Saas Analytics Stack Recap Part 5

We have come to the end of our Saas Analytics Stack series. Ruben will be giving us a quick recap of everything we have discussed of the SaaS data flow. Plus some tips on how to manage your stack.

? Links:

SaaS Analytics Stacks Playlist

Amplitude Analytics:

Practico Analytics:

Welcome to our last video here a conclusion video in our SaaS analytics stack series. And just, you know, to look at everything we cover in the last few videos, you know, we start by looking at the acquisition data. This is typically everything up to the signup event. And we, you know, we use tools like segment, we introduced how segment works, what’s the main benefit of it. Tools like Google Analytics, which you are likely familiar with. And then eventually went through as a tool like amplitude. You know, some behavioral tools, you have amplitude, you got mixpanel, you kissmetrics, you have lots of options here.

Then we look at the onboarding events, right. We look at, you know, once the users sign up for your product, we want to track all the different steps that user must take to be onboarded successfully, right. And we typically do that inside some like amplitude using the funnel analysis report. And lastly, our last video, we looked at some of the critical actions. So some of the behavioral events and retention for our users, that is, are they using the product, are they adopting features, are they being retained, and we finished by looking at some of the revenue data. So these are some of the specific SaaS metrics that you might care about, like monthly recurring revenue and churn.

And some of the best ways of tackling that. So this this can be a great stack. So you know, in our stack we look at we look at a segment, Google Analytics, amplitude and then some kind of payment processor like recurly or charge me with some data box. So we’ve got about four tools there, give or take maybe five, if you include some of the qualitative data here I mentioned here like session recordings, it’s you know, it’s a relatively small stack. Really the last piece of advice I just want to mention here is to keep your stack simple you know, start small and then build upon it. It’s very easy to where the with 5-10 tools all at once especially with the using something like segment the additional tools, the cost of it can seem quite low you know, just simply make a few clicks and you enable and now you have any tool.

But always remember that there’s a big cost when it comes to learning tools, maintaining them especially if you have a team right. Getting multiple people in your team to learn a new tool can be quite time consuming and you don’t want to make them learn five, 6, 10 different tools on a regular basis all the time. So keep it small, keep it simple. And for your event data, you know, start with a handful events you know typically sounded like five or 10 events is something that can be done in an initial pass and then you build upon it. You’re always going to have gaps in your data understand that but you want to be able to tackle some of the your your your biggest challenges, or your biggest priorities and then slowly build upon that right. That can be a great strategy for success as you build out your analytics stack or your proof whatever your analytics that guest right now. I hope you enjoy the series. If you have any questions just post in the comments. I’ll be jumping through the comments and respond as best as I can. And I’m sure Julian can also offer some answers whenever possible. Once again, my name is Ruben and I’m the founder Practico Analytics and I had a grip less going over this stack series with you guys. Thank you.


Retention and Revenue Reporting in SaaS Analytics Stack Part 4

We are down to the last 2 steps in the Saas Analytics Stacks. And today Ruben will be discussing the critical events and actions after a user successfully onboarded and how to use segments and amplitudes to track data for user retention and revenue reports.

? Links:

SaaS Analytics Stacks Playlist

Amplitude Analytics:

All right, and welcome to our last video series, well our last major video. We will have a short conclusion video after this.

In the second video after the intro, I talked a little bit about the acquisition data in our here in our SaaS stack or SaaS analytics stack that we’re looking at here. Then I talk about onboarding in the last video. And then this video, I actually I’ll cover two things. I’ll cover the critical action section. This is typically where you find things like behavior analysis, retention, and then a little bit of the revenue, right?

SaaS is, of course, a special case when comes to revenue, just love very specific SaaS metrics, like monthly recurring revenue, annual recurring revenue, and so on. So we’ll talk a little about how to track those and what’s the best way of have a tool to the stack to look at those numbers.

Let’s start with critical actions.

And critical actions really is at this point, the user has onboarded, let’s say they onboarded successfully. And now we want to understand how they actually use the product, right? Are they using it in a regular basis, whether it’s daily, weekly, or monthly.

And are they retained. Are they coming back, or do they love the product, right.

And this can be a very broad area but we’ll focuses specifically on on critical actions. So some of the most popular things that users love about the product, from a product perspective, or from a product manager perspective.

This can be things like feature adoption and things like that it can influence your product roadmap.

And then retention which is our are they, you know, are you sustaining each of the product and of course, retention, very similar, but look at a few reports.

Now for events, it’s quite broad. So we have critical events and action. So this means that were some of the critical actions for your product, they need to track here.

In our example, reason of a job search site and we’re looking at employers specifically. So employers were posting jobs to hire people.

In their case, they’re critical actions are things like the job posted, the end of the job has successfully filled a job, and things like that. So there’s you know, there’s always a handful, maybe five or 10, critical actions that really explain or understand what the product is, and what users can do with the product.

Then for user attributes, we’re looking at some behavioral traits. And this will also be very specific to your product. For example, this could be things like, how many jobs has this company posted in total, right? How many jobs have been canceled, how many jobs have been successfully filled.

And then how many people have been hired, maybe how long the context of events, right?

These are traits that explain the behavior of a given user a company within the product, right?

From a quality perspective, you may also see something like NPS score, again, we’re looking at customer satisfaction, or how much people love the product.

An NPS score can be an easy way to get started in this kind of world.

From a technical perspective, once again, will keep building on this idea that we talked in last video about tracking data from the server side, likely be the best long term option for your company. But if you can’t, and if you’re in if you want to submit that’s a little bit quicker, a little bit faster, client-side can still work, of course.

In our stack will be the same as before, we’ll use segment and we’ll use amplitude to track our data.

So once again, here segment, I cover quite a bit the acquisition video, but we’re going to flow the data from either get from a JavaScript stores like a client side implementation, or maybe from a server side into the different analytics tools here.

Now, the first report we can look at here is an event segmentation report. And we’ll look at just the most popular events within this account. Right. And when we look here, we get to see events like you’re, you know, searching for candidates, they on board and so on. Right?

So we have a few different things, things that are most popular actions here. And we can filter things out, you know, we might not really care about the viewer loaded page.

We may care about a job application, or searching and and so on. Right? So this case give us a sense of where the most popular actions users are doing within the product in comparison to other actions, right? Are they searching for candidates as much as they’re posting jobs? Or are they successfully filling jobs as much as they’re posting them, right.

And here we are using little bit some of the filters available to us. And we’re looking at only employers, and specifically if person signed up. We know we can have employers who actually didn’t sign up at all right? We just started the funnel and it just never completed, right. That’s one event. And there’s a few different things you can do here right. We can do different ways of filtering and slicing the data. But we just really want to get a sense of where some of the popular actions within the product.

Then the second report, we have retention report. And this report, we can look at a corporate analysis, right. So we’ll start by saying show me all the new users.

And then show me the users that return and perform any active event. So an active event can vary for your product. But this is typically some kind of critical action, it may be posting the job, it might be filling a job, whatever that is. Viewing the page, firing the page view, those things typically don’t qualify here.

In this case, down here, we of course have different user types, we’re actually only looking at employers. We can also do it through the filter type, as we said before. Or we could do it like this, this you get roughly the same result. And here we see our retention curve, you might be a little more familiar with a more cohort analysis curve, right?

