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<v Speaker 1>Welcome to the deep dive. Okay, so if your Google

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<v Speaker 1>Analytics dashboard feels less like a useful tool and more

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<v Speaker 1>like just this overwhelming wave of data hitting you, well

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<v Speaker 1>you're in the right place today. Yeah, we're not just

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<v Speaker 1>going to read off reports. We want to give you

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<v Speaker 1>the tools, the actual techniques to turn all those numbers

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<v Speaker 1>that raw traffic data into real actionable business decisions you

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<v Speaker 1>can use like right away exactly.

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<v Speaker 2>And our deep dive today it's really based on this

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<v Speaker 2>idea of interpretation. We've got this comprehensive guide focusing on

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<v Speaker 2>that because ga, I mean, it's way more than just

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<v Speaker 2>counting visitors, right is grabbing all these details where users are,

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<v Speaker 2>what systems they're on, how they found you, you know,

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<v Speaker 2>their traffic source. It maps their whole journey on your site,

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<v Speaker 2>and crucially, it measures if you're hitting your business goals,

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<v Speaker 2>your conversion to your sales. That's the core.

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<v Speaker 1>But okay, before we jump into the really cool stuff,

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<v Speaker 1>the traffic insights, the sales strategies. The sources we looked

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<v Speaker 1>at were super clear on this. If your basic setup

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<v Speaker 1>is wrong, your analysis later on it's pretty much useless totally.

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<v Speaker 2>You build on sand the whole thing falls down.

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<v Speaker 1>So we have to talk about these like non negotiable

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<v Speaker 1>first steps. The initial setup.

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<v Speaker 2>Okay, yeah, first one seems simple, but wow, it gets

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<v Speaker 2>messed up a lot. The time zone setting.

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<v Speaker 1>Uh huh.

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<v Speaker 2>And look, this isn't just like a personal preference thing.

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<v Speaker 2>It's fundamental for the business side. You absolutely have to

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<v Speaker 2>set that time zone to match where your business actually

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<v Speaker 2>operates or maybe even more importantly, where your main customers

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<v Speaker 2>are actually buying from.

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<v Speaker 1>Okay, so walk me through that. Give us a concrete example,

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<v Speaker 1>like what happens if you get that wrong? What's the damage?

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<v Speaker 2>All right, imagine this. You're sitting in California, right, but

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<v Speaker 2>all your marketing, your ads, everything, it's targeting the UK market. Okay. Now,

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<v Speaker 2>if your gaview is still stuck on Pacific time, well,

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<v Speaker 2>your reports for timing your market they're basically junk. Joe Dell, Well,

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<v Speaker 2>think about London's peak buying time. Let's say it's I

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<v Speaker 2>don't know, ten am to noon over there in GA

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<v Speaker 2>dashboard set to Pacific time. That's going to show up

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<v Speaker 2>as two am to four am.

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<v Speaker 1>Oh wow.

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<v Speaker 2>Okay, so if you look at that report, you might think, huh,

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<v Speaker 2>people are buying in the middle of the night and

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<v Speaker 2>then you shift your ad bids, your campaigns completely wrong.

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<v Speaker 1>Yeah, based on totally misleading data exactly.

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<v Speaker 2>But if you set the time zone correctly to London time,

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<v Speaker 2>you actually see those critical mid morning buying peaks, You

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<v Speaker 2>see the real behavior.

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<v Speaker 1>Okay, that immediately changes things like admitting, maybe even staffing

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<v Speaker 1>if you have support. Got it? So what's the second

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<v Speaker 1>absolute must do? This one sounded serious like data integrity implications.

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<v Speaker 2>Yeah, data integrity is key, and that means the backup view. Seriously,

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<v Speaker 2>this isn't just nice to have, It's mandatory. Anyone developer,

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<v Speaker 2>business owner, or marketer, anyone who's going to touch their

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<v Speaker 2>GA data needs to immediately create a second view, one

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<v Speaker 2>that's totally untouched, unfiltered, just the raw data feed.

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<v Speaker 1>But why, like why the big rush? Can't you just,

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<v Speaker 1>I don't know, undo a filter if you mess up.

