarked previous historical performance. When this is good to say, will this hold, or when we push, do we see a diminishing return and engage users, active carts, initiate checkout, begin checkouts, or actually checkouts conversion rate does the ARPU.

[00:01:55] John Moran: Average, revenue per user on initiate gross checkout and net checkout and actual checkout. Does this stay the same? Cause again, I don't care. That's 0. don't want that to go down.

[00:02:05] John Moran: So this is where I can get a snapshot of future growth and scalability rather than,what could we attribute that week? Now,

[00:02:12] John Moran: this tool is 8k a month, but it is, it's super powerful.

[00:02:15] John Moran: And we got to see, are they still new? Are they still unique? Do they still have product view rates that are high? Yes. It's standard shopping. Of course they will. it's literally the only possible scenario in here because it is, Just look at this top line here.

[00:02:27] John Moran: As long as these metrics stay about the same product view, Do they view a product? Yeah. We sent them to the page. you can even see that there's 10 percent bouncers. Because they didn't even view a product before it even fully loaded. They just left, cool stuff, engage product view rate.

[00:02:39] John Moran: Percentage of product views by engaged users. Whether engaged, it's very good. this is so much fun. Gauge users was zero value carts. There's people that are really looking at this bill and going to multiple pages.

[00:02:50] John Moran: This is so much fun.

[00:02:51] John Moran: So Glenn, does that satiate your want for edge mesh? Does anything here that you want to see? want to see the patterns that you recognize more and then yeah, I want to play with it. I know. Look at this active cart, total value. This is 2600 versus 22 versus 14 versus a thousand. This one right here has got the highest.

[00:03:07] John Moran: And that is from the audience that is mostly engaged. So because we use leading indicators, not attribution, unless these people just start falling off at scale, then I know I hit a point of diminishing return on that channel. I've exceeded the available audience that Google can target, or at least I've exceeded the amount of demand from an inbound perspective.

[00:03:25] John Moran: I can go right up to the point where I would start to lose a dime and then stop. Are you doing anything with the standard shopping campaigns with respect to, making sure that the search terms are relevant, doing any negatives, all that sort of stuff? No, it's already there now, which is good.

[00:03:40] John Moran: the interesting thing though, is this here, watch this. this product, the, Disney collectors bundle, you see how this Disney collectors bundle has a, conversion rate of 0. 53, And it shows that I have, 713 and conversion value. it shows like eight sales. We see how they're starting to decline this page here.

[00:03:58] John Moran: Actually, it's been confirmed. We investigated. It doesn't have anybody converting. So we saw this two weeks ago and I was like, that can't be right. Google's showing, showing a metric. And I was like, this is, this doesn't make any sort of sense. So I noticed this two weeks ago, had a conversation with them this last week. And I was like, Hey, before it makes some expensive decisions, why this showing zero and compared to Google. And it's this one's like purple toning here. This is all the same thing. And then purple toning was like, no, we got a thousand bucks in conversion value.

[00:04:25] John Moran: that's what he's like. Same date range from all cold traffic, And he goes, so Google shopping check manually. Indeed. None of those clicks on the lander had a conversion. he's man, there's definitely no conversions. How? And I was like, actually we track every click for 90 days.

[00:04:37] John Moran: And he's that's convenient for them. And so we were like comparing and contrasting results. and when this one here, along with the, sat in heat list, this one, this last 30 days is showing none. So yes, these people are getting to the site and they are buying something.

[00:04:50] John Moran: But you can even see now this last 30 days, this one's starting to fail that landing page isn't converting anybody. So what does this show us? we see a leading indicator that's saying, Hey, the last 30 days of traffic. Now imagine this in the last four days of traffic where it's like last four days, Noah's converting this page.

[00:05:04] John Moran: Google has returned users, warm traffic that it tries to attribute to those campaigns that are outside of what I'm filtering and says, yeah, no one's actually hitting this page. it's not doing anything. Now this last 30 days, that campaign's tanked 50%. I could have known that a month ago by saying, actually, no one going to this Disney bundles, buying the Disney bundle, they're buying other things, but not that.

[00:05:23] John Moran: So shut that off. I can get these leading indicators before it starts to tank. So it's cool stuff. FAA, what's cracking you?

[00:05:29] John Moran: more question. Those are individual products. We also have the categories. So when you descend by clicks, the number one product was heatless curler. But that's the number three ad group in terms of clicks.

[00:05:40] John Moran: So when you descend clicks in the ad groups, pillowcases has the most amount of clicks. It's distributed around like 10 or 12 products. So to make sure like for Rosemary, do you want that product or do you want the whole Rosemary group? Nope. That product. That's what I love about Edgemesh is it's landing page specific and I want ad spend to landing page specific.

[00:06:01] John Moran: and I want to see if I can push it and correlating search terms. So we'll take just those three products, put them in their own campaigns. we're not going to care about if there's people that used to click on that one and go to another one. I want to see if I can isolate and scale.

[00:06:13] John Moran: that's the point of it. And then I get to go back in time and benchmark that landing page and see, regardless of the campaign, did I start to push more? What does search terms look like different and what we're working better and what was working worse and all the other stuff. we're going to get real deep into this and then overlay AI so we can do predictive indicators.

[00:06:29] John Moran: Oh, it's gonna be so powerful.