Where we have a numbers here, you know, how many users filter criteria on on this week? And then was the retention over time?

And same thing. Once you define the retention, and the frequency that is, you know, what makes the user retain, right?

And then how what was the frequency of users for your product.

That is should they be used on a weekly basis, monthly basis and so on. A typical example here is imagine something like a food ordering app, right, like skip the dishes, fedora, or whatever it would be available, wherever you’re watching this.

You know, for user to be retained for this kind of product would they have to order food once a week, once a month, once every two weeks, once a day, Right? You kind of figure out what is the expected usage for the majority of the users and you of course, have power users who are going to do them what’s more often, and maybe you should do it less. But this starts to play a little bit into , you know, this retention report.

One more thing I was going to show you here on the behavioral traits. So we have our default amplitude properties. But then we have things like this. So this is one of the behavioral traits that we might have, you know, this product, for example, has credits, there’s people buy credits to be able to post jobs. So you can have credits, you know how many jobs slots are remaining. So we have that limit on how many jobs they could post any given time, total job post, total jobs slots, things like that, right? So these are all behavioral traits that are very specific to this product. But from here, we can start to build more constant segmentation, right? Let’s see for users to post them more than 10 jobs, how’s the retention change compare to users who post less than two jobs.

Those are the kind of questions you can start to explore once you have the data in order.

Then our last step here in our SaaS Analytics stack is our revenue data. Right. And in here, we you know, for events, we’re really looking at some kind of subscription event, right?

So this is our user who starts a subscription. And of course, the subscription by renew on a monthly basis or annual basis or any other kind of frequency you might be looking for.

Typically, for that event, we’re looking at things like what’s the plan there on, what’s the amount, the frequency,

Sometimes it’s even helpful to track the old plan if they’re upgrading, right, or if they’re upgrading or downgrading. And I can give you a way to that track how many people are upgrading or downgrading, what’s the most typical upgrade, downgrade path and so on.

And for user attributes, then we’ll track also tricks around subscription. So these are things like what’s the plan there on, what’s the frequency, how much revenue have they had with the company, all these kind of things. So these are all very revenue specific trades. And subscription data by far and large should be tracked server side, right? We talked about here where you may have an option depending on what kind of capacity you have with your team plus description data ready for the best acquisition should be done server side. And this typically can be done by firing from an API. Sometimes you have integrations from popular payment processors but somebody should be aware of. In terms of a stack, we got segment, we got amplitude, and we have recurly here as an example. But there’s quite a few options here recurly charge me. We’re actually be looking at one called Data box. In essence, these are tools that help you process payments. And they also give you a lot of metrics out of the box. So there’s a lot of payment processors like recurly charge me and so on. But they’re designed for SAS companies.

So they’re able to create a lot of analytics dashboards out of the box for you and calculate logic for you. Typically the logic around when the user returns, when when the user renews, doesn’t renew, all this kind of things can be quite complex is still up in the beginning. And it’s really not needed because you can get them with a few clicks, right.

So our typical report will look something like this, right, we can see how many new subscriptions happen. And this month, how much new monthly recurring revenue, total subscriptions,

total monthly recurring revenue, why we get some comparisons right here, right compared to the previous month, break down by plan, want to grow by plan, and so on, right, we get some other metrics. So these are all things that you could go out and find the formula online for this and work them out and so on, and likely even recreate a lot of these metrics insights into like amplitude.

But it’s just in the beginning, you’re better off just taken some kind of pre-built report and get them done. And as I mentioned, you can you can build upon them.

In amplitude, what the you know, you can track the revenue data in amplitude and that really is just to be able to tie it into everything else.

So you have all your users who onboarded, all the users who are retained, can you then confirm their the revenue, Right? Can you say, you know, are most active users are also our users with the most revenue? Or maybe there’s maybe it’s not right. So in here, you’re taking the revenue data, the revenue events to analyze the product and product usage.

But when it comes to revenue metrics, like monthly recurring revenue, and so on very specific SaaS metrics, you you likely be better off looking at something like a custom dashboard from your payment processor.

That’s really it. We’ll make a short conclusion video to finalize everything. But that’s the final two steps in our SaaS analytics Stacks, our critical actions and our revenue.

Hey, Julian here from measureschool. If you want to follow along with the next video in this series, then we have it linked up right over there. And if you want to learn more about the data-driven way of digital marketing, then definitely subscribe to our channel right over there. Now, my name is Julian, see you in the next one.

Amplitude Analytics for Onboarding Tracking | Part 3 SaaS Analytics Stack

The third step in the Saas Stack Analytics process is onboarding. Ruben will be walking us through the onboarding metrics we should track and give us an overview of how we can see those in Amplitude Analytics.

? Links:

SaaS Analytics Stacks Playlist

Amplitude Analytics:

All right, let’s keep moving on to our third video in our SaaS analytics series. In the second video, we cover the signup and login so basically the acquisition steps. And in this video, we’re going to focus on the onboarding. So in step three here. Now, onboarding for a typical SaaS product typically means a series of steps after user signs up. So they create an account. And then they have to complete some kind of funnel to be able to use the product, right, this is where they might create a profile, they might upload photos, they might go to a product tour, it kind of varies. And then for companies, this might mean a completely self-designed, self-contained funnel, right. With special screens, special pages that if the user doesn’t complete, they can’t actually use the product. Or it might mean being guided through just the regular product through like tool tips or things like that, as a lot of different variation you can take. Nonetheless, in our analytics, we want to know the performance of this onboarding funnel, right.

So when we look at the events that we want to track here, we’re looking at the what we call onboarding events. So let’s imagine that you have a funnel, and the funnel has five steps, right.

And for the user to be considered onboarded they need to complete all the five steps, right, and they come right after, right. So we’ll have five events here. And under each event, we can have, you know, onboarding, step one, step two, step three, so on and we have any relevant properties under each event.

Then for the user attributes, it’s always helpful to track the onboarding completion that has the user onboarded successfully. This eventually becomes useful in things like notifications, where you can say, Okay, let me send a special kind of messages, or stop sending special kind of messages to anyone who has already onboarded, right. It’s common for SaaS products to use, let’s say email drips to complement any existing onboarding and funnel so you can stop those drips by having a just a flag, a true or false slide here.

And then the other thing that we might see here into the qualitative data will be something like user record this, right.

So if we are to watch users go to the funnel can be quite helpful. It can give you a sense of worries against stuck. Typically, you know, when we see a lot of issues and onboarding funnels it tends to come to expectations or the design that UX design of the funnel itself.

That is a user sees something and then not sure what that means, they’re not sure why they should do it, or why they should care, you know, why do you think the create profile right now, they can just do it later, things like that. So session recording should give you a sense of that because then you’ll be able to see if user is spending an unusual amount of time on a step that in your perspective as simple, just a couple of input fields, enter information, move on and the user gets stuck. So that would be actually quite helpful, right.

In this case, for data tracking this actually done on server side, and this one should be the recommendation.

So in the acquisition side, we saw a lot of things that needed develop client side just to be able to manage at this properly. In this case, we can adjust our moving server side. And that can be quite helpful especially if you have a cross-platform product that you have a product of mobile web, iOS, Android, so on.

Trying to implement everything always client side will become a little tedious, especially if the same action can happen on any platform. So this is where we can start to build some expertise around tracking events for the server side. If you can’t do that if client side is your recommended approach, where you want to do, that’s fine. Just be aware of that potential limitation.

