Speaker 1: Welcome to the deep dive. Okay, so if your Google
Speaker 1: Analytics dashboard feels less like a useful tool and more
Speaker 1: like just this overwhelming wave of data hitting you, well
Speaker 1: you're in the right place today. Yeah, we're not just
Speaker 1: going to read off reports. We want to give you
Speaker 1: the tools, the actual techniques to turn all those numbers
Speaker 1: that raw traffic data into real actionable business decisions you
Speaker 1: can use like right away exactly.
Speaker 2: And our deep dive today it's really based on this
Speaker 2: idea of interpretation. We've got this comprehensive guide focusing on
Speaker 2: that because ga, I mean, it's way more than just
Speaker 2: counting visitors, right is grabbing all these details where users are,
Speaker 2: what systems they're on, how they found you, you know,
Speaker 2: their traffic source. It maps their whole journey on your site,
Speaker 2: and crucially, it measures if you're hitting your business goals,
Speaker 2: your conversion to your sales. That's the core.
Speaker 1: But okay, before we jump into the really cool stuff,
Speaker 1: the traffic insights, the sales strategies. The sources we looked
Speaker 1: at were super clear on this. If your basic setup
Speaker 1: is wrong, your analysis later on it's pretty much useless totally.
Speaker 2: You build on sand the whole thing falls down.
Speaker 1: So we have to talk about these like non negotiable
Speaker 1: first steps. The initial setup.
Speaker 2: Okay, yeah, first one seems simple, but wow, it gets
Speaker 2: messed up a lot. The time zone setting.
Speaker 1: Uh huh.
Speaker 2: And look, this isn't just like a personal preference thing.
Speaker 2: It's fundamental for the business side. You absolutely have to
Speaker 2: set that time zone to match where your business actually
Speaker 2: operates or maybe even more importantly, where your main customers
Speaker 2: are actually buying from.
Speaker 1: Okay, so walk me through that. Give us a concrete example,
Speaker 1: like what happens if you get that wrong? What's the damage?
Speaker 2: All right, imagine this. You're sitting in California, right, but
Speaker 2: all your marketing, your ads, everything, it's targeting the UK market. Okay. Now,
Speaker 2: if your gaview is still stuck on Pacific time, well,
Speaker 2: your reports for timing your market they're basically junk. Joe Dell, Well,
Speaker 2: think about London's peak buying time. Let's say it's I
Speaker 2: don't know, ten am to noon over there in GA
Speaker 2: dashboard set to Pacific time. That's going to show up
Speaker 2: as two am to four am.
Speaker 1: Oh wow.
Speaker 2: Okay, so if you look at that report, you might think, huh,
Speaker 2: people are buying in the middle of the night and
Speaker 2: then you shift your ad bids, your campaigns completely wrong.
Speaker 1: Yeah, based on totally misleading data exactly.
Speaker 2: But if you set the time zone correctly to London time,
Speaker 2: you actually see those critical mid morning buying peaks, You
Speaker 2: see the real behavior.
Speaker 1: Okay, that immediately changes things like admitting, maybe even staffing
Speaker 1: if you have support. Got it? So what's the second
Speaker 1: absolute must do? This one sounded serious like data integrity implications.
Speaker 2: Yeah, data integrity is key, and that means the backup view. Seriously,
Speaker 2: this isn't just nice to have, It's mandatory. Anyone developer,
Speaker 2: business owner, or marketer, anyone who's going to touch their
Speaker 2: GA data needs to immediately create a second view, one
Speaker 2: that's totally untouched, unfiltered, just the raw data feed.
Speaker 1: But why, like why the big rush? Can't you just,
Speaker 1: I don't know, undo a filter if you mess up.
Speaker 2: No, that's the critical point. You absolutely cannot undo it.
Speaker 2: The fundamental rule here is once you apply a filter
Speaker 2: or exclude bot traffic, or make any kind of configuration
Speaker 2: change to a gaview that original raw data for that period,
Speaker 2: it's changed permanently.
Speaker 1: Permanently.
Speaker 2: Wow, yep. So if you accidentally filter out, say ninety
Speaker 2: percent of your real mobile users, or maybe some experimental
Speaker 2: filter totally messes up your conversion tracking that historical data,
Speaker 2: it's gone forever.
Speaker 1: Unless you have that clean, untouched backup view.
Speaker 2: Exactly. You need that raw data safe. It lets you
Speaker 2: test filters, make changes in another view, and always have
Speaker 2: the original to go bear against or fall back on.
