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So What? Universal Analytics data migration

So What? Marketing Analytics and Insights Live

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In this week’s episode of So What? we focus on Universal analytics data migration. We walk through how to prepare for a Universal Analytics data migration, how to extract your Universal Analytics and discuss the pitfalls to avoid with a Universal Analytics data migration. Catch the replay here:

So What? Universal Analytics data migration


In this episode you’ll learn: 

  • How to prepare for a Universal Analytics data migration
  • How to extract your Universal Analytics
  • Pitfalls to avoid with a Universal Analytics data migration

Upcoming Episodes:

  • Podcasting overview-6/8
  • Podcast marketing strategy-6/15
  • Podcast marketing tactics-6/22
  • Podcast marketing measurement-6/29

Have a question or topic you’d like to see us cover? Reach out here:

AI-Generated Transcript:

Katie Robbert 0:27
Well, hey everyone. Happy Thursday. Happy June 1. Welcome to SWOT the marketing analytics and insights live show. I’m Katie joined by Chris and John, how’s it going, guys?

John Wall 0:38
Some are already instantly. So

Katie Robbert 0:43
on today’s episode, we are talking about Universal Analytics data migration, if you have been living under a rock or have been willfully ignorant of the fact that Google has made a change to their web analytics platform, from today, that where the show is live, you have 30 days to get your Google Analytics 4 instance set up. Because in 30 days, you will not be able to collect data in the Universal Analytics, if you need help with that, contact us or take our course at AI slash GA, for course, or you can contact us and talk with John, our chief statistician.

John Wall 1:27
That’s it one standard deviation from getting it done for it.

Katie Robbert 1:30
But so what we’re talking about today is so one of the things that Google has not made has not done a good job of making people aware of, and I did a very informal poll on this topic on social media was your Universal Analytics data will not exist in the GA, for instance, and your Universal Analytics data will only be available for a few more months after July 1, they have not set a date for that. But they’re sort of hinting around like six ish months, for all we know, they’re gonna cut that much shorter. So basically, if you want to do any kind of historical look back, you need to do something and get that data out of your Universal Analytics system into a different system. And that’s what we’re talking about today. So we’re going to talk you through how to prepare for Universal Analytics, data migration, how to extract your Universal Analytics and pitfalls to avoid with a Universal Analytics, data migration. And so, John, you may be the chief statistician, but Chris is our Chief Technologist. So Chris, where would you like to start?

Christopher Penn 2:39
Well, here’s where we should start. And this goes to what you were talking about the newsletter yesterday, Katie, do you actually need the data?

Katie Robbert 2:49
It’s a really big question. And the reason I wanted to highlight that is because we’re data hoarders, we’re all data hoarders, we think we need the data, we might need the data, maybe you don’t need the data today. But what if somebody asked me a question tomorrow? And I already said, I don’t want the data. So there’s a lot of anxiety around leaving this Universal Analytics data behind. Forget the fact that it’s not one to one with Google Analytics 4. You know, that’s a whole different issue. That’s a whole different ball of wax. But, you know, God forbid, someone asked me what happened on June 1 2016. And I don’t have that data, what am I going to do? Well, does anyone really care? Probably not.

Christopher Penn 3:36
It’s true. So there’s, there’s three Well, three ish forest levels of data backup that you can do for Universal Analytics, and we’ll talk through each of these. There’s quick snapshots, there’s selected data through the API, and then there is full backup through a third party service of some kind. And that can either be cloud hosted or can be locally hosted, depending on the requirements. So this is why we keep coming back to those user statements, right? What is the user statement behind Universal Analytics, okay, for you, as the CEO of the company, what would what is your your user statement around Universal Analytics would or do you not care?