In our tool stack is similar to what we saw before, we have segment, of course, we still want to funnel all the data through segment. And then the main tool that we use for analysis here will actually be amplitude, right, which we saw a little bit before the acquisition video.

So here’s a segment again I won’t cover this in detail cause I covered this quite a bit during the acquisition. But same thing today will flow here what is from a client side of JavaScript, or a new source, like a, like a server-side source, and it will flow down into all the different tools. And we specifically care about amplitude in this case as our behavior analysis tool.

So jumping into amplitude, really, the report here that we care about is the funnel analysis report.

And it looks like this before we show you how it’s built. So we have the series of steps. And then we can see the drop off from step to step right. Now, in this case, our steps, right, user starts the sign-up, they complete the signup, we saw this event quite a bit during the acquisition video, right, as a pretty critical event.

And then we have all the onboarding events. And you can see here, we like to prefix events with the word onboarding. And that just makes it easier to find, it organizes nicely, actually, a lot of tools will organize events alphabetically, so you can find them on one section. So this is just a minor detail that becomes quite helpful when you start working through data. So we have what the you know what the steps are. And, of course, these are relevant to the product itself, that we’re looking at this job search product.

And then the last event here is profile approved, right? So we have 3, 6, 7, 7 onboarding events in this case, right?

Something is about amplitudes that we can actually look at the events in any order or is this specific order, right. I have seen probably quite a bit of issues when you actually have to force an order, right, when it’s okay, users have to complete this one, and then that one, and then this one that one. Basically, what that means is, if for some reason, you have an optional event. Let’s imagine that you’re asking the users to enter a credit card, but optional, right, you have an event called onboarding enter credit card, but they can skip it, then all of a sudden now the steps down below might actually they will be counted, if you’re forcing it to be very specific order, right, because there it’s looking for that flow, and if any event skipped, then your numbers will be slightly off. So any orders can be helpful because I know users can technically go back and redo events, and they’ll still be counted as a whole, okay.

In the funnel itself, this is what it would look like, right. So we see a number then we see the drop-off, you know, the biggest drop of course when the user starts to sign up, but they’ll complete it. And then we go to the onboarding itself, in this case, is onboarding, this is actually quite, quite simple, quite well done. You can see there’s not a big drop offs, except maybe from the last step, or the second last step to the very last step, right, to get the profile approved, right. And, of course, just before we can click into things, and we can see, you know, the 57 users who complete it. We can also see the 26 users who didn’t complete it. And we can see some of the other things. Some of the interesting actually something like user pass.

So we can see, you know, for users who complete a profile approved, what do they do after, right, what were some of the path it take after. And that path explosion can be can start to help us understand maybe a better onboarding flow, right, which is typically in a typical one of the goals you start with, with something that you know, works, and they want to move on to something that might be a much better optimize, Of course, before here will have conversions by dates, right.

We have average time in between steps, right, and so on. So we’re able to segment things here.

That’s really it, we can now see conversion over time. So we can see the overall conversion actually of the funnel over time. So if you see if that’s, that’s getting better or getting worse over the last 30 days, 90 days, 6 months or so on. And then time to convert is typically also a helpful report, we like to see, you know, are you just taking a long time to go through this funnel, or is this something happening with a few minutes, and this is something that you’d likely want to play around a little bit of dates, you might have outliers are really bring it down your averages status. You might find 95% of all your users complete the funnel in a few minutes.

But then you have 5% of very small amount of users who take 25 days, right. That is they started and they come back 20 days later. So you want to play around here a little bit of time to get a sense of how long it’s taken to, to convert, right.

And we gonna foresee the conversion time between specific steps too.

So we have a lot flexibility here. And of course, just like before we can segment this even further, we might want to look at only users on iOS or only on Android or web or maybe from a specific country and so on. So all our segmentation options that we cover a little bit in the first video was are still available here and this is it right. This is this what onboarding is for our series and what the goal is in terms of our data.

Hey, Julian here from measureschool. If you want to follow along with the next video in this series then we have it linked in right over there and if you want to learn more about the data-driven way of digital marketing then definitely subscribe to our channel right over. Now, my name is Julian. See you on the next one.

Acquisition Analytics for SaaS ( and Amplitude) | Part 2

After a brief overview of the SaaS Analytics stacks, let’s proceed to the first step of the SaaS process, the Acquisition. Here is where measuring the Sources and Signup of the prospect and later customer. Ruben is going to show you, Google Analytics, and Amplitude.

? Links:

The SaaS Analytics Stack with (feat. Ruben Ugarte) | Part 1


Amplitude Analytics:

SaaS Analytics Stack Playlist

Hi there, and welcome to our second video in the series. Now, the first video was really just an introduction video and give you a sense of what we’re going to be talking about. In this video, we’re going to dive straight into acquisition, the very first step in our data flow chart from before. Really what we’re talking about the first two rows, so marketing website visits and then the signup step. And sign up, it’s really likely the first major step that we will be looking at here, the very first major action they user has taken as they create an account, they provide an email this our trial where a sign that might be for your product.

So let’s start by looking at the very first step marketing website visit. And this is just a user coming to your website and viewing a page on your website. So it might be like a landing page, or a blog, or a pricing page, or something else.

At this stage, we want to track a regular page view event. And this just fires every time a user loads a page, any page on your website.

Alongside that event, we want to track things like the geographic data, like the city of region, maybe some demographic data, which you may have seen Google Analytics, things like age ranges, gender and things like that. At the user level, we want to track things like UTM parameters. And this is what we use to track marketing campaigns. So if a user come from a Facebook ad, or organic search, or display

ad, or retargeting this how we’re going to store that information. So we’re able to have a sense of things like what was the last touch users had before they signed up, or what was the first touch, or if you’re a more higher level, or you have higher volume of traffic, you might be looking at multi-channel attribution. So how different channels play a different role in the conversion.

And same thing, geographic and demographic. Now, you’re going to see sometimes that data between the event data, which are actions, and then the user data might overlap like we see here, track data at the right section, but we’re still going to track it. At sometimes this will function different roles. And it’s just regular good practice to even if it feels like it’s it’s duplicating the functionality that you might already have.

Now different tools. The very first tool we’re going to look at here is and segment is really at the abstraction tool. The most common use case for segment is imagined that you have a multiple tools on your website. So you have analytics, you have the facebook pixel, you have intercom, and you have amplitude, which we’re going to talk later on. So those are five different tools. So it’s five different snippets that are being loaded. And they will really could use the same amount of data, right. So if you have event data you want to track, you can really send it to all them, you know, Facebook will use it for conversions. Same with intercom, same Google Analytics, and so on.

So stuff manually implemented every single tool and every single event data over and over again. So doing it for Google Analytics, and then Facebook, and then intercom, and then amplitude, and then Google.

You can do it once for and it’ll translate it for you, right. So they’ll they’ll take the day you send them, and they’ll translate in the back end for Google Analytics for Facebook, and so on. So it gives you a little bit of obstruction, as we like to say.

And it also helps you change tools, right. Let’s say, eventually, you want, you don’t want to use intercom anymore, you want to move from that to custom audio, for example. Now, instead of having to rewrite all your calls from scratch, the stuff that you wrote, now, you can just simply enable or disable each of those tools in the in the segment back end. And the data will just keep flowing naturally because again, you didn’t write any specific codes for intercom, you wrote code for segment. Now, let me show you a little bit about how that works here.

