How to Use Edgemesh to Analyze and Optimize Google Ads Campaigns Part 1

John: 00:00:11

So cool stuff.

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Then this one I'm using, I'm using

Edgemesh for this is going from

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best to worst, but our traffic

isn't going from best to worst.

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It's actually going from worst to best.

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Ah, ha.

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We have a mismatch,

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and it's gonna be based off

this edge mesh, and you guys

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will watch, it'll be really fun.

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We're gonna make a live

update with Edge Mesh.

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There we are.

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So check this out.

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Campaign id.

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All right.

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Lemme get all this.

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One UTM, source.

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Lending page, new user, returning

user, returning purchaser,

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should be good for now.

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So this is the last 30 days of data.

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And what we're going to do is use

the same last 30 days of data.

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We're going to do the products.

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Cool.

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So we're going to grab that campaign ID.

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We're going to filter by this campaign ID.

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So this is my standard shopping campaign

from Google, and this is going to be

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sorting descending by unique users.

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And what we're going to see here is we

have a satin heatless curling set and

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in our standard shopping campaign with

our products starting descending, we

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have the satin heatless curling set.

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So we should see satin, biotin,

shampoo bar, and strength.

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The heatless, biotin,

shampoo bar, and strength.

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good.

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Now watch this.

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Here's what our low t ROAS

strategy is not doing properly.

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You're going to notice a few things.

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This is reversed in the four top products.

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So this is.

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First four are unique users going down.

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However, our conversion rate is at

an angle that is going this way.

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It's actually heading up this way.

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If you look at this kind of bar, the

completed checkout rate is also going up.

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The initiate checkout

is also going up here.

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The engagement is also going up here.

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So what this should be doing

is showing the reverse.

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I should be seeing more users down

here and less as I go up, because

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this is going from best to worst.

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But our traffic isn't

going from best to worst.

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It's actually going from worst to best.

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we have a mismatch between

what we have as clicks versus.

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Point one to 0.

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41 to 0.

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49 0.

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93 and conversion rate.

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Now, are these numbers, right?

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Two thirds of all of our sales

happen in Amazon, the trends

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are at an engagement is correct.

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Active cart user rate is high,

but they check out the least.

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Okay, so is this still working well?

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Yes, this is a flagship product.

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And yes, they buy this

on Amazon most often.

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So this is okay because this is

literally what started their company.

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They mentioned it up here.

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this is where we're actually

starting to see a loss.

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So we need to have a high number

of users Are we looking at pages?

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What are these rows?

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These are products in the Standard

Shopping Campaign engagement on the site.

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so what I did is I filtered this actually

by landing page and then used the

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filter of the Standard Shopping Campaign

ID, which is the: 2824

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spending 150K on in this last 30 days.

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it's like saying, If we're seeing more

engagement here in the first four, but

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we should be spending more water on like

rice water and then nourishing shampoo

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bar, Now this is on a 30 day increment.

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So what we're seeing is this 30

day increments matching what are

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30 day increment is in google.

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if you have a seven day sales cycle,

you don't get this in the last seven

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days until they start to purchase.

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However, We do get an edge

match 70s earlier because

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this is a 15 minute interval.

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It doesn't have the data driven

split attribution between brand

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and blah, blah, blah, blah,

blah, blah that we normally see.

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This is all single click results

off of unique users who are first

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time users, not returning users

and not returning customers.

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This is all cold traffic.

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That's why standard shopping is glorious.

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The first time we get a

return user is down here.

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And what's nice is that same product,

the Satin Helix Curling Sunset Tie Dye,

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which is the Satin Helix Curling Sunset

Tie Dye, when they come back, you can

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see their engagement is amazingly good.

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And then it converts at 1.

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35, 0.

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1.

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So when they return, much,

much, much better results.

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So that's why remarketing this product

and other channels is going to be a good

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idea because now you get a 71 percent

initiate checkout rather than a 56.

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You get a 52 completed rather than a

47 and you get a conversion rate from

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those people that come back at a 1.

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35, not a 0.

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1.

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So it's literally 10 times better

when they come back, which is true,

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which is why we look at product splits

between cold traffic and remarketing.

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Cause when they come back with a 10

times the engagement and conversion

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rate, why not spend one 10th or one

20th or sorry, one 10th or two 10ths

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of your budget remarketing them.

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We can see that when they come back on

their own accord, they convert well.

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So that's what I'm using edge mesh, which

is how much cold traffic, how much warm

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traffic, but because I could care less

about the warm traffic, I just need to

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push cold traffic because I lose two

thirds of the sales stamps on anyway.

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So I can't tell if they're actually

going through, but the trends are

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correct in by saying is these are

wrong metrics, but trending in the

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right direction, it's going to be a

little bit difficult to say, no, the 0.

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1 and the 0.

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9 convert, the same on Amazon.

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I would beg to differ.

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These people are engaging with the site

way more often than they are, on this

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product than they are in this product.

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Is that the same correlation on Amazon?

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Is it the same correlation on meta?

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Is it the same correlation with overall

product sales to your business, depending

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upon the overall traffic to those pages?

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What is the product

media efficiency ratios?

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So cool stuff.

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I'm using edge mesh for