Shownotes
John Moran shares why he trusts Edgemesh and how he uses it to optimize Google Ads performance. He walks us through how he uses leading indicators to check what’s happening to the account, how the tool can help you with budget allocation for future growth and scalability, and more. Listen to this episode to learn more about Edgemesh and how you can use it for your Google Ads success.
Watch Part 1 here:
🚀 How to Use Edgemesh to Analyze and Optimize Google Ads Campaigns Part 1:
• 🚀 How to Use Edgemesh to Analyze and ...
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0:00 Intro
0:26 🚀 How to Use Edgemesh to Analyze and Optimize Google Ads Campaigns Part 2
4:08 Using leading indicators
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Transcripts
And then I get to go back in time and benchmark that landing page
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:and see regardless of the campaign,
did I start to push more what the
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:search terms look like different and
what we're working better and what was
4
:working worse and all the other stuff.
5
:So yeah, we're going to get real
deep into this and then overlay AI
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:so we can do predictive indicators.
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:Oh, it's gonna be so powerful.
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:but FA, what we're going to be doing
is removing these three products,
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:not the first one, just removing
these three products and making our
10
:own standard chopping campaigns.
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:For these three with the daily budgets
that are whatever we spent on the monthly
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:budget of those products divided by 30.
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:4.
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:So this would be, 98 73 costs in the
last 30 days, 98 73, what was called
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:10 G's of 10, 000 divided by 30.
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:4.
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:This campaign is going to have
a 330 daily budget on Rosemary.
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:If these metrics hold inside of
edge mesh, then we get to push.
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:We just scale as long as my conversion
rate holds my initiated checkout,
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:my active car, my engages, as long
as it's holding to these benchmarks.
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:I'm going to be continually pushing
because this is a snapshot in the future.
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:Seven to 10 days of revenue, both
inside in app and also on Amazon.
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:I don't have to push, wait seven
days, get the, loss of attribution.
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:Maybe they click brand,
maybe they bought on Amazon.
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:Maybe they, maybe we ran an email.
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:Maybe they came back organically.
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:Maybe we didn't spend too much
of all that stuff goes away.
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:But I can say if they're getting
to the site and they're staying,
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:within these benchmarks metrics
that they have for the last 30 days,
30
:I'll continue to push cool, right?
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:We get to use our actual benchmarked
previous historical performance.
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:When this is good to say, will this
hold, or when we push, do we see a
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:diminishing return and engage users,
active carts, initiate checkout,
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:begin checkouts, or actually checkouts
conversion rate does the ARPU.
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:Average, revenue per user on
initiate gross checkout and net
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:checkout and actual checkout.
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:Does this stay the same?
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:Cause again, I don't care.
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:That's 0.
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:don't want that to go down.
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:So this is where I can get a snapshot
of future growth and scalability rather
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:than, what could we attribute that week?
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:Now,
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:this tool is 8k a month, but
it is, it's super powerful.
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:And we got to see, are they still new?
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:Are they still unique?
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:Do they still have product
view rates that are high?
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:Yes.
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:It's standard shopping.
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:Of course they will.
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:it's literally the only possible
scenario in here because it is,
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:Just look at this top line here.
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:As long as these metrics stay about the
same product view, Do they view a product?
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:Yeah.
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:We sent them to the page.
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:you can even see that
there's 10 percent bouncers.
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:Because they didn't even view a
product before it even fully loaded.
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:They just left, cool stuff,
engage product view rate.
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:Percentage of product
views by engaged users.
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:Whether engaged, it's very good.
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:this is so much fun.
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:Gauge users was zero value carts.
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:There's people that are really looking
at this bill and going to multiple pages.
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:This is so much fun.
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:So Glenn, does that satiate
your want for edge mesh?
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:Does anything here that you want to see?
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:want to see the patterns that
you recognize more and then
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:yeah, I want to play with it.
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:I know.
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:Look at this active cart, total value.
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:This is 2600 versus 22
versus 14 versus a thousand.
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:This one right here has got the highest.