So we’re in here for the segment back end. And all this data mobile show is from a client, they have a marketplace for employers and candidates, things like, or a job posting site, really. So they have we’re looking at their data. So let’s actually go back one step.

So segment, you wanna think about two concepts, or think about things from a two concepts. You have sources, what day is coming from. So it could be a website, an app, maybe some kind of software, like Facebook, maybe you want to pull cost data from Facebook, or campaign data. And we have this niches where you want to send the data to right, you want to send to Google Analytics, you want to submit to amplitude, mixpanel, or 250 plus options that can be listed here. So in this case, we have a JavaScript library. So it means we’re doing something on the website.

And we have five destinations. So if you click into the the JavaScript, here we can see all the data is flowing through segment, right, if you go to a debugger, we can see data in real time. So here’s an event, we have the event name, which calls for candidates, and then we have all this event properties are flowing through, right. We can also enable, for example, this is a regular page view. And here we have an event which could load the page, we have information about the the URL of the page, the employer, the page name, and so on. So this data is flowing through, right, because you build the schema. So we see all the events that have actually formed through overtime, and just some other, you know, data purpose events, when it is when last seen the candidate, and so on.

And then let’s go back here, all the data is flowing to destination. So every destination has specific settings. So if you have been Google Analytics, for example, you’ll see, you know, you have an option to the tracking ID or the mobile tracking ID or server tracking ID. And then you have basically every possible setting, you might want to enable that you might have the option of doing it, if you were manually implementing Google Analytics. You get option here inside segment, right. So you can do custom dimensions, you can do custom metrics, you can do enhance eCommerce, you can do user ID, you can do custom sample rates, pretty much every possible option can be done here, right. So you don’t have to manually edit the Google Analytics code, you can just enable or disable something and say, you know, you might use a sample rate, this will typically done behind the code level, maybe just simply enter the number one here and change it. And that’s, that’s the power of it. And then we’re going, of course, add destinations with your route destinations. And that’s and the data will still flow through into any destination cause segment will convert it for you, right.

So that’s really the power of segment. So it’s quite handy. Now, there’s no charts here, there’s no visualization, there’s nothing, there’s an analysis and segment, it’s just a pipeline. Data is just flowing through.

And we’re going to do all our analysis inside this specific tools, right.

So if we jump into Google Analytics, we’re going to see the data forms of segment into Google Analytics from the website. And we’ll see typical things you might expect here, right, we still have the acquisition reports, we can see user data, new user, session rate, pages per session, all the typical stuff you might expect, right. So that’s, that’s the first stage. So we have two tools we have segment and we have Google Analytics. And now we can analyze all the marketing web services. The second stage we will look at is the sign up, it’s just a bit special because it’s really that transition period between acquisition and onboarding, which we’ll talk in the next video.

The signup stage, the in here, we want to have a signup event. So this would be like a signup completed, or something else. And typically will track things like name and email, maybe during the trial, like a 14 day trial, 30 day trial, maybe they don’t have a trial, they actually bought a plan ahead of time, right. So if they were forced to enter a credit card, so that could be store here. In the user level of the user attributes, we have things like name and email, trial status when this was created. And then we have a special thing called user ID. And this is really the ID of the user that we want to track. So at this point, users are not anonymous like they were in the previous stage. Now, we can actually know who they are, right? So it’s John Smith, and their ID is 1234 now we know that so now we can actually store that.

Another user ID, you know, there should be a you want to use a permanent ID so typically look like a database, it is pretty good. Emails work is technically an ID, but he was can change. So then as permanent as something like a database ID. So your development team can help you here.

Then, in terms of tools, we still have segment with Google Analytics, you know, Google Analytics can get that sign up event. So for example, once we have to sign up event, we can have a goal. So we look at, you know, registration, and then we can see the different conversion here for different channels against that specific goal. So this is that event flowing into Google Analytics. And then we can also flow into a new tool. And this case will be flowing to amplitude and amplitudes is a category of tool called behavioral analytics tools.

And this just helps me analyze what’s happening in your product. So questions like onboarding performance, retention, feature adoption, these are all things that this category of tools can offer can offer here you have amplitude, you have mixpanel, and a 20 plus options that we could probably come up with, and that are supported by segment. So you do have quite a bit.

For examples we’re going to use, we can use amplitude and we’re going to show you one report here. So when great charts ministry, the segmentation see, we have quite a few things here. And a segmentation is the is a classic, typical report.

So we’ll take that same supplement that’s fine to Google Analytics. And now we can see the progress over time. So we see that, you know, on the number 13, we had 57 users sign up for our product. And we can then view more than stacks, right? We might want to see the user path for example, where are some of the other events that users took after the sign up so maybe they onboarded, maybe didn’t onboard, maybe they ended session, how many you know how many then abort some of the for a future video, maybe user streams. So we can actually go look at specific user IDs, right.

So now it’s just like an anonymous users. Now we can actually see them by name and you know, by name and email, we can go see John Smith is the exactly what John Smith did or didn’t do after they sign up for the product, right. So we have a lot of different options, we can we can filter those we can group this by city, by region.

We may only want to look at specific marketing sources, right. So specific UTM parameters so this all here as we start to create charts and analyze it. And this is a more of a visual what’s going on chart we can create funnels and a few other things. But the important thing is the data is still flowing to segment so we still flowing through everything from segment and most of the stuff here will actually be client side too.

As it will be tracked through a JavaScript library like you saw in the example for a second before. And that’s really it. That’s the very first two stages, the marketing website visit and the signup stage at the acquisition level in the three tools that you have in your stack up to this stage.

In the next video we’ll look up onboarding. Some of the reports you might see there and some of the events that might be relevant there. Stay tuned for that.

Hey, Julian here from Measureschool.If you want to follow along with the next video in this series, then we have it linked up right over there. And if you want to learn more about the data-driven way of digital marketing then definitely subscribe to our channel right over there. Now, my name is Julian. See you on the next one.

The SaaS Analytics Stack with (feat. Ruben Ugarte) | Part 1

What are the right tracking tools for your SaaS business? We have Ruben Urgate (from Practico Analytics) back on the channel to show us the right tracking software for the different stages of the business. This is the introduction of our SaaS Stack Analytics Series we will be publishing on the channel.

Check out video No2 on Acquisition tools here:

? Links:

Practico Analytics:
SaaS Stack Overview PDF:


In this video, Ruben is going to introduce you to our SAS analytics stack series. All and more, coming up.

Welcome back to another video of teaching you the data-driven way of digital marketing. My name is Julian and today we got Ruben back on the channel. Now, Ruben runs Practical Analytics where he helps businesses to figure out which tools should they use for the different uses of analytics in their businesses, at what stage the customer actually is at and what data you can track any apps obviously also with the implementation. Now while chatting with Ruben, I was super interested in what tools he would recommend for a SAS business or a software as a service business that has recurring revenue. Since there is not just one tool to rule them all, he made a whole video series for us, which we are going to release on this channel in the coming weeks. Now, he’s going to cover everything from tools that you would use to actually track the acquisition of the customer until to the retention of the customer and which metrics you should pay attention to. In this video, we’re going to get a brief introduction to his framework that he uses. And then in the next video, we’re going to dive right into the tools that he would recommend. Now, we got lots to cover. So Ruben, take it away.

