So What? How to Identify Marketing Opportunities With Generative AI

Well, hey, howdy, everyone. Happy Thursday. Welcome to So What, the Marketing Analytics and Insights live show. Once again, I only know it's Thursday because we're doing this. Uh otherwise the days all blurred together.

How are you guys? Doing good. Living in the poly tundra here, which is March. Here we go. Well, you know, because it was the first day of spring, so now you get snow.

Yeah, exactly. The ski areas crying in their beer, unfortunately. Oh, well, let's do something we're not gonna cry about. We are gonna talk about how to identify marketing opportunities. So today we're gonna use the 5P framework to gather our requirements.

Uh we're going to extract insights from generative AI using our um ideal customer profile, and we'll go over what that means, and then how to identify marketing opportunities uh from your data sets. And so this came about because we've been doing a webinar series on generative AI for marketers. And you know, we've done it a couple of times, and now we want to start addressing other needs in the marketplace from our customers. We asked them a couple weeks ago, what kind of content would you want to see? And they gave us a list of different um, they voted on different categories, different verticals.

So real estate, technology, education, e-commerce, so on and so forth. And that's the data that we're starting with. That's our starting place for figuring out what are our marketing opportunities? What do we do next? And so you can see uh people want to know about uh what they want content about general consulting, they want content about education and so on and so forth.

And so that's where we're starting. So, Chris, um, we said we wanted to do this on the live stream because we wanted to walk through our process of how we're going to take this data um and actually turn it into something valuable uh for our audience. Right. So the first part we have to look at is in the data itself, what do we see, right? So agencies as consulting is by far the the leading, uh, followed by sort of creative then higher ed and all education, e-commerce and financial services.

So if we're going to create some content for a vertical, uh these would be the verticals to serve. Now, here's the important question. This this is statistically useful information, but does it align with the essentially the cost the kinds of customers we want? And that's a question that um, you know, to what you were saying, Katie, when we think about uh the five Ps, the the first one is of these companies, which ones are our customers, which ones are are best suited for us to serve? Because we could, I mean, we certainly could make content for the construction industry.

I mean, other than having a hat, I don't really have any experience with the cut with the construction industry. So I don't know that making it. It is actually. Um this is an OSHA-approved um safety hat. Uh it has been displaced, by the way.

OSHA regulations say they must be include full helmets, no longer just hard hats and work sites. Uh, I found that out because it was on sale on Amazon. But we don't have a lot of background in construction compared to other industries. So the the first question is of the say top five or even the top 10, which ones as the CEO would you want? So you know, in terms of the five Ps, that's uh that's a big part is that purpose.

Right. And that's exactly it. So the question is, what is the purpose of us of us doing this exercise at all? And so the purpose of this exercise is for us to reach our different audience segments with valuable information that shows them what's possible so that they then in turn hire us or purchase or subscribe to something from us, take an action that leads to some sort of dollar exchange. Um, you know, being fully transparent, it's all about the money, but it's all about also making sure that we are reaching the right audiences at the right time for what they want.

And so, you know, agencies, uh agency consulting, like it's a big topic. And so, what I would want to do if we were to go that route is I would try to dig into what does that mean? Because there's a lot of information there, like a lot of different directions, whereas you have um other categories like consumer electronics or e-commerce online retail or financial services, banking, insurance that are a little more specific. And so even these ones are still, you know, they're big topics. And so the first thing that we'd want to do is figure out, you know, how much of our current customer base falls into that category.

So we would look in our CRM to say, you know, do we have enough prospects that we could say, hey, we're putting together webinar series based on your industry. Would you like to come and invite them and personally like here and pick them? Uh the other thing we could do is say, well, we don't have a lot of those folks, so let's you know put it out on our newsletter or on LinkedIn and try to capture some of that segment. Exactly. And for folks who are wondering how do you get this information to begin with, here's how we did it.

So we send out a dedicated email, and you'll note, you know, like on the right hand side is what the customer would see in the inbox. And we sent this obviously to our entire mailing list. On the left hand side, you can see the back end code. And the back end code is very simple. It just goes to a single thank you page on our website.