Katie Robbert 4:22
It’s not that I don’t care. But I’m having a hard time justifying the need to keep that data around. It’s not going it’s not data that I at this time would make a decision on so present day data month, you know, data from the past month data from the past couple of months. I can look at that and make some decisions but data from a couple of years ago. I’m not going to make a decision with that. I’m going to look at it and go, Okay, that was interesting, but the company is so different in terms of our marketing tactics are so versus the maturity of the company, the audience of the company, like it’s not the same as it was when we first launched plus three years of that data was during the pandemic. And so the data is not what we would expect it to be anyway for us. And so I don’t personally see it, the usefulness of having a backup of all that data, what I would probably prefer, is I could take some screenshots of existing dashboards, and refer back to that, and just say, oh, that’s what the data looks like on that day. But that’s about it. I don’t really feel the need to keep all the data. But that’s me.

Christopher Penn 5:40
John, what about you, since marketing over coffee has had a website since like, 22,007, where you have 16 years of Universal Analytics data? Yeah,

John Wall 5:49
you know, the data KV nailed it with, you know, snapshots are good enough, like having a rough idea of, okay, here was how much traffic per year, maybe enough to break it down by month, and to know, the channels, you know, that we would expect 20% to come from social and 20% that come from Apple. And that’s really enough, you know, unless you have pretty much if you’re an organization where you have a VP of marketing or a CMO, and you have a team of at least three or four people, then you’re getting to the point where you want to have that data to prove ad budgets. And if you have to prove anything over three or $400,000. Now you’re talking about, okay, you know, and our classic ROI on that 20% is set aside for measuring, you know, if you’re at 400 grand, well, now you can talk about spending 7080 grand, building a database that you could go back and query and that would be worthwhile. But, yeah, for marketing over coffee, it’s not, you know, we have a rough idea of how much traffic we should have. And the other thing is, we’re really only tactically focused, right? Like, I only need to know, okay, I turned on the ads this week, did we do better than last week? It’s not like, I’m not looking at seasonal podcast trends, or, you know, over multi year analysis, because that’s just I don’t have the bandwidth for that kind of stuff.

Katie Robbert 7:00
Okay, what I’m likely going to do is put together a couple of Looker, studio dashboards, their tables that are you know, here’s 2022, here’s 2021, and break it down by month of like, the top three metrics that I care about in Google Analytics, and then take screenshots of those, stick them in a doc. And if I ever need them, then they’re sitting in my folder somewhere.

Christopher Penn 7:24
Gotcha. Okay, so I think we can talk through the first couple of those scenarios pretty easily. So the first scenario really is that whole, Hey, I just want to know the basics. So you go into Universal Analytics, maybe you go into your audience, go into overview. And so Trust Insights, we’ve been around for quite some time. So I’m going to crank this back to 2017. And here is, of course, our data for the last five ish years. From at this point, you can just hit export, punch out your CSV, and now you’ve got your, your basic user data, right stored in the format of your choice, super easy to get at this data, right? Very, very straightforward. And if that’s all you need, that’s, that’s probably good enough, right? There’s, there’s not a lot of complexity to this. Same is true, if you wanted to do this by channel, right, you can export this data by channel, you will get the same table, and then you’ll get the individual channels. Now, this is not broken out over time, these are just the total. So that’s what’s going to come out in the in the table, tabular data. But again, very straightforward. This requires obviously, zero technical skills, right, you just got to find the charts that you care about. And I would suggest, if you’ve got the time and the inclination to do so you can have you know, someone go through each of the screen, the major screens, so you would identify which screens, you want to have export, like the you know, maybe the All Pages data, and one by one, boom, Export CSV over and over and over again, this is arguably the least efficient way to do this. But it’s also the lowest tech way to do this.

Katie Robbert 9:05
You say arguably the least efficient, but it depends on how much data and how many properties you’re working with. So for us, I would say this is good enough, because what I would do is I would bring these CSV files into, you know, Tableau, for example, put together my workbooks and boom, done, it’s all done. I don’t need to worry about it. I can refer to it whenever I need it. So for us, I would say it probably is the most efficient because, you know, we’re not looking at landing pages and, you know, a lot of the other stuff that comes with it. It’s just not what we need for data. So I would say for us, it probably is the best option.