Thanks Julian. And welcome to the SAS analytics stack series that will be going over here over the short little while. My name is Ruben, and I’m the founder of Practico Analytics. And we specialize in helping software companies especially fast growing software companies really set up the right data foundation in place. This typically means we come in and we help them design a stack that helps them cover some of the most fundamental questions you know, what marketing campaigns are driving the best users, what’s going on with the product, are user actually love the product or are they being retained. All these kind of things. And what we came up with the series is we have a handful of videos here about three or four videos, and we’ll be looking at all the series, you know, the typical journey the user will go through when they want to use your SAS product. And we’ll cover some of the different tools, reports and bands, data tracking, all these different things, they should be considering when you build out your stack. And you know, with some of the mention here is that typically, a lot of companies want one tool that can do everything and that’d be really great. In an analytics world that’s still not quite the case. So you really still going to end up with multiple tools, a stack of sorts, to be able to answer all the questions that you need. And I’ll cover what the tools are. Now, some notes or some things to keep in mind as you go through his videos. Really, there’s lots of tools out there, really, there’s hundreds of options. So whenever I mentioned the tool, I’ll give you other options if you prefer to explore those. So you don’t have to pick the recommendations I mentioned. But on the other hand, you also want to keep it small, right. So keep your stacks small, I think, you know, our final stack will be about four or five tools.

That’s something that’s manageable, you can probably compress a little bit more maybe two, three tools. So you want to start small always, you know, especially if you don’t have anything you start with a handful events, build upon those a small amount of tools, build upon those and so on. Don’t just try to go big and be like hey, you know we’re doing this we might as well do a big which is a company’s tackle. So really want to tackle small and get value out of it right. If you have a priority in your company, if you say hey, next six months acquisition’s a big priority, you could probably just cover that step, you know that the steps are covered there and just start with those events, those tools and eventually come back and tackle retention and onboarding and everything, you’ll have tackle everything at once.

With that being said, we’ll jump to the first video where we’ll talk about acquisition and some of the acquisition data that you can be tracking for a SAS products and the tools, some of the events, reports and so on. And then from there, we’ll start so I’ll see you in the next video.

All right, so there you have it, a brief introduction to our SAS analytics stack series here on this channel. If you want to follow along, you can click on the next video right over there where we’re going to talk about acquisition and which tools you should use to track this correctly. And if you want to keep up to date with all the videos that come out on the channel and also the future videos of the series, definitely subscribe to our channel and click that bell notification icon so you don’t miss out on anything that we do here. Now, my name is Julian. See on the next one.

Habits for data-driven marketers | Tips from Experts at Measurecamp

What is the #1 skill to build as a data-driven marketer?

We had the chance to attend Measurecamp in London, which is also known as the ‘World’s best analytics unconference”. We asked 5 experts for their opinion on what skill or habit you should build as a data-driven marketer in 2017.

Experts seen in the video:

– 00:57 Peter O’Neill (Founder of Measurecamp)
– 01:43 Jonathon Hibbitt (Web Analyst at Site Visibility)
– 02:05 Krista Seiden (Analytics Advocate at Google)
– 02:25 Phil Pearce (Freelance PPC/SEO/Web Analyst)
– 03:25 Simo Ahava (Senior Data Advocate at Reaktor)
-04:12 Jono Alderson (Principal Consultant at Distilled)


? Helpful Links
Google Tag Manager Fundamentals:
Google Analytics Demo Account:
More Google Tag Manager Tutorials:

? More from Measureschool

Correct Google Analytics Setup Course:
GTM Resource Guide:
Free GTM Beginner course:


How to Make Money selling Analytics Services

You know GA, GTM and more Analytics? Turn your investment into skills profitable and start making money. How? In this video I’m going to show you 5 services you could be offering with your analytics skill set. They are..

Analysis and Reporting


? Learn more from Measureschool:

?Looking to kick-start your data journey? Hire us:

? Recommended Measure Books:

? Gear we used to produce this video:


Now I’ve been a freelancer selling analytic services for a while. And today I want to give you my tips on what to sell and how to make money with your analytic skills. All and more come up. Hey there welcome back to another video of Teaching you the data-driven way of
digital marketing. My name is Julian, and, on this channel, we do marketing
tech reviews tutorials and tips on tricks video just like this one. So, if you
haven’t yet, consider subscribing to the channel and click that down notification
icon so you will stay up to date with all that we do here on this channel. Now
today I want to talk with you about selling analytics services. I have been a
freelancer offering my services to various clients in various fields over the
last few years and have learned a lot at what you can be selling from your
skill set as a digital analyst or a digital marketer that knows a lot about
analytics. And I must say it’s a very rewarding field because there are not many
people out there specializing in this kind of digital marketing field. You can
see a lot of types of website type of data that comes across your desk and the
clients are less price sensitive I would say if you get the right client to
sell this analytics services. But what do you offer in an analytics consultation
or in an analytic service? So I want to show you here my five products that I
would sell as a digital analyst. First up audits. Now audits are really the
first thing that I do to get it feel for the client but also for the state of
the analytics implementations. And you can sell as a single product to the client as a service because the audit can bring a lot of benefit to an organization. it ensures data
quality and gives them some direction on how to improve the analytics
implementation in terms of gathering maybe more data, enhancing their existing
data quality, and obviously avoiding detrimental mistakes that they have in
their implementation. So, you start out with a thorough check of the Google Analytics or the implementation of the Google Tag Manager if your own checklist available that you go through in order to make sure that all this is set up correctly. And then you would give your client a report that outlines all the findings and the suggestions that you might have.

