So What? Introduction to Google Opal

Well, hey everyone, happy Thursday. Welcome to So What The Marketing Analytics and Insights Live Show. I am Katie joined by Chris and John. Howdy, fellas. And we got the high five.

We are all in one place for a hot second. Just this week. Well, you know how it goes. Uh this week, since we are all here, we are talking about Google's Opal. And so you may be familiar with tools like N8N, you may have used tools like Zapier or make an Opal is akin to these tools.

It is an automation tool. The difference between Opal and some of the other tools is that at this current moment, it is restricted to just things within the Google workspace. So your Google Docs, your Google Drive, all that sort of stuff. Whereas other tools like N8N, which we went over on a previous episode, which you can catch it. Trust insights.ai slash YouTube.

Go to the so what playlist. Uh Opal does not at this time integrate with tools outside of the Google Workspace. We should probably start with what the heck is this thing? So the place you would go to find this, uh, and you do need a Google account for it, is Opal.google. That is the the new address, the new domain.

And once you sign up and say yes to everything, and by the way, this is still technically part of Google Labs. Hence the experiment button up top. That does mean that at any point it could just vanish if Google decides, you know what, we don't think this is working out, and it just goes poof. Um, so that there's there's that big caveat because it's Google Labs, that also means your data privacy is not guaranteed because they're looking at the data to see how people are using this tool to make product improvements and things. So please don't use this with confidential data.

That's kind of a big deal. I mean, that's kind of like a good best practice in general. It's a good best practice in general, but this is a free tool, and what we always say is if you're not paying, you are the product. So, what have we got? Um, in Opal, once you start a new app, this is it's really an agent builder is what it is.

At the bottom of the window, there's a prompt box where you can describe what you want, and Opal will try to figure out uh what it is that you that you're trying to do and try to build some kind of app. And then up top are four different node types, four different controls. Um, the controls are user input where when you turn on the app, it will ask the user for something. You could say, you know, type in the address of your website or this or that, or anything that the user could provide. Then the second class of nodes are what are called generate nodes.

Generate nodes use AI and the tools that are available are Google's Gemini 2.5 Flash, which is great for things like summarization, 2.5 Pro, which is the smartest model that takes longer to think things through. There is an agent version of Flash called Plan and Execute, where it will do its own multi-step assessment to say, how should I try to approach this task? Or you can tell it here's how to approach this task. Um it will try to carry out. There is the deep research tool, which you could say, uh, you know, do some research on uh the marketing over coffee podcast, and it would spin up Google's deep research tools to go out on the web and gather all the data and return it.

Then there's confusingly two different image generation capabilities: Image N4 and Nano Banana. Generally speaking, you should be using uh nano banana because it's the the newest of the two. There is a audio generator that will take text and create an audio file. There is Google's VO model that will generate a video from an output, and then there's Liria 2, which will generate instrumental music from text and is generally terrible. So that's the second class of nodes uh is generates.

After that, there's a third class called outputs, and this is what do you want to what do you want this thing to do? Where do you want it to send stuff? Do you want to create do you want it just stuff to be displayed exactly the way it comes out of the model? Do you want it to try and make a web page from your results? Do you want to make a Google Doc, a Google slideshow, which is just purely text on slides, or a Google Sheet?

So that's the third class of nodes. So it's input, do stuff, output, and then the fourth one is uh integrations. So this allows you to upload a file, uh, allows you to connect to your Google Drive, allows you to connect to a single YouTube video. Uh, you can provide a chunk of text, or you can provide an image or drawing, and these can act as inputs uh to the different processes. So those are the four node classes.

It's a good idea to know them and to know what their names are and what they do, because very often when you're using the prompt box below, uh Opal will say, you know what? I'm not sure how to do that. Maybe try this instead. And it comes up with some nonsensical suggestion, or tells you, you know what, you just can't do that. But if you know what the node types are, you can say, no, no, you have a plan and execute node.

Use it to tell it what to do. So I feel like there's a logical place to start with all of this. There sure is. A race. What would that be, Katie?

Got it. I win. It's the 5P framework. The reason the 5P framework is so fantastic for something like Google Opal, where you can give it a prompt versus trying to like an N8N, it can feel overwhelming because you're trying to connect everything yourself, and there's so many different options. Uh Opal, a lot like Google Gemini, because they're both built by Google, you can add in the prompt and tell it what you need it to do.