Christopher Penn 9:48
Okay, I disagree, but that’s okay.

Katie Robbert 9:51
Well, we’ll go back to data hoarders.

Christopher Penn 9:54
Yes, exactly. So the second way, which is kind of what John was talking about, if your spouse typically interested in things like acquisition channels in the past, like if you wanted to see like, hey, you know, here’s when we ran ads, for example, it’s gonna be very difficult to sew that all together from all the individual CSV exports, it can be done. But it’s not going to be pretty, though this way requires some technical know how it is, you will be asking the Google Analytics API for specific information. So this is an R script that we write, there’s actually a data exporting facility. And what we’ve done is you’re allowed to choose up to 10 metrics and seven dimensions to export. So you would go through user stories, right, as a podcast owner, I need to understand the marketing channel usage over time so that I can make decisions about what what platforms, I should be continuing to run something along those lines from that user story, you then figure out what are the seven, the seven dimensions, you can have up to seven, there’s, like 210, just seven, and then export up to 10 different metrics. So numerical values. So these are the ones that we see clients asking about the most. It is sessions, pageviews new users, balances, session duration, organic searches users, as the metrics, and the obviously the date, the source medium, the campaign, the page, the user type landing page, and the country as sort of being the mix of, of variables. And then what this script does, is it goes week by week from the starting date, which we have set to 2017. Through today, and exports all that data into a local database. So it’s, there’s a database system called SQL Lite, that is a flat file database, you can obviously push it to Google BigQuery. If you wanted to have it accessible in days to dashboards, you could push it to a MySQL database or the the database platform of your choice. But this is sort of the second method, it’s not the full data. But it’s enough. And it’s granular enough for a lot of people’s basic year over year decision making to be able to make decisions from it.

Katie Robbert 12:10
So this is where in the newsletter yesterday I was. So I think a user story is good to start with. But the way to really focus that is to do a KPI map, because that’s really going to help you. You know, for us, we’re looking at the metrics that are most commonly asked for, but that’s not going to be the same company to company, some companies may not care about pageviews or bounces, they may care about, you know, specific cart fills or events or you know, whatever the thing is, so doing that KPI map is going to help focus that in especially if you’re limited to the number of data points that you can export through the API.

Christopher Penn 12:51
Yep, exactly. And I think those KPI maps would be super helpful to do. And what you end up with is you end up with a database, you end up with a database of some kind that has all the information that you’ve requested in all those different fields. And it’s available for you to slice and dice. And so that that is sort of the net output. Now, when we ran this, this was about 104 megabytes worth of data for five years. So for Trust Insights, in those first few years, we didn’t have a ton of traffic. So this could get to be a very large database, depending on the size of your Google Analytics installation, how many? visit your website, and so on and so forth. So this is method number two.

Katie Robbert 13:32
So let me ask you, Chris. So when we were talking about method number one, and I said that those CSV exports were good enough, you said you disagree? What is it that you feel in your role that we need from our data?

Christopher Penn 13:47
The challenge for me with the CSV exports is that many of the measures are not necessarily one to one on a given date, in the end to have those exports, you would unify them by the by the dates. And so extracting it all this way, gives you the different dimensions, which may have different scopes, and timescales all unified into one table. So that you can you can look at, okay, this session, these two sessions were from China on this day, which were two new users and so on and so forth. You won’t get that level of granularity in the CSV export. So you get the very high level, which again, for some companies is totally fine. But if I wanted to look at only traffic from China, or only new visitors or only visits to the homepage from a specific channel, you can’t do that from the CSV exports because they’re not granular enough.

Katie Robbert 14:40
To be fair, is that something you currently do?