So, this is a product that you can sell your analytic skills on. It’s also creative because not every business is the same and you need to customize and suggest customizations to your client so there is a lot of possibility to show your client that you know all about Google Analytics but also suggest the right implementations, the right customizations to make their analytics implementation better. Building on that we have implementations. Now once you have made these suggestions, obviously the team your client could go out and implement them themselves but most of the times they want to have an expert on this case and you can up sell them to an implementation. Now implementations everything from installing button click tracking to certain goals or making sure the analytics is set up correctly. But here you would go in and change things around in their tracking setup. This has become
increasingly easier with the help of Google Tag Manager obviously because we
can do a lot of this stuff by ourselves now as visual analyst. But sometimes
you also need to be able to write a specification for a developer and help him
out to implement the right data layer for example. so, there’s a lot of
communication already going on between you and the clients but then also between
the different of departments that need that data to have the right
requirements, the right implementation, and making sure the data is correctly
in your system. Now the next point is analysis and reporting. Now a lot of
people think that we as digital analysts or digital marketers specializing in
analytics only do analysis work or reporting work but it’s just one part of the
whole analytics process. Obviously, you can log in to a Google Analytics
account and start digging through the data, but you need a lot of knowledge
about the business, about its actual functioning, and where the biggest
leverage points are. So, the inside generating machine of a digital analyst is
a big myth here. There’s a lot more to it to help the business to make
impactful changes. So, the skill of an analyst really starts with the
preparation of analysis project, what does the business actually want to
achieve with such a project, where can you find that data, is the data
qualitatively good, and can you produce the outcomes that the business would
expect. Now in most cases a lot of people think that they can just analyze a
little bit of data and then come up with a certain change on the website that will make a big impact. But it’s not always that easy because you also need to have the standing inside of the company to be able to move people forward and help them make a decision or help them change their behavior. And therefore, analysis project that lead to a certain outcome sometimes hard to accomplish within a company. so, a lot of people resort back to the reporting so building dashboards for a company which can be super helpful if the client knows what to do with that data and if he can drive the behavioral change forward in his company. Now setup of the dashboard is something we are involved with as well. But we always want to make sure that the data is relevant to the client. We can optimize that process as well but ultimately, he needs to work with that data and make changes happen in his company or in his organization. We always need to be upfront with that. We don’t want to be pushed into the direction of a data monkey who just pulls out data from Google Analytics and sends it to the client. Then the client will ask himself at some point why do I actually pay this person I could automize this whole process. So making sure you understand what you’re going into in an analysis project or a reporting project. That said I think that an analysis project in itself is most of the times not what you are doing. You would spend a package with all these three components of audit, to the implementation, and then a little bit of analysis to actually get insights to show what you can do with the data. And then help the client to take it from there to generate his own habits, his own processes to bring more insights, more change to the company with the data. Which brings me through a fourth point which is training. Now this is obviously what I do here on this channel but I also do it for clients and companies out there. Helping them to understand the data better than they have in systems to make sure that they can utilize that data and build the processes the habits to actually make a data-driven organization work. Data isn’t easy and it’s very customizable, it’s very individual to the business so investing into training is something that a company should do in the long-term. Just because outside freelancers will never have the standing in the company itself to lobby for change inside of the company with the data that I have available and the insights that I have available. So, training an in-house person long term is always an important objective to have in mind when developing people inside of the company. And therefore, training is something that’s very rewarding to me at least because the impact that you can make is long-term inside of the company. And our fifth point is obviously the action. So, all the automation that you could do or the optimizations that you could do with the data. So, I have some clients that want to set up automation with the data that they have available in their systems build new audiences for example in Facebook and generally, optimize their campaigns with their data-driven approach. Now one optimization a lot of people think about is A/B testing. Now A/B testing really encapsulate all these different components that we talked about before if you have to write an analytics implementation you can do the correct analysis the correct research to come up with a good A/B test. and then test these hypotheses, make the change on your website so you can then again start at the beginning from the defining, to measurement, to analysis and then testing as well again, so this is a full circle analytics implementation or analytics process which obviously is also a product that I could offer. But it gets really really complicated in most of the cases you would need to have a team to actually set that up correctly and make a big impact on your company. A/B testing sounds easy from the tools that we have available nowadays but to build an impactful change and an impactful optimization for a long-term is really something that should be left up to professionals and invested in the right time and the right place. Alright so there you have it. These are my five products that I would think of selling as a digital analyst if you want to turn your analytic skills into money. But I know that there are many consultants and freelancers out there that may use different techniques different services that they offer that work maybe well. I’d love for you to share them in the comments below as well. And if you haven’t yet then please consider subscribing to our channel right over there because we’re bring you new videos just like this one every week. Now my name is Julian. Till next time.


How to use YouTube Analytics to rank higher w/ Nico from Morningfame

How can you use data from YouTube Analytics to rank higher in Search Results? In this video I’m talking with Nico from Morningfame how he uses the data YouTube provides to help other YouTubers improve their search rankings. His tool does a great job of conveying the right data to take action on your next video and rank higher in YT search results.

Morningfame YT Channel:
Video on similarities between Google and YouTube SEO:
Screenshot of Email:
Morningfame Website:


? Learn more from Measureschool:

?Looking to kick-start your data journey? Hire us:

? Recommended Measure Books:

? Gear we used to produce this video:

?Looking to kick-start your data journey? Hire us:


– In this video, I’m talking to Nico from about YouTube analytics and how we can improve our channels, and our videos to rank them higher on YouTube. All the more coming up. Hey there, welcome back to another video of teaching you the data-driven way of digital marketing. My name is Julian, and today I’m joined by Nico from, the tool Morningfame, which is a new YouTube analytics tool that helps us creators here on YouTube to rank our YouTube videos higher and help us to improve our overall performance of our channels so we can have a greater impact on our viewers, just like you. So welcome, Nico.

– Hi, it’s a pleasure to be here. Actually I’m also a total data nerd, I could say, and seeing your channel is like a delight.

– Thank you, and it’s really great because to have you here, one, I reached actually out because I bought a tool just a few weeks ago, and I saw how well you have actually put the data together, and maybe we can talk a little about first how does YouTube actually rank videos and what do you think are the basic metrics that a creator, just like myself, need to look at when we want to a rank video higher on YouTube search results.

– Absolutely, I mean YouTube, you can say, it’s a little bit simpler than Google, I would say. Comparatively. And there are few important metrics YouTube looks at, and one metric that is the most important one according to a study done by Matt Gillen, you can put a link into the description if you want. Very interesting is how many people are let to the video from outside onto YouTube. So YouTube wants a lot of audience on the site, so if your channel is doing so, like for example, you send some emails out when you publish a new video, and people go into YouTube. This is a very important metric for YouTube to know all right, this is a good video, I will promote it even more in all the different means that YouTube has, by the way, it’s not only YouTube search, like Google search, but also suggested videos, you know those videos at the side alongside a video you are just watching, and all other, like the home screen and so on, so proof that YouTube has a lot of ways to promote your videos, and leading more new viewers to the platform is one important part. The second most important part is watch time. Watch time means: similar we talked about that on my channel right, it’s similar to how long people stay on your website. So in this case, it’s about the video how long you people watch the video. It’s quite common that they are bored watching a video in the middle, because they got bored or something. You know, the video is not that great, and they don’t watch it ’til the end, and YouTube measures that first and foremost to find out that the people are really, really enjoying the video. So something like you would argue maybe some fluff and comments, and subscriptions, all that stuff that people can engage with your video, they are actually not so important so, it’s more watch time. So people should really watch to the end.

– That’s very interesting, I mean, Google and YouTube is a property of Google right, it’s not our own website and we can’t do anything to improve watch time in the sense that, well we can improve watch time by having a better video, but the data itself is provided by Google. We can see that in our YouTube analytics, and then act accordingly and try to improve that. Now, this is also a metric that is not easily gained, right? I can’t,

– Absolutely.

– Buy links, like on Google and try to gain my search results through that. So that’s very, very interesting. So how much data does YouTube actually give you in that regard in the YouTube analytics. Are you satisfied with what you can see there?

– Yeah, actually it’s still overwhelming if you look at YouTube analytics at first. I mean you guys know there’s probably from Google analytics as well, that’s certainly overwhelming but still it’s quite condensed to just a few things that are really, really valuable. The watch time is very well thought out. YouTube analytics where you can see where people stop watching the video so you can go to this position in the video, and look at what were you talking about that in this video at that point. Maybe that was boring and you should cut out a scene in the future where you don’t do an excerpt that is not interesting to your viewers. Anyway, this is very interesting to see, and also of course, traffic sources. This is very valuable because you can see there where people are coming from, and from Google analytics, you know, that is Facebook and all the other websites where people are coming from, but also on YouTube you can see all the internal traffic sources. So the ways YouTube can promote your video, and that is YouTube search, you can even see there which search terms they used in order to find your video. Such as the video, you can see which other videos brought you those views to your video, and yeah the all the other places, like the home screen, which people see first when they log into YouTube or open the YouTube app.