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<v Speaker 2>No, that's the critical point. You absolutely cannot undo it.

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<v Speaker 2>The fundamental rule here is once you apply a filter

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<v Speaker 2>or exclude bot traffic, or make any kind of configuration

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<v Speaker 2>change to a gaview that original raw data for that period,

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<v Speaker 2>it's changed permanently.

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<v Speaker 1>Permanently.

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<v Speaker 2>Wow, yep. So if you accidentally filter out, say ninety

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<v Speaker 2>percent of your real mobile users, or maybe some experimental

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<v Speaker 2>filter totally messes up your conversion tracking that historical data,

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<v Speaker 2>it's gone forever.

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<v Speaker 1>Unless you have that clean, untouched backup view.

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<v Speaker 2>Exactly. You need that raw data safe. It lets you

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<v Speaker 2>test filters, make changes in another view, and always have

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<v Speaker 2>the original to go bear against or fall back on.

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<v Speaker 2>It's your safety net.

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<v Speaker 1>Okay, that's a really strong warning data safety net. I

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<v Speaker 1>like that. So let's assume we've done our homework. We've

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<v Speaker 1>got clean data. It's time synced correctly. Now, strategy, this

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<v Speaker 1>is where it gets interesting, right, moving beyond just charts

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<v Speaker 1>and integrating GA with actual business frameworks. You mentioned the

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<v Speaker 1>seven piece of marketing.

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<v Speaker 2>Yeah, exactly. The real power comes when you overlay this

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<v Speaker 2>data onto established models like the seven p's. We can

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<v Speaker 2>look at say place, market and location strategy, and people's

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<v Speaker 2>service and user experience to see how the data drives

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<v Speaker 2>real decisions.

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<v Speaker 1>All right, Let's start with place, so figuring out where

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<v Speaker 1>our visitors are coming from. But more than that, like

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<v Speaker 1>how good are those visitors? Comparing volume versus actual quality?

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<v Speaker 2>Percisely, we dive into the geographic reports. Let's say we're

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<v Speaker 2>looking globally, the data probably shows the US is number

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<v Speaker 2>one and just sheer volume, right, most clicks, most traffic

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<v Speaker 2>makes sense. But then maybe we look deeper and we see,

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<v Speaker 2>let's say Canada, maybe Canada is only I don't know,

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<v Speaker 2>fourth or fifth in terms of raw traffic volume. Okay,

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<v Speaker 2>but when we look at the e commerce conversion rate

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<v Speaker 2>or eCCr, Canada is consistently ranked second highest.

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<v Speaker 1>Whoa hold on ACCR. That's the percentage of visits that

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<v Speaker 1>actually buy something, right exactly, So Canada's fourth in traffic

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<v Speaker 1>but second in buying rates. That's a pretty big disconnect.

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<v Speaker 1>What's the strategic move there?

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<v Speaker 2>Well, the insight is clear, Canada isn't just traffic, it's

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<v Speaker 2>high quality traffic. It's a high potential market. So while

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<v Speaker 2>the US needs maybe the most basic attention just because

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<v Speaker 2>of the volume, Canada shows this amazing efficiency.

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<v Speaker 1>So you don't just chase volume there.

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<v Speaker 2>Right, The action is probably to invest in optimizing the

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<v Speaker 2>experience for Canada. First, maybe tweak delivery options for them,

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<v Speaker 2>offer some specific local deals, capitalize on that high conversion

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<v Speaker 2>intent they already have, rather than just trying to pump

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<v Speaker 2>more raw traffic.

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<v Speaker 1>Yet okay, so prioritizing quality over quantity in that specific

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<v Speaker 1>market makes sense. Now, what if we drill down. Let's

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<v Speaker 1>say we focus just on the US. How can GA

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<v Speaker 1>help decide between, say, spending more on online ads versus

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<v Speaker 1>maybe opening a physical store somewhere.