Christopher Penn 14:44
Or date is it it is when we do our once a year anniversary posts? You know, we go back and say like, here’s what’s changed over the last five years. Realistically, from a marketing tactical perspective, we rarely look back more than you Hear from a historical anniversary, hey, you know, this is where we were five years ago. Yes, that’s when we pull up this data.

Katie Robbert 15:07
And I think for a lot of companies, those are the hard decisions that they’re going to wrestle with of, do we really need more than a year’s worth of data, if you set up Google Analytics 4, you know, a year ago, you have that years worth of data, you don’t need to worry about Universal Analytics at all. And that’s, you know, companies, you’re going to have a hard time. It’s like cleaning, I talked about it before, it’s like cleaning out your closet, you know, that blue sweater that’s been sitting on the floor for the past five years that you haven’t worn, you know, it’s okay to get rid of it. But you know, in your brain, your brain is telling you, but I’m going to need it tomorrow. If I get rid of it, I’m going to need it tomorrow. And then it becomes an obsession, you start thinking about only that blue sweater, and you’re convinced I can’t get rid of it, I have to have it. And so bringing in an outside company who can ask you those questions and help you make those decisions might be a good, you know, way to go with this. Because you do end up data hoarding, you do end up taking unnecessary steps to hold on to things that you don’t need. But this I mean, one person’s opinion, I love throwing things away. I hate extra stuff. I hate clutter. So for me, good, great be gone. Done.

John Wall 16:24
You can go for Marie Kondo, you can be like, I only keep data that brings me joy, everything else brings me joy. accountants don’t like that. That doesn’t work. That’s not a best practice.

Christopher Penn 16:36
It comes down to risk to risk mitigation, right? Because you could make the same general argument about things like health insurance, like nothing’s wrong, nothing’s been wrong for five years, I don’t need this like, well, the risk of not having that is very, very high right. Now, with your, your Universal Analytics data, is there a substantial risk? To John’s point? It depends. If your auditors come knocking, yeah, you might need that data, he might, they might be a case where like you need to pull up. This is how much traffic we got from this place. If you were working, for example, in a highly regulated industry. Yeah, you’re gonna be you might be asked, we need seven years worth of data for traffic to your website from the United Arab Emirates, like

Katie Robbert 17:17
you like, this is information you would already know about your business. Like if you’re just figuring out now that you are working in a regulated industry, and you might be audited? You know, that’s a whole different conversation if you aren’t aware of this. And so for companies that, you know, don’t have those regulations and rules already in place, companies like ours, who, you know, we’re not going to be audited by the number of people coming to our website on May 21, three years ago, on a random Saturday, like nobody cares. So why are we holding on to that data? And those are the questions that we need to be asking ourselves, but answering honestly,

Christopher Penn 18:00
I recall A certain former company we worked with when it was acquired. We were asked to produce five years of website data for the acquisition. So if it there’s a they a remote but nonzero possibility, KT that in three years, we sell Trust Insights for a billion dollars. And part of the due diligence says hey, we need five years of website data. And that

Katie Robbert 18:25
all goes through that scenario planning as you’re trying to figure out and so and that’s a totally valid question. And so internally, the questions we would be asking are, you know, do we think that there’s a chance that we might be acquired? Do we think that there’s a chance that, you know, what are the different scenarios that would would exist that somebody would need five years worth of data? You know, and so those are the different exercises that you should be going through with your team?

Christopher Penn 18:54
Exactly. I will say, if, if you do want to buy Trust Insights, $3 billion, just email us, you can just ask for Katie.

Katie Robbert 19:04
For me directly. Okay, satisfaction.