– Okay, so that’s very interesting now as a creator, as myself, I bought a tool from your company, Morningfame, and also going to link up the YouTube channel down below, with Morningfame, I saw that you run additional analysis on the data set that you have available through YouTube API, right,

– Yeah.

– So what is actually the data that you bring into your tool.

– Yeah, true, if you look at YouTube analytics in raw of what YouTube is providing to you, usually what you first look at it is of course views, right? And you can do much more with this data to understand what really resonates with your audience. You have to understand, I mean same for web pages of course, right?

– Yeah.

– You want to provide some content that your viewers, your visitors, are really enjoying. And on YouTube that is not only each individual video, but a more broad apperrong, petong, that means in particular, the video topic. And for example, I do a channel where I talk about YouTube SEO, and what I found out is of course that when I talk about the YouTube algorithm, then people are really enjoying that. All my videos about the YouTube algorithm, they are really work, doing great. So I’m of course doing more videos about the algorithms, explain how it works internally. And maybe some nuggets where I explain how to improve your own videos to better get promoted by YouTube. So extracting which video topics works well with your audience. What my audience enjoys watching. That is really valuable and you usually don’t see that by just looking at the views. You also have to take into the account, do they watch those videos to the end? Do they engage by giving thumbs up and commenting? Because that’s of course the tip of the iceberg, not all people will write a comment, but if they do, that’s really a sign that they really enjoyed the video, right? So taking all that into consideration, you can further analyze which video topics work well with your audience and do more of those. And that actually, I built into the software, looking at all the different metrics at the same time to see which topics were effective.

– Maybe can you tell us a little bit more about what your tool, how you get people to get that point of actually taking action. What do you do in the tool, giving them different visualizations, giving them different tips, how did you think about this when you thought about, because on this channel we also tell people about the data that they can gather, but then they also need to take the next step of implementing this, taking action upon the data, changing something in their business so, how did you think about this when you built your tool.

– Yeah, absolutely. I mean besides choosing the proper video topic to do more of, there’s also one interesting part, you know, you don’t have that too much on websites, but on YouTube, the thumbnail, the picture you see before you watch the video, this one is really important. You can compare that to a magazine cover, right? You’re buying the magazine because at the grocery store, you see the magazine cover and it’s looking nice, so you buy it. And the same is for the thumbnail for the video, and improving this thumbnail, to make it really intriguing that the viewers click on this video, is really important. First of all, because people are then more intrigued to watch the video, and second of course, because the YouTube algorithm also tracks the click-through rate. We don’t have, unfortunately, access to this information, but anyway, it is important for the YouTube SEO. And the thumbnail, is very crucial to this process, because if your thumbnail can stick out between all the other videos that the viewer sees for example, on the YouTube search result page, then they will rather click your video instead of the other videos.

– [Julian] Right.

– So, I built a neat report where you can see for your video that you just published, 24 hours after you publish a new video, you see five other thumbnails of videos that rank alongside your video, along with your own thumbnail, and you can compare. Does your thumbnail stick out? Is it visually more attractive than other thumbnails, because if you can achieve that, yeah, then you win the click instead of the other videos. And improving that will boost your overall success of your video.

– Absolutely. But yeah, that’s really, really interesting. I think that what we can do for this video, is maybe, I will publish this video, and then 24 hours or 48 hours later, I will get an email from your tool, telling me what I’ve done well on this video, and maybe I can link up a screenshot down below so people can see what data is actually on this report, and you can learn from this, maybe even from your Google analytics reports, or what you tell your client, because this is what really matters for us YouTubers to take the next action to improve our channel. And maybe that will give you ideas later on for your analytics and how you display and visualize data later on as well.

– Absolutely, and I think one big issue you usually have into conveying whether you did a good job or not, right? Is whether the new videos you published on a channel are getting better and better; more successful, more successful. And that is one interesting effect. In the long run, your video may accumulate thousands of views, and that is of course a success, but you want to know your results immediately. And that is also the reason why Morningfame sends you 24 hours after you publish a new video. This report that you will be linking up, because then you can see how the views of that video grew. And it’s quite interesting to see. I’m studying this view increase in the first 24 hours for countless channels, and it’s usually the case that you can already tell after 24 hours that if this video will be a big success or not.

– Yeah.

– And so, this is a good way to measure whether you are improving over time. And also something you can show to your boss.

– But I don’t have a boss, but, that’s great to hear. I think that in the end we can really learn from what the tool, even if you’re not a YouTuber, you can learn from how Nico has built these visualizations, how he has packed information into a short email that was really actionable to me, and that’s why we got talking. Because I was like, wow, this is a great report for me, and maybe you can build a report for the stuff that you are doing for your website or for your business that you are doing analytics. All right, great information. Thank you, Nico, for this valuable insight. Where can people find more out about you, and find your channel and so on.

– Absolutely, you can go to my main website that is Morning F-A dot M-E. Quite complicated, but anyway, you’ll find the link in the description or check out my channel called Morningfame, where I talk about YouTube SEO quite a lot, and you can learn a little bit more about how to optimize your videos.

– We also did a video for your channel, so I’m gonna also link that up right here so you can head over what I have said on his channel, because we were talking a little bit about what are the similarities between website SEO, and YouTube SEO, and how we could maybe transfer the learning over. So if you’re interested in that, check out that video as well. All right, now you heard Nico, if you have anymore questions, and if you want to give Google a good signal to rank us higher, tell YouTube to rank us higher, then leave the comment down below, and if you haven’t yet, consider subscribing to this channel because we’re bringing new videos every week. Now my name is Julian, ’til next time.

– Cheers.

Facebook Analytics vs. Google Analytics – Which is better?

Facebook Analytics has been introduced to give new insights on the data that is collected via the Facebook Pixel. Is it better than Google Analytics? What are the differences or similarities between the tools? Let’s find out….

? Links mentioned in the video:
Facebook Analytics:
Google Analytics:


? Learn more from Measureschool:

?Looking to kick-start your data journey? Hire us:

? Recommended Measure Books:

? Gear we used to produce this video:


In this video we’re gonna find out what is better Google Analytics or Facebook Analytics. All and more coming up.

Hey there welcome back to another video of teaching you the data driven way of digital marketing. My name is Julian and today we want to take a look at the differences between Google Analytics and Facebook Analytics.

Now this has been a really interesting question. I’ve seen this picture on Facebook, where somebody typed in Google Analytics and we see that Facebook Analytics actually puts AdWords advertising on top of the search results here. How funny is that? And they say here it says here, measure people, not cookies, Facebook Analytics, So, today I wanted to take the opportunity to actually look at Facebook Analytics and Google Analytics and maybe the potential on how it changes the game in terms of what analytics tools you would be using in future on your website and your app. So, lets do a little comparison of Facebook Analytics against Google Analytics.