If you structure your prompt with the five Ps, I can almost guarantee you're gonna get a lot farther, a lot faster. The reason is because you want to be very specific about what it is. And specific in places like purpose, what is this thing for? What is it meant to do? People, who is gonna use it, who is gonna get the output.

Process, probably the most important part of this whole thing. If you have a very clear defined process, if you have an SOP, if you have documentation, that's a good thing to add to the assets. So the assets are basically just your attachments. And so you can upload your document, add it from Drive. You know, if you built a bunch of walkthrough videos and put them on YouTube, that's a great place to start.

Make sure that you are clear about the process. If there are steps that you're not quite sure about, that's okay. But if you have like the high level, you know, step one, it should do this, step two, it should do this, step three, it should do this. You're gonna get a lot farther. The platform.

Well, we already know that we are restricted to anything within the Google workspace. And so, you know, you're using your Google documents or YouTube videos or slides or sheets, and then the performance is your output. What is it meant to do? And so Chris showed the output. So, you know, the performance is I can then go to a web page, or I then have a slide deck that tells me what I need to know.

So I highly, highly, highly recommend using the 5P framework to structure your prompt in Google Opal. Okay. So let's build something in Opal. Let's let's create something. Uh and because the five Ps are so useful, let's just use the five Ps as the actual prompt.

So let'll make this a little bit bigger here so we can all see what's going on. Um, so let's start with something simple. Let's start with uh we're gonna use Opal to construct a newsletter. Uh you so we're gonna say we're gonna say the the purpose is to build a newsletter about current topics in AI for people to know the most important stories in AI this week. The newsletter audience, business users who want to know what's happening AI this week.

Uh the newsletter should be non-technical to serve their needs. The process um use the generate um deep research node to find news stories about generative AI, large language models, chat GPT, etc. in the last seven days um in English, and then the platform is obviously open. We don't have to spend a whole lot of time there, but we want to we want to make sure that we're using the deep research node. Um for this one, we might want to have uh a use use the uh generate plan and execute node to decide which of the news stories is most relevant to our people and order the news stories in descending order by importance, and then we're gonna say use the output node to generate a web page with auto layout for the results.

Now, this is reasonably clear in terms of what we want this thing to do. Any any suggestions or tweaks, Katie, you would make on this? Um, well, I don't want to jump ahead, but when it comes to people, I feel like it's a great opportunity to bust out your ideal customer profile that you built along with us from previous episodes of the live stream, um, which you can get on our so what playlist. I've done something very similar to this in Opal. And what I found was the more clear I could be about the end user, the better the results were.

Because as you know, Chris, and as you know, John, there's no shortage of articles coming out about AI every single day. A lot of them are developer focused, a lot of them are overly technical. So I personally don't feel like it's enough to say it's a non-technical person or it should be non-technical. I feel like the more you can specify, even if you don't have an ICP, if you can sort of list job roles. So this is for C-suite, this is for mid-level managers, this is for uh directors who are overseeing marketing teams or that kind of thing.

That really helps uh narrow down the amount of non-relevant data you're gonna get back. Okay, so let's use that. So let's go into the canvas here and get rid of everything, clean things out. I'm gonna start by uploading file. And the file I'm going to upload is the trust insight's ideal customer profile.

So that's already are we gonna be we're gonna put that in here as the starting point so that our prompt knows that it's there. So let's go ahead and take this and see if Opal can in fact correctly parse this to understand what it is that we want to do. And if it can't, we can always build these steps individually. One of the great things about using the five Ps is that if you sit there and think about this, like this lovely error, uh you can say, well, if I think through the process, then even if the tool can't for some reason understand what it is trying to do, I can use, I can follow the five Ps to their logical conclusion to do this. So yeah, it's just not happy with this.

So let's take those five Ps. We're gonna have our generate uh deep research node. We'll have our generate plan and execute node, and we will have our output node that will be the web page with auto layout, and we're gonna tie this, we're gonna drag these little things together, chain them together like so. The only thing that is missing now is there's no prompts. When you use the automatic box at the bottom, it will generate the prompts for you, which is very nice in the individual notes.

Because we don't have that, we have to do this manually. So if we go back to our five Ps and we say the purpose is to build a newsletter about this, um we're gonna say the newsletter is this. There's the ideal customer profile. We're gonna use this to find the last seven days. So now this node has effectively the five P's as its prompt.