Christopher Penn 19:08
Okay. So method one for backup is just manual export. Method two, for backup is selective export from the Google Analytics API for the key variables that you want to know. Method three, is to use a third party service of some kind that can rip all the data out of the Universal Analytics and store it somewhere else. And the system that we have had success with and a lot of struggle with it for doing this is a open source system called matomo. So matomo, which you can [email protected] allows you to set up a Google Analytics import and connect it to your existing Google Analytics account and vacuum up the data. And there’s two flavors there is the cloud hosted flavor where you can just sign up for the Cloud account. And then you go in, for example, this is the dashboard, you go into the your, your setting, go into System, choose Google Analytics import, and then you run face first into the configuration of this very complex process to export. That’s flavor, one of matomo. Flavor two of matomo, is to set up your own server that you know that you host with their software, because it’s open source software, again, set up the importer and stuff like that. And then backing up the data, the two differences that you would want to tackle if the cloud hosted version has a limitation of two years. So it can only hold on to last two years worth of data, the self hosted version, you can have it all. So for example, when I go to backup, my website, I’m going to use the cloud was the server hosting one because I would like to keep all 13 years of my website’s data, no special reason I just like having it because I’m a data hoarder.

Katie Robbert 21:05
admitting it is the first step. It really

Christopher Penn 21:07
is. Now, the other thing is both methods, but particularly a self hosted method has a very high technical bar. It is I did a setup yesterday for for giggles. And I documented the process and it took me who’s a reasonably technically competent person about two hours to do it, we prepared a very special music video of the experience that is not two hours long, not two hours long. It’s only 90 seconds but.

So that that is the process?

Katie Robbert 23:31
Got it? Write it all down? No. And that’s it’s worth noting that if you want something that captures you know, there’s a lot of companies that do have 13 years worth of data. And they have valid reasons to keep all of that data. So they would need to go through a process like that. But being clear about what specifically they’re collecting and keeping and making decisions on. You know, and again, we sort of keep joking, half joking, but it’s the kind of thing that we can help with. Because, you know, if, Chris, if someone came to me and said, All right, you have five years worth of Trust Insights, data, you need to keep all of it. I would go to you, I’d be like, okay, Chris, good. Got got to get some time, because I’m not the technical person on the team. And so either version of that in matomo is going to be a struggle for someone like me.

Christopher Penn 24:27
Yes, particularly the API setup, because you have to go into your Google Cloud Platform account. And if you don’t have one, you need one of those. First, you have to go into Google Platform account, set up an app, and then take the app, setup off credentials, set up the endpoint the off endpoints, authorize them, then take the JSON authorization file, bring it into matomo to authorize it and then go through the web auth for that, so there’s this multiple steps that apply to either version

That sounded like words.

Katie Robbert 25:04
I mean, it was words. I don’t know, once that’s

John Wall 25:07
built, okay. You just use that as your analytics too, though, I mean, or is that solely an export and database?

Christopher Penn 25:13
You absolutely can. And in fact, there are. When we switched over to Google Analytics 4 Back in October of 2020, we installed matomo analytics on the Trust Insights website as a backup, because at that time, we weren’t really sure how well it was going to work. And so we run matomo, in parallel to our website, I’ve had it running on my own website for five years now. There’s actually a version for it. If you run a WordPress site, there’s a version that runs right inside your WordPress instance, that requires much less setup, and does a really terrific job. So yeah, you can use that as your web analytics as well. You should be using it as your web analytics, if you are in the EU, right? If you are doing business in the EU matomo is one of the very self hosted is one of the few web Alex packages is fully GDPR compliant. As of today, June 1 of 2023, Google Analytics still is not GDPR compliant, GA four is still not compliant. So anytime the EU could drop the hammer and Google say, Nope, you’re not allowed to use here. matomo would give you that option.

Katie Robbert 26:24
So technical hurdles aside, what are are their pros and cons to holding onto your Google Analytics data other than, you know, it makes you feel comfortable.

