Digital analytics will show you what is happening,
where and how much.
(uplifting instrumental music)
Third step in our conversion research process
is digital analytics.
So for most people, it's Google Analytics,
but you can use whatever other web analytics tool
that you know, is a better fit for you.
Number one thing in analytics is always
is everything being measured?
So every single thing that a user can do on a website
and every single thing that a user can experience
on a website should be tracked and measured.
So for instance, let's imagine that
we have an E-commerce site.
So what are all these things that people can do?
They go to the homepage, maybe there's a slider there
and they can manually click through different images.
Every time they interact with the slider,
we should mark it down.
We should fire an event.
That's what it's called in an environment,
let me fire an event
when they're interacting with a slider.
They're scrolling down.
Oh, how far down, 25% of the page, 50% longer?
We record that automatically.
There's a YouTube video maybe about a product overview.
Are they watching the video?
And if they are watching the video, how long?
Did they watch everything or just the first two seconds?
You need to measure that.
They're searching for something.
We should measure that.
They go to the category page, there are all this filters
so you know, size, filter by size, price, color,
blah, blah, blah.
Are they interacting with the filter?
Which one?
If they click add to cart, we need to click that.
And obviously, all the purchasing data,
the full funnel, they go to the cart page
and all the multiple checkout steps,
everything needs to be measured.
Because if things are not being measured,
we can't improve stuff.
So let's say that you have a product filter for,
filter products by color.
Now how many people are using that?
And if they are using it, what is the impact
on user behavior?
Like, are they more likely to purchase,
less likely to purchase, no difference?
And if like 1% of the users are using that filter
and if they are using it they convert worse,
it's probably a good idea not to have it to begin with.
But you wouldn't know that if you wouldn't track it.
Also, if you want to optimize your product page
so more people would click on add to cart
but you're not measuring specifically cart adds,
you can't optimize for it because you don't know
if the change you made increase cart adds or not.
So it's very important that we measure everything.
If you're using Google Analytics,
then all this measurement you can set up
at Google Tag Manager.
And it's not difficult at all.
In fact, if you're a marketer, you need to be able to use
Google Tag Manager on your own,
and you can set up all this tracking
without any developer involvement.
There are other tools that record more stuff out of the box
like Heap analytics for instance, measures everything
that needs to be measured right away automatically.
But you know, as soon as you go above 50,000
page views a month, it becomes rather expensive.
So first thing, make sure everything is being measured.
Everything that is important for you.
Number two, the data that we're measuring, is it accurate?
It's so often that the data that,
the funnels that have been configured and so on,
it's actually not true.
Sometimes you see websites where they have
really low bounce rate.
For instance, a bounce rate is like 1%, 2%, 3%,
people are like (chuckles) I'm the boss,
look at my bounce rate.
Whenever you see this, like no,
this is called broken measurement.
So if you have the GA code loaded twice on the page,
your bounce rate will be off and below 10%.
If you have an event that is firing
and it's not set to non-interactive,
engagement is being recorded, again your bounce rate
is artificially low.
So those things can screw up your data.
Also you see all the time
where people have a five-step checkout funnel
and it shows the kind of percent
people go through all the steps.
This never happens in real world so it's broken.
Or the final purchasing count
or the revenue does not match
what we're seeing on the backend
in our content management system
or on our E-commerce system.
So you need to verify that all these things are active,
that they make sense.
When you look at the data, like on average,
people add 77 products to the cart.
Really?
I don't think that's accurate, right?
Whenever you see something that's fishy,
it's like it's probably not true.
Also you see off the revenue being double counted.
Or if people for some reason can reload
their thank you page, which they shouldn't be able to do,
again the transaction is loaded multiple tires,
it can inflate the revenue and all these metrics.
And then there are also things like
you're using subdomain.
You know let's say your blog is on a subdomain
or you use multiple domains
like your shop.domain.com and blog. and then you have
your main domain.
If people navigate between those sub domains,
the sub domains you're tracking implement it,
or every time they switch between sub domains,
maybe it shows up as a new visitor
even though it came from a paid Google ad.
So that attribution gets lost if subdomain
and cross-domain tracking are not properly set up.
So it's very, very important.
If you cannot trust the data, you cannot be data-driven.
So these are very, very important first steps.
Now assuming that everything is all right,
we can trust the data, everything is recorded,
now for conversion optimization purposes,
digital analytics helps us identify
three very important things.
Number one is where are the leaks?
Every single page on your website is leaking money.
Meaning users are dropping off,
they're leaving your website.
And you have you know, some sort of
a typical customer journey, a funnel,
and so you wanna understand in which of these funnel steps
people are dropping off the most?
So typically, let's say on E-commerce cart page,
50%, proceed to checkout.
So if you're in your site, it's like 20% to 30%,
you have a big, big problem with your cart page.
Or on your product page, typically an average site
is like 10% of people add something to the cart.
If for you that's 1% or even less,
something's wrong with your product page,
either you have the wrong people on the site,
a relevance issue in your checkout.
So what is the checkout completion rate?
Should be like around 90%, which would be good.
If only 20% of people finally put in the credit card
and finalize the payment, again it's probably
the checkout that has the problem.
Of course these are all hypotheses,
we don't really know but we can,
we don't really know what the problem is
but we can see where they're dropping off, very important.
And again, you have to look at this
across devices separately.
So mobile separately, desktop and so on and so forth.
We also wanna see, look at correlation.
So the people who are buying something,
what other behaviors are correlating
with high purchasing rate?
Now what we you don't know is like
is it that people who were gonna buy anyway
use site search or they just know what they want?
Or was it that using the site search
helped them find more relevant products,
which increased their likelihood of making a purchase?
So if that is the case, we should try to get more people
to use the search and we will make more money.
Make the search box maybe more prominent and bigger
and so on and so forth.
So we wanna understand what are people doing
or not doing and how does that correlate
with conversion rate or revenue per user?
And of course, we wanna always segment
all the data per traffic source, per device,
any segmentation that might make sense for you.
If on your website people are logged in,
you might also be able to segment per gender.
Or if it's B2B, by revenue, business type,
what they do, all those things.
So very important.
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