Then in our plan and execute, we're gonna say, decide which of the news stories, just like so. And then we just make sure that's connected to this output. Now, if whoa, if we did this right, and we have our ideal customer profile, we have these things, then when I hit start, it should be able to immediately get started with these different components and try to create the newsletter. This, because it's using deep research, is probably going to take anywhere from five to fifteen minutes for it to generate its outcome. But you can see generally the thought process for putting this together.

Do you have to use deep research? You don't have to. However, it's not going to be quite as good as Deep Research. How so? Um, Deep Research is an agent system of its own that when it when you give it a search thing, like news stories about AI, it recursively goes and checks its results.

It says, I've got some news stories, I'm gonna look at them. Nope, that's not it. You know, it will keep trying. Like when you watch regular Deep Research in Gemini go and do this, it will say, I found these articles. Nope, they weren't right.

I found some more articles. Nope, I'm gonna go back and keep doing research until I satisfy the conditions of the original prompt. Whereas if you you don't use deep research, it will then just say, Hey, I did a web search. Here's a bunch of articles that came back, done, ready to go. And it's like, no, no, you need you need to think about this.

Like, actually read the the search results you got and decide how relevant they are. And the non-deep research web search just doesn't do that. It's almost like managing people. It is. And in this case, knowing the individual nodes and what they do is really helpful for being able to pull off something like this.

So, John, when you look at tools like Opal, um, do you immediately start thinking of your, you know, marketing over coffee workload or you know, the work that you do with Trust Insight, which is business development. Do you like start thinking, oh, I can build an automation for X? Like, do you do those kind of use cases immediately become apparent to you, or do you just like to play around with tools first to see what they do? No, actually, this is the process right here. I wait until something is baked enough so that Chris will do a webinar about it.

And if we get to the webinar and it doesn't suck, then I will start playing it. And yes, I have a whole list of like here's a whole bunch of stuff that could be automated and done. But yeah, there's just been, you know, so much failure. It really is going to come up. You know, are we going to get a bunch of graphics with people with six fingers and stuff?

Or is this actually finally going to do a deck of slides for us that you love? That's really, you know, but yeah, it's it's funny that this is the process because there's there's just so much going on. There's so many tools all the time. I finally can't even keep up. I'm just like, whatever.

And so this is the filter. So yeah, and even like we're at the point now, this is still like VC smoke and mirrors. Like everything up to this point looks awesome. Of like, look, it's like workflow and it's connected. And we plug in the prompts, and it all looks awesome right now.

So we'll see where we end up in about 15 minutes here. But um, yeah, you know, so so far I'm intrigued. This looks very cool. This is you know, this was the promise of N8N that everybody was kind of like, yeah, it's just not working yet. So it has actually finished its work.

Uh in the right-hand side, what you'll see is four different things. There's you know, obviously, themes you can make for these things. Um, there is the preview, which is the the final result of what it's spit out, and you can download the preview if you want as as the output, or you can uh share it with somebody else. But most important is the console. The console tells you what happened at each step in the process.

So we can see here, for example, the first Gemini call. Uh we can see the individual parts and what it did. And like you can see, it even wrote its own prompts to try and do this deep research. And we, if I start expanding this out, we can see what those individual components are. So it did a web search there, give this query.

Um, and and dig into what it tried to do at each stage throughout the process. And this, by the way, is super interesting. If you are the kind of person who wants to know what's going on under the hood, you can see in great detail what it tried, what uh Gemini, what Google decided it uh the pro the steps of the process were going to be. So start the research, do the research. Here's here's the file data that contains the ICP, etc.

Um, the model response, and it said, here's a summary of generative AI news from the last seven days, regulatory landscape and ethical considerations, industry adoption and investment, emerging trends and development, conference, the artificial intelligence Marseille 2525 form is taking place, public sector applications, and of course, you can see all the news sources. Uh, and its responses, and ultimately uh how it came up with its answer, and then it creates the web page output uh and and makes the nice product at the end. So, this console is the audit trail to understand did you do something what I wanted you to do? Which means that if it gives you a work product you don't like, you can go through the audit trail and see where it went off the rails. Like maybe it did okay with the research, maybe it found the right things, but then maybe our prompt in the ranking section was just so poorly written that's like I don't know what to do here, and it it as a result, it did not do what we're supposed to do.