Christopher Penn 26:41
So I mean, the pros is you have the data, if you do need it, even if it’s just for that once a year anniversary look back blog post right there. If you want to do any kind of long term trend analysis, like multi year analysis, you would have, you want to have that available. If you look just like data as a security blanket, it’s, it’s available. And, you know, five years of data for us worked out to about 104 megabytes. So it’s not exactly a huge piece of information, you could stick that on a flash drive, you know, and just put it away somewhere and it’s there in case you need it. The cons are yeah, there’s the technical hurdles to get set up and get the data extracted is sizable. Even for a technically proficient person, it is sizable to go through all the steps, it’s not something that people would have a lot of practice doing. Because it’s nothing to do all very much. There is there’s no downside risk to your data. Obviously, you can’t erase data from Google Analytics. And if you store it in an open database format of some kind, then there’s no risks there. But there it does consume some space, not a lot. But but some. And the big thing for folks is it consumes time, it consumes time and resources to do the process. matomo takes backs up about seven days, every hour was what it was pacing at. So it’s going to be a couple of weeks before it finishes with our five years worth of data, it’s gonna take about a month for my website with 13 years of data. And so if you’re, if you need it, and you have not gotten started, and say Google announces January 120 24, it’s all going away and you start this Christmas week, you are in a lot of trouble, right? So this, this would be one of those things where if you know you’re going to need it sooner is better to get this going.

Katie Robbert 28:31
Yes, there are risks. So we’ve seen this with past clients using Universal Analytics. Is there a risk that you might accidentally export and store PII information, so personally identifiable information? And so how do you make sure you don’t do that?

Christopher Penn 28:56
Well, first, if you have PII or SPI within your Google Analytics, you’re in violation of the terms of service. And you could be kicked off Google Analytics literally any moment if they discover it. So you definitely want to audit your Google Analytics instance, to make sure you’re not capturing that data in the first place. The place where that happens the most is in query parameters. So if you have an older form on your website that passes things through what’s called a get post a get versus a post mechanism, you will have like an email address in the URL of the site that gets trapped. The easiest way to deal with that, once you’re done with the extraction and you have in a database of some kind, you can write a short script that can look for URLs with the apt symbol because there’s no you there’s no website URL that reasonably should have the Add symbol in it. Right and that’s the that’s one of the easiest tests to look for is like look for the at symbol in a URL. If there is you probably have PII in there somewhere and then scrub that out. And you know, cautiously, you know, write some scripts to remove that information from from your website, you can substitute it with like a numerical ID if you happen to need IDs of some kind, or an MD five hash, but you definitely would want to scrub that out. And then, as with anything, you’d want to secure that data, like, you probably should not run this on a public web host with no passwords, but that’d be a really bad idea.

Katie Robbert 30:27
And I think that though, those are some of the pitfalls that we need to think about. Because it’s, you know, to your point, the technical hurdles aside, there’s other considerations with exporting and storing this data. I can tell you horror stories of what a company I used to work for did with protected health information, because they just, you know, we were all ignorant to what those regulations, we’re in terms of proper data storage. You know, and so that’s probably step one is get educated on data storage securely, and what is considered personally identifiable information. And if you are accidentally collecting any of it, because it does come with big federal fines, like it’s not an inexpensive issue, if you get caught, you know, even if even if you didn’t mean it, it’s still the federal government doesn’t care, they’re still gonna find you, you know, at least five or six figures.

Christopher Penn 31:29
Yep. If you are in a highly regulated industry, your best bet for safety is to buy one of these like little mini PCs that you can get like on Amazon for like 200 bucks. So as long as Scott, you know, 512 gig of storage and like 16 Giga RAM, like 300 bucks, or preferably have your IT team do this, right, you haven’t set this up in your data center, you run Ubuntu Linux on it, the 2204. lts version, which is long term, long term safety. And then that you install, like everything that you just saw in the in the musical video, you install that on that machine, that machine is then behind your firewall, in your data center, your data is on your network doesn’t leave your network, that is the safest way to protect it that way, it’s totally under control. And if you’re audited, if there’s a Security Security team comes by, that’s the way to handle it that way, you know, for sure where your data lives, it’s in your data center on a little box.