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<v Speaker 2>Yeah, the domestic drill down often gives you these seemingly

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<v Speaker 2>conflicting signals, which is actually useful. So we might find,

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<v Speaker 2>for example, California brings in the most overall traffic, huge

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<v Speaker 2>numbers like ninety seven thousand visitors, eight hundred and fifty

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<v Speaker 2>five transactions, massive deman Okay, sounds great, but their conversion

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<v Speaker 2>rate is only say zero point five to one percent,

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<v Speaker 2>pretty low. Then you look at Texas, maybe less overall traffic,

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<v Speaker 2>but they're conversion rate significantly better, maybe zero point eight

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<v Speaker 2>one percent.

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<v Speaker 1>Okay, So high volume, low conversion in California, medium volume,

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<v Speaker 1>high conversion in Texas. Where does the money go? That

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<v Speaker 1>seems tricky, it does.

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<v Speaker 2>But the data suggests two different actions. For immediate online

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<v Speaker 2>ad spend, you probably invest more in Texas. Why, because

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<v Speaker 2>it converts efficiently. You get a better return on your

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<v Speaker 2>ad dollars.

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<v Speaker 1>Right now, Okay, boost the Texas online budget.

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<v Speaker 2>But if the discussion is about opening a new physical store,

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<v Speaker 2>you might actually choose California, maybe San Francisco.

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<v Speaker 1>Well wait, why if the online conversion is lower there?

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<v Speaker 2>Because that massive number ninety seven thousand visitors signals huge

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<v Speaker 2>underlying consumer interest just raw demand. The fact they aren't

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<v Speaker 2>converting online as well as Texas might point to other issues,

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<v Speaker 2>maybe logistics, maybe trust. Maybe they just prefer to buy

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<v Speaker 2>in person for that product. A physical store could solve that.

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<v Speaker 1>Ah. I see, So the high visitor count justifies exploring

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<v Speaker 1>the physical potential even if online isn't perfect yet, and

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<v Speaker 1>the high conversion rate in Texas justifies immedia digital spending.

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<v Speaker 2>There exactly two different strategies, two different investments, both informed

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<v Speaker 2>by digging into the same report but looking at different metrics.

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<v Speaker 1>That's really smart separating those decisions. Okay, let's shift gears

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<v Speaker 1>to another. People thinking about service user experience, maybe testing

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<v Speaker 1>if our help content like FAQs is actually helping.

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<v Speaker 2>Right, this is a great place to use segmentation. We

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<v Speaker 2>want to know if all the effort we put into

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<v Speaker 2>making clear store policies or detailed faques is actually useful,

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<v Speaker 2>or if it's maybe just confusing people or turning them off.

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<v Speaker 1>So how do we segment for that?

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<v Speaker 2>We create two segments, converters people who actually bought something

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<v Speaker 2>and non converters people who visited but didn't.

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<v Speaker 1>Buy got it, and then we look at how those

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<v Speaker 1>two groups behave specifically on say the FAQ page. What

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<v Speaker 1>are we looking for? What's the red flag?

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<v Speaker 2>We're looking closely at bounce rate and exit percentage for

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<v Speaker 2>that specific page, comparing the two segments. If you see

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<v Speaker 2>that the non converters have a really high bounce rate,

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<v Speaker 2>let's say, seventy seven point nine to one percent, and

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<v Speaker 2>a high exit percentage maybe sixty five point three yer

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<v Speaker 2>percent on that FAQ page.

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<v Speaker 1>U huh?

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<v Speaker 2>Come compared to the converters who have much lower rates

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<v Speaker 2>on the same page. That's a huge alarm bell.

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<v Speaker 1>Okay. So if the people who don't buy are bouncing

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<v Speaker 1>or leaving from the faq page way more often, what

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<v Speaker 1>does it tell us. It sounds like the page isn't

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<v Speaker 1>helping them convert exactly.

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<v Speaker 2>It suggests the FAQ page for that undecided group is

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<v Speaker 2>acting more like a barrier than a help guide. The

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<v Speaker 2>information might be confusing, maybe too technical, maybe too long,

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<v Speaker 2>maybe doesn't answer their specific question. It's frustrating them enough

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<v Speaker 2>that they just leave.

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<v Speaker 1>Wow, So the page itself could be killing potential sales.