And what we do is we have each response tagged with a an arbitrary what's called a query parameter. It's just as a text string that for each of these responses has you pushes that into our web analytics. So this poll, when someone taps on any of these responses, it then goes sends to our website. That little tracking code goes into Google Analytics. And then what we do is we take go into Google Analytics and we export all the data that has this URL with these tracking codes and just use uh use a little bit of just basic uh basic addition to add up all those responses and tabulate them.

So if you wanted to do this for yourself for your company, your mailing list, this is the format to do it. Super straightforward, it's very easy. It uses Google Analytics. Um so you don't have any additional serving software to set up, nothing to buy. Uh it's very, very helpful.

So we're not gonna, I'm not gonna pull open our CRM on a live stream for a variety of reasons. Right. Yeah, please don't. But that is that is would be indeed the next step. And if we look in our CRM software, what we do see is that agencies and consulting uh does occupy a substantial percentage of uh what our customer base is in terms of the companies we we typically work with.

Right. Um, and so that's sort of what we're trying to understand first, because we there's a difference between who's subscribed to our email newsletter and who's actually a sales qualified lead. Um, you know, so they fall into different categories in our funnel. So we wanna see first like where do we align? Do we have enough uh prospects or sales qualified leads first to even you know create some of these categories?

Exactly. So if we look in this is an old snapshot, but this is an older snapshot from our hub spot instance of uh the companies identified by revenue uh by revenue earned. Uh, this is from 2021. So it's it's I had did not have a chance to run this before today's live stream, but you can definitely see there's a different set of companies in here computer software, uh, marketing, advertising, public relations, communications. That would all fall into the the agencies and consulting, uh hospital, healthcare, telecom.

Uh so there's there is a difference between who we've earned revenue from versus who is on our list today. So that would be part of the intersection with some of the things that you do see. You do see the agencies and consulting. We have earned revenue from companies like that. So that would probably be the a good first place to start.

So given that our initial idea around this was to focus on predictive forecasting, I would say we would look at the agencies and consulting as a broad category and then the different kinds of agencies, agencies and consulting uh who've been customers in the past or prospects. And uh the methodology that we were going to do was to take a look then at SEO software and start to look at you know what keywords, what topics um you know exist for these verticals. Yep. Okay, so if we were to go into so this is uh at the hrefs uh software package um I just typed in AI for and I speci I specified matching terms um let's see let's do phrase match uh to make it a little more specific AI for teachers ai for writing AI for images ai for resume ai for excel ai for logo for business logo design so there is there are a variety of different terms and terminology here let's do um let's take a look here the AI image generator AI for business AI stock trading uh AI for teachers as the parent category AI and graphic design so there's a lot of options here in terms of sort of the general purposes that people are looking for we you might want to to potentially narrow down to to other ways people might type this in. Well so why are we focused on AI for so if we're looking at agencies and consulting we're immediately focused on AI.

Well so what is what is the content we want to create because we want to know what those people are searching but probably searching for. We're gonna try and infer at least a broad set of of terms. So if we want, do we want to do webinar content about AI for agencies? So originally, so our purpose was to do uh predictive fork, yeah, predictive. Okay, um let's see what we get just just in general on those terms weather forecasts, time series forecasting.

People are typing some strange things into Google, demand forecasting, financial forecasting, and so on and so forth. So that's the go ahead. I was gonna say, so I believe the idea, and you know, correct me if I'm wrong, because you know, we are you know, for anyone watching, this is us live workshopping, you know, what our process is for finding these marketing opportunities. The idea was that we would do a predictive forecast on the keyword terms that that vertical wants to rank for, so not AI for or not predictive analytics, but we would do a predictive forecast for all of the you know uh keywords and topics that they would then want to be ranking for. Gotcha.

So for their agency, so like PR agency, for example, correct the amount of search line for that. Okay, let's take a look at that. PR firm. We'll use the the little classics here, yeah. Um, so we've got that's a relatively small volume.

So PR agency, PR firm. Um, let's take those two do a quick export here. Only selected. So this is gonna give us the the overall search volumes. We are then going to need to uh actually extract those terms themselves.