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:And that is from the audience
that is mostly engaged.
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:So because we use leading indicators,
not attribution, unless these
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:people just start falling off at
scale, then I know I hit a point of
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:diminishing return on that channel.
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:I've exceeded the available audience
that Google can target, or at
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:least I've exceeded the amount of
demand from an inbound perspective.
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:I can go right up to the point where I
would start to lose a dime and then stop.
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:Are you doing anything with the
standard shopping campaigns with
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:respect to, making sure that the
search terms are relevant, doing any
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:negatives, all that sort of stuff?
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:No, it's already there now, which is good.
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:the interesting thing though,
is this here, watch this.
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:this product, the, Disney collectors
bundle, you see how this Disney collectors
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:bundle has a, conversion rate of 0.
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:53, And it shows that I have,
713 and conversion value.
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:it shows like eight sales.
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:We see how they're starting
to decline this page here.
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:Actually, it's been confirmed.
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:We investigated.
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:It doesn't have anybody converting.
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:So we saw this two weeks ago and
I was like, that can't be right.
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:Google's showing, showing a metric.
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:And I was like, this is, this
doesn't make any sort of sense.
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:So I noticed this two weeks ago, had a
conversation with them this last week.
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:And I was like, Hey, before it makes
some expensive decisions, why this
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:showing zero and compared to Google.
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:And it's this one's
like purple toning here.
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:This is all the same thing.
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:And then purple toning was like, no, we
got a thousand bucks in conversion value.
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:that's what he's like.
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:Same date range from all
cold traffic, And he goes, so
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:Google shopping check manually.
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:Indeed.
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:None of those clicks on the
lander had a conversion.
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:he's man, there's
definitely no conversions.
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:How?
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:And I was like, actually we
track every click for 90 days.
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:And he's that's convenient for them.
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:And so we were like comparing
and contrasting results.
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:and when this one here, along with
the, sat in heat list, this one,
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:this last 30 days is showing none.
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:So yes, these people are getting to
the site and they are buying something.
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:But you can even see now this last 30
days, this one's starting to fail that
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:landing page isn't converting anybody.
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:So what does this show us?
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:we see a leading indicator that's
saying, Hey, the last 30 days of traffic.
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:Now imagine this in the last four days
of traffic where it's like last four
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:days, Noah's converting this page.
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:Google has returned users, warm
traffic that it tries to attribute
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:to those campaigns that are outside
of what I'm filtering and says, yeah,
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:no one's actually hitting this page.
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:it's not doing anything.
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:Now this last 30 days,
that campaign's tanked 50%.
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:I could have known that a month ago by
saying, actually, no one going to this
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:Disney bundles, buying the Disney bundle,
they're buying other things, but not that.
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:So shut that off.
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:I can get these leading indicators
before it starts to tank.
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:So it's cool stuff.
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:FAA, what's cracking you?
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:more question.
133
:Those are individual products.
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:We also have the categories.
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:So when you descend by clicks, the
number one product was heatless curler.
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:But that's the number three
ad group in terms of clicks.
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:So when you descend clicks
in the ad groups, pillowcases
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:has the most amount of clicks.
139
:It's distributed around
like 10 or 12 products.
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:So to make sure like for Rosemary,
do you want that product or do
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:you want the whole Rosemary group?
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:Nope.
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:That product.
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:That's what I love about Edgemesh is
it's landing page specific and I want
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:ad spend to landing page specific.
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:and I want to see if I can push
it and correlating search terms.
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:So we'll take just those three products,
put them in their own campaigns.
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:we're not going to care about if
there's people that used to click
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:on that one and go to another one.
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:I want to see if I can isolate and scale.
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:that's the point of it.
152
:And then I get to go back in
time and benchmark that landing
153
:page and see, regardless of the
campaign, did I start to push more?
154
:What does search terms look like different
and what we're working better and what
155
:was working worse and all the other stuff.
156
:we're going to get real deep into
this and then overlay AI so we
157
:can do predictive indicators.
158
:Oh, it's gonna be so powerful.