So you could say, okay, well, this is the node that broke because the console shows that it got good research and then just mangled it. That's assuming you understand everything you're looking at in the console. So, could someone who isn't you, Chris, take a like basically take a copy of everything that's in the console, give it to Gemini, for example, and say, I didn't get what I wanted. Where did it break? You absolutely could do that, yes.

Um, you could do that, or you let's actually let's give this try uh because I'm not sure that this works. Um, take a look at the rank news node. I want it to carefully arrange and screen the news articles based on the trust Insights ideal customer profile. Can you improve that node's prompt? We'll try this here and see if this works.

You can also wow. No. That is quite the error message. Um, let's see if we can do this here. Say, I want you to fix this.

Because there is a a prompt button. Well, that's not like that's literally what I typed. That doesn't help anything. I think we broke it. Yeah.

Oh, there. Ensure a thorough and precise screening process. Yeah, okay. That's that's better. It's saved.

I'm gonna refresh because that is just lovely there. All right, so that is a better prompt now. So if we were in the console and we're like, we look at the steps, um, create the newsletter. Oh, it there's there's no steps here because it hasn't run it, it erased the last run. But this time through, if we were to run it again, we could say, okay, this is a better prompt.

Same for this one here. We could hit the the magic button here and literally say improve this prompt to have it be more specific to our ideal customer profile. I would also add to that, I think one of the things that people find helpful is why is it relevant to you? So, what's the so what? What's the takeaway?

Why should you care about this thing? Yep. Um well, so that's something we'd want to do in this section here. Say, as you rank the news stories, ensure that there is a clear so what to our ideal customer profile. You know what?

Maybe that should be its own node. Something it can take the newsletter stories and write that separately. So let's add that in. We're going to add in a new generation node. Let's glue that there, delete that one, and glue this here.

Take out this and say, um take the news stories from rank news and then analyze them against our ideal customer profile. After that, write a short paragraph for each news story explaining why that story is relevant to our ideal customer profile. So we're gonna add that in. I understand you want your request is incomplete. I'll just do it myself then.

All right. So now we've got an extra node in here that will that will then process this and hopefully come up with an even better answer. Let's see if I can bring the ICP back in here. Yeah, there we are. And let's make sure the ICP is available here as well, so that it's available in all the steps.

Now let's hit start and see what happens. So what we've done is we've added another step to the process to say, write the so what. Don't just throw the stories at me, but write the so what explain why this is relevant. And I think that that's a key piece. I mean, not just for you know, prompting, um, but for this kind of an exercise.

So you know, we know a lot of people want to create their own version of an AI newsletter. Um the big problem I see is I don't I don't know which things I should pay attention to. There's so much information. You know, even our you know friends over at uh SmarterX who do a fantastic job of you know the roundup of all the AI news every week it's still a lot of information. It's still a two hour podcast.

It's still a two hour podcast and I don't know where I need to be paying attention. And you know I feel like when people look to us to really guide them our job is to make sure we're being very specific and clear about what they need to look at because there's there's just too much information. Now almost every use case I've seen for Opal so far and admittedly I've only seen like four but every use case I've seen so far has been build a newsletter or gather some articles. Like what are some other use cases for a sys for a system like Opal, especially when you're constrained to the Google workspace that's a really important question because it requires you to think about the inputs and the outputs. So what things can you put into this knowing that you have nodes that do summarization that can do extraction rewriting classification planning synthesis, and obviously generation.

What would you do with those capabilities? So, as an example, if I had in my Google drive, say I had a document about trust Insights and had a document about our our main competitor, we'll just say Accenture, right? I mean, even though our annual revenue is like Accenture's cream cheese budget. Um I had those two things there, and I had a plan and execute node that said, here's a SWOT analysis, uh, perform a SWOT analysis, then build a strategy, then analyze the strategy, then do some role play and scenarios. All think about all the things that you would do in a sequence in a chat in a regular Gemini prompt window.

You would instead make individual nodes. And so if you had all those nodes collected, and then you had a little Google Docs thing at the end, instead of you having to copy and paste those prompts over and over again, or even have them in a multi-step gem where someone's just talking, you know, the gem is asking back and forth, oh, and now what about this? And now what about this? You would have nodes that would do that if they were preloaded with all the knowledge available, so that you just hit go. Maybe you upload a new piece of data, like you know, this month's analytics from the company, and it would build you a new strategy or a new tactical plan or a work plan.