Katie Robbert 32:33
Yeah, don’t just put it into this little box and then stick it in the top drawer of your desk. That’s a bad idea.

Christopher Penn 32:39
Yes, that’s about it. I mean, even if you have like an old laptop laying around, you could do that. And you know, if the laptops, you know, less than five years old, again, work with your corporate IT team, get it into your datacenter. And, and it’s a good way to repurpose old hardware, there are versions of matomo, that you can scale down to even run on a little raspberry pi, you know, those little hobbyist boxes, and I might not go that route. Because the you know, they’re not built for enterprise grade reliability. But if you got one laying around and you got a small website, and you want to have it under your control, that’s that’s not a bad way to do it. But no matter what you do, having a clear plan of data security is a good idea. Like, you know, we have one of those server boxes in my office here and stuff is it because I want it on my local network. So I can work with it quickly. But it’s also behind a firewall, and you know, it’s got its own firewall on it as well. So that data that’s stored on it can’t go anywhere.

Katie Robbert 33:38
Unless someone breaks into your house and steals it, which is a different conversation that we should probably handle

Christopher Penn 33:44
it it is but again, as long as you practice good data security, and you don’t have short passwords and stuff like that, you know, all systems nowadays have encryption built into them. If you’re not using it, you should. But you know, even if someone wanders off with you like your your laptop computer, or at least the computers we use, we use MAC’s and these things have File Vault and good luck getting data off of there. The federal government can’t. They’ve repeatedly asked Apple for backdoors and Apple’s been like no.

Katie Robbert 34:16
John, questions, thoughts considerations?

John Wall 34:19
Yeah, no, I would definitely Rob Chris’s neighbors before I would rob him like I know that that’s a huge mistake to invade the pendant household. So yeah.

Katie Robbert 34:32
Okay. All right. So migrating Universal Analytics data. First, you have to decide whether or not you’re going to need it if you’re going to need it. There’s a few different ways to handle it. I personally feel like exporting a few CSVs and sticking them into some kind of a presentation and then filing it away for a rainy day is going to be the best bet for us. Chris disagrees. And so he went down a more technical route where he actually exported data from To the API, so you can have a full database table. And then the third most technical option is to import it into a system like matomo, which does take a lot of technical expertise. And if that is not you, you should definitely find a consultant who can do that for you.

Christopher Penn 35:17
Exactly, I think most organizations will be fine with option two, you name your your key variables that you need to save, you export from the API, and you store that in a database somewhere. For for the kinds of analysis, most people need to do on a year over year basis, that’s probably gonna be good enough. But again, you have probably six ish months, sevenish months to make the decisions make the decision soon, like, make the decision before July one about what you’re going to keep, and then do the migration cuz you have to worry once July one hits, there’s no new data to add, right. So you don’t have to worry about keeping up with it. But when July one hits, for for Trust Insights, I’m going to rerun the scripts. For us, I’m going to run from marketing over coffee, I’m going to run for my personal website, we’re going to have the data stored safely. And then we will we will have a nice drinking party for Universal Analytics, we’ll send it off with a champagne toast and, and and all continues to suffer with Google Analytics 4. So to what Katie was saying, if this is something that you would like to have done for you, hit us up. We’re happy to help if you want to do it yourself. We have a course for that. Go go take it. You know we probably should add an export chapter to the course. But maybe we’ll do that in July as as our as our farewell ceremony for that. But any any parting words?

John Wall 36:55
Get your act together. Clock’s ticking.

Katie Robbert 36:58
Those are gonna be my parting words too. So I think we’re good.

Christopher Penn 37:01
All right. Thanks, folks. We will see you all next time. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources. And to learn more. Check out the Trust Insights podcast at trust AI podcast and a weekly email newsletter at trust Got questions about what you saw in today’s episode. Join our free analytics for markers slack group at trust for marketers, see you next time.

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Trust Insights ( is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.

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