Speaker 2: It's your safety net.
Speaker 1: Okay, that's a really strong warning data safety net. I
Speaker 1: like that. So let's assume we've done our homework. We've
Speaker 1: got clean data. It's time synced correctly. Now, strategy, this
Speaker 1: is where it gets interesting, right, moving beyond just charts
Speaker 1: and integrating GA with actual business frameworks. You mentioned the
Speaker 1: seven piece of marketing.
Speaker 2: Yeah, exactly. The real power comes when you overlay this
Speaker 2: data onto established models like the seven p's. We can
Speaker 2: look at say place, market and location strategy, and people's
Speaker 2: service and user experience to see how the data drives
Speaker 2: real decisions.
Speaker 1: All right, Let's start with place, so figuring out where
Speaker 1: our visitors are coming from. But more than that, like
Speaker 1: how good are those visitors? Comparing volume versus actual quality?
Speaker 2: Percisely, we dive into the geographic reports. Let's say we're
Speaker 2: looking globally, the data probably shows the US is number
Speaker 2: one and just sheer volume, right, most clicks, most traffic
Speaker 2: makes sense. But then maybe we look deeper and we see,
Speaker 2: let's say Canada, maybe Canada is only I don't know,
Speaker 2: fourth or fifth in terms of raw traffic volume. Okay,
Speaker 2: but when we look at the e commerce conversion rate
Speaker 2: or eCCr, Canada is consistently ranked second highest.
Speaker 1: Whoa hold on ACCR. That's the percentage of visits that
Speaker 1: actually buy something, right exactly, So Canada's fourth in traffic
Speaker 1: but second in buying rates. That's a pretty big disconnect.
Speaker 1: What's the strategic move there?
Speaker 2: Well, the insight is clear, Canada isn't just traffic, it's
Speaker 2: high quality traffic. It's a high potential market. So while
Speaker 2: the US needs maybe the most basic attention just because
Speaker 2: of the volume, Canada shows this amazing efficiency.
Speaker 1: So you don't just chase volume there.
Speaker 2: Right, The action is probably to invest in optimizing the
Speaker 2: experience for Canada. First, maybe tweak delivery options for them,
Speaker 2: offer some specific local deals, capitalize on that high conversion
Speaker 2: intent they already have, rather than just trying to pump
Speaker 2: more raw traffic.
Speaker 1: Yet okay, so prioritizing quality over quantity in that specific
Speaker 1: market makes sense. Now, what if we drill down. Let's
Speaker 1: say we focus just on the US. How can GA
Speaker 1: help decide between, say, spending more on online ads versus
Speaker 1: maybe opening a physical store somewhere.
Speaker 2: Yeah, the domestic drill down often gives you these seemingly
Speaker 2: conflicting signals, which is actually useful. So we might find,
Speaker 2: for example, California brings in the most overall traffic, huge
Speaker 2: numbers like ninety seven thousand visitors, eight hundred and fifty
Speaker 2: five transactions, massive deman Okay, sounds great, but their conversion
Speaker 2: rate is only say zero point five to one percent,
Speaker 2: pretty low. Then you look at Texas, maybe less overall traffic,
Speaker 2: but they're conversion rate significantly better, maybe zero point eight
Speaker 2: one percent.
Speaker 1: Okay, So high volume, low conversion in California, medium volume,
Speaker 1: high conversion in Texas. Where does the money go? That
Speaker 1: seems tricky, it does.
Speaker 2: But the data suggests two different actions. For immediate online
Speaker 2: ad spend, you probably invest more in Texas. Why, because
Speaker 2: it converts efficiently. You get a better return on your
Speaker 2: ad dollars.
Speaker 1: Right now, Okay, boost the Texas online budget.
Speaker 2: But if the discussion is about opening a new physical store,
Speaker 2: you might actually choose California, maybe San Francisco.
Speaker 1: Well wait, why if the online conversion is lower there?
Speaker 2: Because that massive number ninety seven thousand visitors signals huge
Speaker 2: underlying consumer interest just raw demand. The fact they aren't
Speaker 2: converting online as well as Texas might point to other issues,
Speaker 2: maybe logistics, maybe trust. Maybe they just prefer to buy
Speaker 2: in person for that product. A physical store could solve that.