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<v Speaker 2>Absolutely. The actual conclusion here is direct you need to

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<v Speaker 2>go talk to whoever owns that content. Maybe it's the

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<v Speaker 2>legal team, maybe customer service, and tell them, look, this

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<v Speaker 2>page needs a rewrite, simplify it, make it clear. Cut

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<v Speaker 2>the jargon, because right now it's actively driving away people

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<v Speaker 2>who might have dot something.

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<v Speaker 1>Yeah, connect that specific data point right back to an

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<v Speaker 1>internal team's action item. That's powerful. Okay. Let's move on

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<v Speaker 1>from the marketing mix frame where it can dive into

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<v Speaker 1>some specific audience reports. These seem like they uncover really

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<v Speaker 1>interesting user behavior trends, but maybe get overlooked sometimes.

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<v Speaker 2>Oh definitely. Two really valuable ones are the Lifetime Value

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<v Speaker 2>LTV report and cohort analysis. LTV basically shows you how

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<v Speaker 2>much revenue a user generates over their entire life span

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<v Speaker 2>with you, starting from when you first acquired them. Okay,

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<v Speaker 2>and the source data we looked at showed something really interesting. Often,

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<v Speaker 2>the biggest jump in LTV happens really early on, usually

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<v Speaker 2>within the first three weeks after they become a user

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<v Speaker 2>or make that first purchase.

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<v Speaker 1>Three weeks that seems really fast. They kind assume LTV

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<v Speaker 1>builds up slowly over months. You know, as people repurchase.

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<v Speaker 1>Why such a quick spike, Well, think about it.

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<v Speaker 2>So I'm buy something, they're excited. Maybe they immediately realize

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<v Speaker 2>they need an accessory or a refill or a related product.

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<v Speaker 1>Ah, okay post purchase enthusiasm.

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<v Speaker 2>Yeah, or maybe they were Yshia's first time bought just

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<v Speaker 2>one thing, but now they trust you and they come

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<v Speaker 2>right back to buy more. It capitalizes on that immediate momentum.

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<v Speaker 1>So the best window to get a repeat purchase is

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<v Speaker 1>like right after the first one, within three weeks. What's

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<v Speaker 1>the strategy implication?

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<v Speaker 2>Then it means your remarketing campaigns targeting recent buyers don't

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<v Speaker 2>wait sixty or ninety days. You need to hit them

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<v Speaker 2>fast within those first three weeks. Capture that peak interest

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<v Speaker 2>period before it fades. Be aggressive right after that first conversion.

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<v Speaker 1>Okay, strike while the iron is hot.

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<v Speaker 2>Yeah.

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<v Speaker 1>How does cohort analysis play into this? Does it confirm

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<v Speaker 1>that urgency?

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<v Speaker 2>It does? Yeah. Cohored analysis groups users based on when

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<v Speaker 2>they first showed up, Like everyone who first visited in

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<v Speaker 2>the first week of June is one cohort right, and

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<v Speaker 2>the data consistently shows that the very beginning of any

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<v Speaker 2>cohort's interaction, that initial period right after they join, is

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<v Speaker 2>almost always the most profitable time. Revenue spikes right at

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<v Speaker 2>the start.

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<v Speaker 1>So the lesson from both reports is basically, grab the

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<v Speaker 1>momentum early, don't wait around hoping customers will just naturally

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<v Speaker 1>come back later for more.

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<v Speaker 2>You got it. The actionable insight is clear. You should

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<v Speaker 2>be thinking about prompting customers for related items, maybe upsells,

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<v Speaker 2>maybe bundles during that initial buying process. Make it part

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<v Speaker 2>of the first experience. Don't delay trying to maximize that

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<v Speaker 2>immediate revenue potential.

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<v Speaker 1>All right, that makes sense. Now, shifting from user behavior

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<v Speaker 1>to potential technical problems, the hidden stuff that kills conversions.

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<v Speaker 1>You mentioned using GA to find things like browser compatibility issues.

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<v Speaker 1>How does that work?

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<v Speaker 2>Yeah, this is a big one. You absolutely need to

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<v Speaker 2>check your reports segmented by browser and operating system, especially

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<v Speaker 2>look at bounce rate. This is where you can find

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<v Speaker 2>like silent killers, technical debt that's costing you sales without

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<v Speaker 2>you realizing it.