Um so it is content, but it's not content for public distribution, it's content that you would use. Like here's the SWOT analysis and quarterly competitive analysis for this quarter. I in literally uh 15 minutes, I'm gonna be doing a webinar with our friends over at SMPS. And one of the things that I talk about in that webinar is how do you do scenario planning with AI? Well, suppose I had three nodes here, worst case scenario, you know, steady as you go scenario, best case scenario, and I had all the documents on the left-hand side, and I said, okay, do some scenario analysis, and it passes them all through, and each node fires and does its own thing, comes up with here's you know the best case scenario, here's the worst case scenario, et cetera.

And it comes up and then it glues all the analysis together. I don't have to prompt that anymore. And I don't have to run it through a gem anymore because now it's all baked into this little application. Hmm. So here in our output, we see key topics for data-driven marketing leaders, executive training preparedness, LLMs as a AI chief of staff.

That's actually interesting. Um we have enhanced capabilities, personalization. Now you'll note there's some things here that could use improvement. Like, what is the story? Like, could you put the URL to the store here so we can see it?

Uh, those are all things that you would iterate with this. But that's essentially that is this stage here, right? So there's this is just as make the thing. I might say we need to have these features uh in this output node to condition what you want it to look like. It's once again almost like doing the five P's ahead of time is gonna tell you all of those things.

Uh, you know, because to your point, the output, make a web page. Okay, why would I look at a web page I can't interact with? Um, you know, do your user stories as a persona. I want to sew that. You know, as the CEO, I want to see the top 10 articles so that I can read them and stay informed.

Well, in order to read them, I need to be able to click on them. So there's like those little nuanced things to your point, Chris, about like you can iterate with it, but like we always say, the more work you do up front in terms of the requirements gathering, the less development you'll be doing on this side to try to like perfect it and get it right. Um, be honest. How many times a day do you use the 5P framework? Every day.

I mean, it's all about people for me, really. That's that's always the missing link for everybody. Because you run down the list, you're like, oh yeah, you've got your platform straight and you've got your process. You know, have you talked to anybody about this? And it's always, oh, well, no, you know, we we're just building this stuff.

So uh yeah, no, I live by the five Psynding that last P in there is what closes the business. Mm-hmm. Yep. So I just added an audio summary node and gave it the same instructions. Now, uh, there are limits.

I believe Opal gives you either two or three video generations a day, and I think five audio generations a day. So you'd want to test out and make sure the rest of the workflow works first before you add in those very expensive things. I imagine if the product ever does come out of beta uh and and successfully emerges, that it will either tie to your Gemini uh subscription or tie to your Google Cloud subscription, and you'll get a bill uh for when when you use audio and video because those are very, very compute intensive products. But for now, they give you some free samples. And I think that again, it sort of goes back to why are you using in the this in the first place?

And is this the right tool? Um, you know, John, to your point about it's about the people, a lot of times we see people jumping right to the platform, like, oh, I found this great new thing called Opal. Let's figure out where it fits in. Versus, hey, we have all of these different processes that we want to automate. What is the right tool for it?

So Opal might connect to all of your workspace, but N8N might, you know, cost less in compute to create a video or you know, whatever the situation is. And that's, you know, a really just good reminder of why you want to, you know, do that analysis up front before picking the tool. Trying to retrofit a tool into everything else is really costly, and 10 times out of 10 doesn't work. Yeah, exactly. So let's give this a listen, see how this sounds.

We'll play just a couple seconds of the Opal output. Explain what they are, why they're the top three and what the ICP should do with the news. Executive training and preparedness. While executive training and preparedness is a general leadership topic, it is relevant to the data-driven marketing leader's commitment to continuous learning and the strategic imperative to foster data literacy at all levels. Okay.

So it's basically taking the news. So it sounds like we would need another node between the analyze news and the create audio summary that would take it that would basically do the transformation into a speaking script for the audio. Because it sounds like the audio node literally just reads aloud whatever data it's getting. Yeah. I think the other thing that's interesting, and you know, we would continue to iterate it before anything going public, is the why is it relevant to the ICP feels more like an internal report than something that you would publish externally and say this is why it matters to you.