Speaker 1: Ah. I see, So the high visitor count justifies exploring
Speaker 1: the physical potential even if online isn't perfect yet, and
Speaker 1: the high conversion rate in Texas justifies immedia digital spending.
Speaker 2: There exactly two different strategies, two different investments, both informed
Speaker 2: by digging into the same report but looking at different metrics.
Speaker 1: That's really smart separating those decisions. Okay, let's shift gears
Speaker 1: to another. People thinking about service user experience, maybe testing
Speaker 1: if our help content like FAQs is actually helping.
Speaker 2: Right, this is a great place to use segmentation. We
Speaker 2: want to know if all the effort we put into
Speaker 2: making clear store policies or detailed faques is actually useful,
Speaker 2: or if it's maybe just confusing people or turning them off.
Speaker 1: So how do we segment for that?
Speaker 2: We create two segments, converters people who actually bought something
Speaker 2: and non converters people who visited but didn't.
Speaker 1: Buy got it, and then we look at how those
Speaker 1: two groups behave specifically on say the FAQ page. What
Speaker 1: are we looking for? What's the red flag?
Speaker 2: We're looking closely at bounce rate and exit percentage for
Speaker 2: that specific page, comparing the two segments. If you see
Speaker 2: that the non converters have a really high bounce rate,
Speaker 2: let's say, seventy seven point nine to one percent, and
Speaker 2: a high exit percentage maybe sixty five point three yer
Speaker 2: percent on that FAQ page.
Speaker 1: U huh?
Speaker 2: Come compared to the converters who have much lower rates
Speaker 2: on the same page. That's a huge alarm bell.
Speaker 1: Okay. So if the people who don't buy are bouncing
Speaker 1: or leaving from the faq page way more often, what
Speaker 1: does it tell us. It sounds like the page isn't
Speaker 1: helping them convert exactly.
Speaker 2: It suggests the FAQ page for that undecided group is
Speaker 2: acting more like a barrier than a help guide. The
Speaker 2: information might be confusing, maybe too technical, maybe too long,
Speaker 2: maybe doesn't answer their specific question. It's frustrating them enough
Speaker 2: that they just leave.
Speaker 1: Wow, So the page itself could be killing potential sales.
Speaker 2: Absolutely. The actual conclusion here is direct you need to
Speaker 2: go talk to whoever owns that content. Maybe it's the
Speaker 2: legal team, maybe customer service, and tell them, look, this
Speaker 2: page needs a rewrite, simplify it, make it clear. Cut
Speaker 2: the jargon, because right now it's actively driving away people
Speaker 2: who might have dot something.
Speaker 1: Yeah, connect that specific data point right back to an
Speaker 1: internal team's action item. That's powerful. Okay. Let's move on
Speaker 1: from the marketing mix frame where it can dive into
Speaker 1: some specific audience reports. These seem like they uncover really
Speaker 1: interesting user behavior trends, but maybe get overlooked sometimes.
Speaker 2: Oh definitely. Two really valuable ones are the Lifetime Value
Speaker 2: LTV report and cohort analysis. LTV basically shows you how
Speaker 2: much revenue a user generates over their entire life span
Speaker 2: with you, starting from when you first acquired them. Okay,
Speaker 2: and the source data we looked at showed something really interesting. Often,
Speaker 2: the biggest jump in LTV happens really early on, usually
Speaker 2: within the first three weeks after they become a user
Speaker 2: or make that first purchase.
Speaker 1: Three weeks that seems really fast. They kind assume LTV
Speaker 1: builds up slowly over months. You know, as people repurchase.
Speaker 1: Why such a quick spike, Well, think about it.
Speaker 2: So I'm buy something, they're excited. Maybe they immediately realize
Speaker 2: they need an accessory or a refill or a related product.
Speaker 1: Ah, okay post purchase enthusiasm.
Speaker 2: Yeah, or maybe they were Yshia's first time bought just
Speaker 2: one thing, but now they trust you and they come
Speaker 2: right back to buy more. It capitalizes on that immediate momentum.
Speaker 1: So the best window to get a repeat purchase is
Speaker 1: like right after the first one, within three weeks. What's
Speaker 1: the strategy implication?
Speaker 2: Then it means your remarketing campaigns targeting recent buyers don't
Speaker 2: wait sixty or ninety days. You need to hit them
Speaker 2: fast within those first three weeks. Capture that peak interest
Speaker 2: period before it fades. Be aggressive right after that first conversion.
Speaker 1: Okay, strike while the iron is hot.