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<v Speaker 1>Okay, give me an example.

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<v Speaker 2>So let's say you look at bounce rate by browser

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<v Speaker 2>and you see Chrome is at maybe forty point four

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<v Speaker 2>zero percent. Pretty normal. But then you look at Microsoft

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<v Speaker 2>Edge and its bounce rate is fifty six point four

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<v Speaker 2>to four percent.

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<v Speaker 1>Well, that's a sixteen percent difference. It's huge.

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<v Speaker 2>Exactly, that massive gap is a major red flag. It's

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<v Speaker 2>basically screaming technical failure. It strongly suggests your website isn't

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<v Speaker 2>working correctly or looks terrible, or it's super slow, specifically on.

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<v Speaker 1>Edge, and every one of those users bouncing on Edge

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<v Speaker 1>is potentially a lost sale purely because of a technical glitch.

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<v Speaker 2>Precisely ignoring that sixteen percent difference, you're just letting revenue

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<v Speaker 2>walk out the door. The action here is immediate and technical.

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<v Speaker 2>You alert the web development team, like right now. Their

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<v Speaker 2>job is to figure out why it's broken on Edge

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<v Speaker 2>and fix it, improve compatibility for that specific browser to

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<v Speaker 2>stop the bleeding.

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<v Speaker 1>Yeah. That's about as direct a link from data to

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<v Speaker 1>dev ticket as you can get. Yeah. Okay. That brings

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<v Speaker 1>us to our last section, which feels like maybe the

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<v Speaker 1>most complex strategically, really understanding how different marketing channels work

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<v Speaker 1>together to lead to a sale using those conversion and

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<v Speaker 1>attribution reports.

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<v Speaker 2>Right, we often start with the acquisition reports, looking at

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<v Speaker 2>source medium traffic. But just looking at volume isn't enough

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<v Speaker 2>to really see the value. You need to compare at

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<v Speaker 2>least two metrics together, Like look at revenue that tells

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<v Speaker 2>you the quantity the total dollars, but also look at

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<v Speaker 2>e commerce conversion rate that tells you the quality how

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<v Speaker 2>efficiently that channel converts.

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<v Speaker 1>Okay, so compare dollars in efficiency side by side.

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<v Speaker 2>Yeah. So you might find, for instance, that Google organic

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<v Speaker 2>traffic brings in the most revenue overall. But then you

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<v Speaker 2>cross reference that with specific landing pages within organic search

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<v Speaker 2>to see which pages are not only driving traffic but

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<v Speaker 2>also driving high quality traffic that actually buys.

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<v Speaker 1>But then there's the problem of credit. Right. Someone might

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<v Speaker 1>click a Facebook ad, then read a blog post from

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<v Speaker 1>organic search, maybe see a tweet, and then finally days

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<v Speaker 1>later type our website addressed directly into their browser and buy.

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<v Speaker 1>Who gets the credit for that sale?

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<v Speaker 2>Ah, the million dollar question. That's where assisted conversions and

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<v Speaker 2>attribution modeling come in. Assisted conversions helps you judge the

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<v Speaker 2>role at channel plays.

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<v Speaker 1>How does that work?

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<v Speaker 2>It gives you a value. If a channel's asistic conversion

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<v Speaker 2>value is near zero, it usually means that channels the

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<v Speaker 2>one that closes the deal. It's often the last touch point,

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<v Speaker 2>think direct traffic or maybe branded organic search.

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<v Speaker 1>Okay, the closer, but if the.

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<v Speaker 2>Assisted conversion value is greater than one, it means that

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<v Speaker 2>channel mostly assists. It's part of the journey, maybe earlier

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<v Speaker 2>on introducing the customer or helping them research, but it's

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<v Speaker 2>not usually the final click. Think referrals or maybe generic

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<v Speaker 2>paid search or display ads.

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<v Speaker 1>So like referral sites might send people who are researching

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<v Speaker 1>and they get a high assisted score even if they

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<v Speaker 1>don't get the final last click credit exactly.