It's really more of an internal, hey guys, this is why I chose this, and this is why it's relevant to your ICP. So, you know, is it a go no go? Great, thanks. And that's a part of the process that it's like, no, just make those decisions. Just go ahead and do the thing.

I don't need to triple check it, because that's actually creating more work for me to have to go through and read everything. So it's just it's a slight tweak, but those are the things that you know, as you're prompting, you want to be aware of. Exactly. And as with so much of the rest of generative AI, when you look at the system, what's really powering it is the data that you provide. We always say the more data you bring to the party, the better AI is going to perform.

In this case, we're bringing in our entire ideal customer profile. I could plug in our entire sales playbook if I wanted to have it, you know, I could bring in from Google Drive something like CRM records, uh, where we could we could have uh you know any number of uh pieces rows of data and say you know, based on a sales playbook, do this level of analysis on a row by row basis. So all those things are possible if we know what each node does, and we know and we have the data. Um do it using Opal without data to me, is you're basically relying on Gemini to have the information, and that's not a not something I would suggest. Just trust the internet, it's it's all true.

Yeah, yeah, no. Um so any other uh questions, Katie, on how we're gonna start using this thing. I would say in a hierarchy of tools and technologies, there's prompting, there's gems, there's opal, and then there's N8N, and then there's code, would be sort of the hierarchy. So it kind of sits in the middle of the hierarchy. It's harder to use than a gem.

Um, it's easier to use than N8N, and obviously, because of its limitations of the Google ecosystem, it's if you're using chat GPT, this is not available for chat GPT. They do have their own agent builder, and it's terrible. Okay. No, I mean, I think it makes sense. And uh, you know, again, I will just you know, die on this hill with anything.

Start with the 5P framework, you know, get your requirements right. You can get your own copy at trust insights.ai/slash 5p framework, use it, love it, learn it, live it, all the things. I would uh add to that also, um, if you want to use it, learn how to use it really well, we have a course for that. Like we literally have a course for this on how to use all these frameworks together uh to do some really, really cool stuff. Um, and a lot of what we talked about today, stuff that's covered in the course.

Yeah, that's that's factual. There are no lies, no hallucinations, no, no, no, no hallucinations. Um, what do you think? So now that you know Chris has demonstrated the usefulness of the tool, how are you gonna start using it? What what kind of things are you thinking about?

Well, the big thing is to start putting together some of the workflows and see what kind of results we get out of it, you know. I mean, it because it does cross that line. Uh, there has yet to be a good agent builder, you know. There literally has not been one that can build one that's predictable and delivers quality results every time. And so this is the closest I've seen to that.

Like I it's the the way Chris had laid it out as far as code N8N, and then this, in some ways, I feel that there's two accesses going on the I feel like this could invalidate N80. Like this could just destroy NADN if it does a better job of connecting and delivering results. Um, so yeah, there's a lot going on in trying to get these products to mature, but it is great to see because we've all seen this point where writing great prompts and getting stuff just doesn't scale. You know, you've got to have an agent to get to the next level. Um, otherwise it's just gonna be, you know, the rest of your life sitting there turning the crank, which is fine.

If you get better results than you've ever received before, that's great. But until you get a way to automate it and make more happen with it, it's you're not gonna get that next level of productivity. Yep. Bring data, bring as much of your data as you can. The tool is not great at getting it.

I tried having it retrieve the contents of our YouTube channel. Um, and one out of five times it worked. When I extracted that data myself and I just dropped it in as an upload, perfect. It was it was great. Um, one of the things I want to try doing with this is I want to try having it write a book from our our YouTube channel transcripts, uh, both from So What and from N Ear Insights, but it couldn't do that.

It it was like that's too much data. Because I I asked it for just just pull all of 2025. It's not that much, it's only a hundred and some odd episodes. And it's like, no, no, I can't do it. Like, okay, I can see I'm gonna have to do this myself.

So bring your own data. All right. Well, that's gonna do it for this week, folks. We will talk to you all on the next one. And if you're gonna be at the Marketing Props B2B Forum next week, uh Katie and I will both be there.

Come find us and say hello. We'll talk to you all on the next one. 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 Trustinsights.ai slash TI podcast and our weekly email newsletter at TrustInsights.ai slash newsletter.

Got questions about what you saw in today's episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai slash analytics for marketers. See you next time.