Speaker 2: Yeah.
Speaker 1: How does cohort analysis play into this? Does it confirm
Speaker 1: that urgency?
Speaker 2: It does? Yeah. Cohored analysis groups users based on when
Speaker 2: they first showed up, Like everyone who first visited in
Speaker 2: the first week of June is one cohort right, and
Speaker 2: the data consistently shows that the very beginning of any
Speaker 2: cohort's interaction, that initial period right after they join, is
Speaker 2: almost always the most profitable time. Revenue spikes right at
Speaker 2: the start.
Speaker 1: So the lesson from both reports is basically, grab the
Speaker 1: momentum early, don't wait around hoping customers will just naturally
Speaker 1: come back later for more.
Speaker 2: You got it. The actionable insight is clear. You should
Speaker 2: be thinking about prompting customers for related items, maybe upsells,
Speaker 2: maybe bundles during that initial buying process. Make it part
Speaker 2: of the first experience. Don't delay trying to maximize that
Speaker 2: immediate revenue potential.
Speaker 1: All right, that makes sense. Now, shifting from user behavior
Speaker 1: to potential technical problems, the hidden stuff that kills conversions.
Speaker 1: You mentioned using GA to find things like browser compatibility issues.
Speaker 1: How does that work?
Speaker 2: Yeah, this is a big one. You absolutely need to
Speaker 2: check your reports segmented by browser and operating system, especially
Speaker 2: look at bounce rate. This is where you can find
Speaker 2: like silent killers, technical debt that's costing you sales without
Speaker 2: you realizing it.
Speaker 1: Okay, give me an example.
Speaker 2: So let's say you look at bounce rate by browser
Speaker 2: and you see Chrome is at maybe forty point four
Speaker 2: zero percent. Pretty normal. But then you look at Microsoft
Speaker 2: Edge and its bounce rate is fifty six point four
Speaker 2: to four percent.
Speaker 1: Well, that's a sixteen percent difference. It's huge.
Speaker 2: Exactly, that massive gap is a major red flag. It's
Speaker 2: basically screaming technical failure. It strongly suggests your website isn't
Speaker 2: working correctly or looks terrible, or it's super slow, specifically on.
Speaker 1: Edge, and every one of those users bouncing on Edge
Speaker 1: is potentially a lost sale purely because of a technical glitch.
Speaker 2: Precisely ignoring that sixteen percent difference, you're just letting revenue
Speaker 2: walk out the door. The action here is immediate and technical.
Speaker 2: You alert the web development team, like right now. Their
Speaker 2: job is to figure out why it's broken on Edge
Speaker 2: and fix it, improve compatibility for that specific browser to
Speaker 2: stop the bleeding.
Speaker 1: Yeah. That's about as direct a link from data to
Speaker 1: dev ticket as you can get. Yeah. Okay. That brings
Speaker 1: us to our last section, which feels like maybe the
Speaker 1: most complex strategically, really understanding how different marketing channels work
Speaker 1: together to lead to a sale using those conversion and
Speaker 1: attribution reports.
Speaker 2: Right, we often start with the acquisition reports, looking at
Speaker 2: source medium traffic. But just looking at volume isn't enough
Speaker 2: to really see the value. You need to compare at
Speaker 2: least two metrics together, Like look at revenue that tells
Speaker 2: you the quantity the total dollars, but also look at
Speaker 2: e commerce conversion rate that tells you the quality how
Speaker 2: efficiently that channel converts.
Speaker 1: Okay, so compare dollars in efficiency side by side.
Speaker 2: Yeah. So you might find, for instance, that Google organic
Speaker 2: traffic brings in the most revenue overall. But then you
Speaker 2: cross reference that with specific landing pages within organic search
Speaker 2: to see which pages are not only driving traffic but
Speaker 2: also driving high quality traffic that actually buys.
Speaker 1: But then there's the problem of credit. Right. Someone might
Speaker 1: click a Facebook ad, then read a blog post from
Speaker 1: organic search, maybe see a tweet, and then finally days
Speaker 1: later type our website addressed directly into their browser and buy.
Speaker 1: Who gets the credit for that sale?
Speaker 2: Ah, the million dollar question. That's where assisted conversions and
Speaker 2: attribution modeling come in. Assisted conversions helps you judge the
Speaker 2: role at channel plays.
Speaker 1: How does that work?