So first up let’s talk about the history of the tools. Now Facebook Analytics is pretty new. It just came out in 2017, broadly to the public and is now available to all the advertiser of Facebook Analytics. Now this is a pretty much new tool to us and the data basis is revolutionary and different to Google Analytics. Now let’s consider the history of Google Analytics, a much older tool here. And that also plays into maybe some of the disadvantages here but also the advantages, because Google Analytics actually was different company before it was bought by Google. It was a company called Urchin. And in 2005, Google saw the potential of all these analytics tool out there and bought this tool for their own advertisers that were doing AdWords at the time. The rationale behind it was really that AdWords advertisers couldn’t measure the complete customer journey. They were only able to see how much they spent on an advertising that had made them money. But maybe not what was actually happening on the website itself. So, they thought about a solution on how they could help advertisers out to spend more money on AdWords. And that’s when they came up with the idea to buy a tool like Urchin and integrate it into Google Analytics or the suite of Google Analytics. So advertisers could actually make more money with AdWords. And they made it all free, I mean, the software, Urchin itself cost at some point 20,000 dollars per month.

Now Google Analytics premium actually cost that same amount now, but you can see that the power, the raw power of Google Analytics at that point was pretty substantial and they brought out this tool for free. So, you would be able to use it on your website. And it grew really fast because for that money, free you couldn’t get a solution out there. So a lot of website owners actually put this on their website and started tracking. Nowadays, Google Analytics is the defacto standard when it comes to web tracking because we can, first of all look at our data and compare it to other sites, so how many page use has it, how many users do you have per month, for example. But that actually comes with a clear model that Google Analytics gave us. So this model actually evolved from website tracking and it 2005 websites looked a lot different than what the web looks like today. So you would have a model that actually is centered around paid use, centered around cookies, that’s what they say in the ad and not around maybe users which Facebook is really good at.So, just remember that Google Analytics is a much older tool. Facebook is much newer. So the technology is evolved different and they come from different times of the internet history so to say. So we need to take that into account when we look at these two different tools.

Now that leads us to the next part here, which are the two tracking mechanisms. Now I already mentioned that Google Analytics is heavily relying on cookies. What does that mean? That’s the mechanism Google Analytics is built upon. So, the sessions, the page use and the users are all calculated through this cookie mechanism that is actually placed on the browser of the users to re-identify the user. Now you already can think of many different instances where this falls apart. If you, for example, come back through mobile device, you would have a different browser, a different cookie stored on your computer and you would be essentially a different user that is actually identified by Google Analytics.

Now Facebook Analytics is completely different from that, because Facebook actually knows exactly which user has been identified on there platform. So, if I click on and advertising on Facebook, it actually knows which user goes to the website. And if I click on the advertising again on my mobile device again Facebook knows exactly who I am. So, the paradigm shifts here from cookies to actually users because Facebook actually exactly knows who I am and then can track throughout my user session. So that gives them a bit more better data. So if you look into the audio demographics for example, in Facebook Analytics, you can see that there is much more data there that is interesting for us because we know that they are real user profiles that Facebook Analytics actually has in their database that they can provide us now as an advertiser or as an analytics user.

Now let’s talk about the popularity or the reach of the tools that the tools have. I think that Google Analytics is still the most widely used web tracking tool out there. There are a few competitors. I think of Yandex Metrica, which has also a very robust system behind it and give you all the data that you need. But people are just very locked-in to Google Analytics because they don’t want to change their tracking system. Maybe they don’t want to re-tag their website and it has been running for years. You know that Google Analytics is not retroactive, so you can’t be pulling in historical data into the tool. So they just keep it running, most of the website owners keep it running because they have a history of data. And if they would turn that off all that data would not exist anymore. So there’s a strong lock-in effect in terms of using Google Analytics.

Now Facebook Analytics actually has another tool that we install. Pretty much everybody, as Facebook Ads got more and more popular, you install the Facebook pixel on your website and the Facebook pixel actually provided ours with conversion tracking data. Now it’s more retargeting data and suddenly we have this whole database of Facebook Analytics available to us. So the popularity will grow as Facebook Ads grows to be the defacto one of the websites that you can buy advertising on. And Google has that obviously as well, but Facebook Analytics becomes more popular because you just have it installed already. That said, will you change over your defacto analytics tool from Google Analytics to Facebook Analytics? That’s the question. I haven’t yet seen people discuss their Facebook Analytics metrics, rather their Google Analytics metrics. So if you see somebody who is selling a website, you might as well look into their Google Analytics rather than their Facebook Analytics. It is interesting to see whether this will change over time. But for now, Google Analytics remains the dominant tool in the analytics market when it comes to website tracking.

Now let’s talk a little bit about functionality and versatility of the tools. Obviously, Google Analytics is a tool that has been developed over the years and has a lot of functionality that Facebook Analytics just doesn’t have. So if you think about the base checking, yes, you can see page use, which Facebook Analytics and Google Analytics. You can see sessions and so on. But Google Analytics has a much broader spectrum of customizations that you can make through to it. When I think about enhanced e-commerce checking, custom dimensions, user ID tracking, calculated metrics, custom metrics and so on, that’s all a very customized version of Google Analytics. And you can customize Google Analytics to the business model here. With Facebook Analytics it gives you the basics, but it doesn’t let you expand a lot of that. Now, Facebook Analytics has opened up their Facebook pixel and it has opened up over the years so you can send custom data in. But in Facebook Analytics it’s really hard to still see that data, to pull it out of the interface and actually make sense of that data. They are now doing auto tracking, so they are picking up all that meta information that you might have on the page. But they’re not really exposing that in the interface per se and giving you smart insights on that. So it’s still in the basic raw form of tracking data. I think that there will be more features introduced and maybe these features will be different from what we see in Google Analytics. But for now, Google Analytics has still many more customization features then Facebook Analytics.

So in the end, what is my conclusion? What tool is actually better? If I look at Facebook Analytics, I think that it’s really interesting that the shift really happens in the underlying tracking mechanism on how Facebook Analytics is able to track people and maybe give us more insights in terms of maybe attribution, but also demographic data and then lifetime of the user, going through the system and maybe looking at different websites. They play to their advantage a little bit of that already in there demographics data or in their funnel analysis. But it’s not too apparent yet why Facebook Analytics is the next analytics tool that we should be using. We see over the next coming months, over the next years how Facebook Analytics evolves and maybe it will become the defacto tool because it just has a better tracking basis of what we can do with our analytics data. But as you know, Google Analytics is much more than just a tool. We have so much more data available outside of Google Analytics and they are connecting so much more to it. So we have Google Tag Manager, we have Data Studio, we have Google Optimize so all these different tools really feed into the whole ecosystem that Google has built with their analytics suite. And maybe that will keep us locked-in to the analytics suite. For now, I don’t see why you shouldn’t be using two tools at the same time. It just gives you a different tracking and different database at the end. For most of my data, I actually still use Google Analytics to do quick analysis. I just know the interface better, but also I trust the data more because I know how it actually pulls in. And Facebook Analytics, it’s still a black hole. Their documentation is not yet there where you really know how to manipulate any kind of customization that you can do in the tool. And maybe they will never reveal this because right now Facebook Analytics is a tool that you add on to you advertising to get more insights from it. It’s not really something that you would use as a stand alone tool to analyze your website. But as I said, maybe I’m a very particular case.

And I’d love to hear from you if you are using Facebook Analytics over Google Analytics then please let me know in the comments below. I’d be really interesting to hear if you’re using Facebook or Google Analytics as your main tool. And if you like this video then please, give us a thumbs up and also subscribe to our channel right over there because we will bring you new videos just like this one, every week. Now my name is Julian, till next time