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<v Speaker 2>And here's the huge catch. Google Analytics by default uses

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<v Speaker 2>the last interaction model CULT setting yep, straight out of

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<v Speaker 2>the box, and it's like imagine analyzing a soccer game

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<v Speaker 2>but only giving credit to the person who scored the goal.

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<v Speaker 2>You completely ignore the amazing pass from midfield and the

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<v Speaker 2>setup assist right before it.

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<v Speaker 1>So the last click, the final touch point, gets one

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<v Speaker 1>hundred percent of the credit for the sale.

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<v Speaker 2>Correct. So in your example, the user typed your domain

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<v Speaker 2>name directly direct traffic right before buying. Under the default model,

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<v Speaker 2>direct traffic gets all the credit the Facebook ad they saw,

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<v Speaker 2>the blog post they read ignored zero credit.

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<v Speaker 1>Wow, that sounds like a really easy way to undervalue

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<v Speaker 1>channels that are actually bringing you customers. You end up

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<v Speaker 1>underfunding them.

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<v Speaker 2>It's a massive, critical flaw in relying only on the default.

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<v Speaker 2>If you go into the model comparison tool on GA,

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<v Speaker 2>you can switch models, compare that default last interaction model

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<v Speaker 2>to say, the first interaction.

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<v Speaker 1>Model, and what happens when you do that.

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<v Speaker 2>Often you see a dramatic shift. Direct traffic might get

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<v Speaker 2>tons of credit in the last interaction model because it

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<v Speaker 2>closes sales, but switch to first interaction and suddenly organic

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<v Speaker 2>search or maybe your social media campaigns light up. They

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<v Speaker 2>get the credit because they were the first point of

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<v Speaker 2>contact for users who eventually bought something, even if it

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<v Speaker 2>was weeks later via direct visit.

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<v Speaker 1>So the first touch introduces them, the last touch closes them,

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<v Speaker 1>and the default model only values the closer.

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<v Speaker 2>Pretty much. The implication for your marketing budget is enormous.

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<v Speaker 2>If you start looking at first interaction or maybe a

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<v Speaker 2>linear model that gives partial credit along the path, you

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<v Speaker 2>realize you need to fund the channels that introduce customers

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<v Speaker 2>that fill the top of your funnel, not just the

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<v Speaker 2>channels that happen to be the last click before checkout.

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<v Speaker 1>You have to fund the whole journey, not just the

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<v Speaker 1>finish line.

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<v Speaker 2>You've absolutely got it. So hopefully this deep dive has

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<v Speaker 2>give you a better framework moving beyond just looking at

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<v Speaker 2>traffic numbers. We covered that critical setup, the time zone fix,

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<v Speaker 2>the mandatory backup view. We talked about translating data into

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<v Speaker 2>strategy using the seven p's for location and service, how

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<v Speaker 2>to spot those technical red flags like browser issues, and

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<v Speaker 2>now how to use attribution modeling to really understand which

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<v Speaker 2>channels start the conversation versus which ones just closed the deal.

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<v Speaker 1>Yeah, and that attribution piece feels like the real game

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<v Speaker 1>changer here for a lot of people listening. I mean,

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<v Speaker 1>if you're just running on that default last interaction model

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<v Speaker 1>right now, you are almost certainly undervaluing and probably underfunding

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<v Speaker 1>those really important channels like your SEO efforts, your content marketing,

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<v Speaker 1>the things that actually bring most of your future customers

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<v Speaker 1>through the door for the first time.

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<v Speaker 2>Exactly. So here's a challenge for you listening. Go into

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<v Speaker 2>your Google Analytics, find the model comparison tool, so right

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<v Speaker 2>there under conversions, switch the view from last interaction to

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<v Speaker 2>first interaction, or maybe try linear and just ask yourself, Okay,

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<v Speaker 2>what channels here are clearly assisting my sales, maybe driving

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<v Speaker 2>initial awareness, but are getting almost zero credit right now

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<v Speaker 2>because they aren't the final click.

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<v Speaker 1>That insight alone could completely change how you allocate your

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<v Speaker 1>marketing budget tomorrow.

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<v Speaker 2>Absolutely, it could shift things overnight