Speaker 2: It gives you a value. If a channel's asistic conversion
Speaker 2: value is near zero, it usually means that channels the
Speaker 2: one that closes the deal. It's often the last touch point,
Speaker 2: think direct traffic or maybe branded organic search.
Speaker 1: Okay, the closer, but if the.
Speaker 2: Assisted conversion value is greater than one, it means that
Speaker 2: channel mostly assists. It's part of the journey, maybe earlier
Speaker 2: on introducing the customer or helping them research, but it's
Speaker 2: not usually the final click. Think referrals or maybe generic
Speaker 2: paid search or display ads.
Speaker 1: So like referral sites might send people who are researching
Speaker 1: and they get a high assisted score even if they
Speaker 1: don't get the final last click credit exactly.
Speaker 2: And here's the huge catch. Google Analytics by default uses
Speaker 2: the last interaction model CULT setting yep, straight out of
Speaker 2: the box, and it's like imagine analyzing a soccer game
Speaker 2: but only giving credit to the person who scored the goal.
Speaker 2: You completely ignore the amazing pass from midfield and the
Speaker 2: setup assist right before it.
Speaker 1: So the last click, the final touch point, gets one
Speaker 1: hundred percent of the credit for the sale.
Speaker 2: Correct. So in your example, the user typed your domain
Speaker 2: name directly direct traffic right before buying. Under the default model,
Speaker 2: direct traffic gets all the credit the Facebook ad they saw,
Speaker 2: the blog post they read ignored zero credit.
Speaker 1: Wow, that sounds like a really easy way to undervalue
Speaker 1: channels that are actually bringing you customers. You end up
Speaker 1: underfunding them.
Speaker 2: It's a massive, critical flaw in relying only on the default.
Speaker 2: If you go into the model comparison tool on GA,
Speaker 2: you can switch models, compare that default last interaction model
Speaker 2: to say, the first interaction.
Speaker 1: Model, and what happens when you do that.
Speaker 2: Often you see a dramatic shift. Direct traffic might get
Speaker 2: tons of credit in the last interaction model because it
Speaker 2: closes sales, but switch to first interaction and suddenly organic
Speaker 2: search or maybe your social media campaigns light up. They
Speaker 2: get the credit because they were the first point of
Speaker 2: contact for users who eventually bought something, even if it
Speaker 2: was weeks later via direct visit.
Speaker 1: So the first touch introduces them, the last touch closes them,
Speaker 1: and the default model only values the closer.
Speaker 2: Pretty much. The implication for your marketing budget is enormous.
Speaker 2: If you start looking at first interaction or maybe a
Speaker 2: linear model that gives partial credit along the path, you
Speaker 2: realize you need to fund the channels that introduce customers
Speaker 2: that fill the top of your funnel, not just the
Speaker 2: channels that happen to be the last click before checkout.
Speaker 1: You have to fund the whole journey, not just the
Speaker 1: finish line.
Speaker 2: You've absolutely got it. So hopefully this deep dive has
Speaker 2: give you a better framework moving beyond just looking at
Speaker 2: traffic numbers. We covered that critical setup, the time zone fix,
Speaker 2: the mandatory backup view. We talked about translating data into
Speaker 2: strategy using the seven p's for location and service, how
Speaker 2: to spot those technical red flags like browser issues, and
Speaker 2: now how to use attribution modeling to really understand which
Speaker 2: channels start the conversation versus which ones just closed the deal.
Speaker 1: Yeah, and that attribution piece feels like the real game
Speaker 1: changer here for a lot of people listening. I mean,
Speaker 1: if you're just running on that default last interaction model
Speaker 1: right now, you are almost certainly undervaluing and probably underfunding
Speaker 1: those really important channels like your SEO efforts, your content marketing,
Speaker 1: the things that actually bring most of your future customers
Speaker 1: through the door for the first time.
Speaker 2: Exactly. So here's a challenge for you listening. Go into
Speaker 2: your Google Analytics, find the model comparison tool, so right
Speaker 2: there under conversions, switch the view from last interaction to
Speaker 2: first interaction, or maybe try linear and just ask yourself, Okay,
Speaker 2: what channels here are clearly assisting my sales, maybe driving
Speaker 2: initial awareness, but are getting almost zero credit right now
Speaker 2: because they aren't the final click.
Speaker 1: That insight alone could completely change how you allocate your
Speaker 1: marketing budget tomorrow.
Speaker 2: Absolutely, it could shift things overnight