So let's go and put a keywords folder. Let me ages the keywords there. While I'm doing this, you two talk amongst yourselves. It's gonna take so well, I guess. So, John, I mean, you are on the front lines talking with prospects, talking with a lot of audience members.

Are you hearing that there's an appetite for uh predictive forecasting? Everybody wants predictive forecasting, right? Everybody feels that they're gonna get an edge there, but then the problem you find is they usually don't have the data, right? You need to have the data ahead of time, and they don't have the feedback from their customers. I showing off that surveying tool is huge.

Like anytime you can set up something like that where you're continually harvesting info and getting insight from your customers without having to talk to them one at a time, um is just a huge competitive advantage and where you want to go. So um, and yeah, you know, right now in the business cycle, predictive is low on the list for people. It's a nice to have, you know, for most companies, they know that it's uh gamble, you know, they're gonna build it and run it and they're gonna hope that they get some actionable info that delivers a return, but there's no guarantee on that. You know, you have to really be kind of on the cutting edge and not afraid to take a little bit of risk. But the other thing is the risk is there and it's huge.

And so many companies just do this um as stream of consciousness, right? Like the CEO or some other rando employees on the team come up with some ideas and they go to market with that. And we all know that the odds for failure are huge in this. So to be able to actually have some data, and you know, if you can double or triple your chance of success by acting on real data, it can make a huge difference on your ability to survive through these down cycles or to become you know hugely profitable. Because the big thing is if you can use this to find a niche that everybody else is not in, you know, it's just like all the other things we see with SEO, where everybody's just kind of doing the same five things, and it's the companies that come up with something new and out of the box that they have these huge returns until the rest of the world catches up to it.

So yeah. Okay, so what we're what we're gonna do, uh let's do this uh as promised with generative AI. So I'm gonna go log into chat GPT here, and we're gonna give chat GPT so uh a forecast. So in this case, I'm gonna take, and this is uh I'll show you how to do this. If you go into Google Trends, um, this is there's a more efficient way to do this for a lot of keywords, but since we're just doing a uh sort of a toy version, type in the trend that you want.

In this case, we looked from our keyword tool, we know PR agency is kind of the term. We're gonna look at five years worth of data, right? So there's what this search term looks like over the last five years. One of the cautions with Google Trends data is it is always relative, uh, which means that you're never getting actual keyword volumes, you're just getting the the inferred uh relative terms, and then you have to decide what market you're what geography you're in. You might be worldwide.

Uh, I'm gonna use the United States here because that's easier. Um let's take this term and say, okay, today we're going to do some predictive forecasting, specifically time series forecasting. Uh, let's do this. Let's take out the that data file. Let's say, what do you know about best practices for time series forecasting?

What we're doing here is we're gonna we're priming the model. Essentially, we are asking it to build us a really big prompt in some ways. Because one of the things that's true about transformers, which is what chat GPT and its brethren are, is that when they predict things, they take into account what they've what they has already happened in the conversation, whether you did it as the user or whether the machine did it. We talked about this on our recent webinar, uh the which uh you can go over. Um I think it's in the in the in the old newsletter this week if uh if you wanted to catch the replay of the webinar.

Because I'm asking it for what it knows about best practices in time series forecasting. I would have to write all this out in a prompt. This would suck, right? Uh instead, I've had it write this out and say, this is great. Now let me give you a file of a time series with a date and a value.

The date is column DS and the value is column Y. We want to forecast ahead using the most statistically valid method, probably something like Arima the next year. Now we can attach that file, and this is right out of Google Trends, and it's gonna do some thinking. It's gonna take a look at the file, analyze it, and then start writing the Python code that's necessary to do the time series forecast. Um this may or may not blow up because chat GPT is had a rough warning.

I was talking to this one, you had it had a rough warning. I don't think I had enough coffee. But this is what you would do if you don't have like because one of the questions that you've always had, Katie, in the past is what does a person do who does not have a data scientist on staff? Right. Now or a developer.

Exactly. Now chat GPT, you've got one on staff. But it's still just giving me code. I still have to do something with the code, right? No, that so this is using the advanced data analysis module.

Oh, okay. It's writing and running. Yeah. So it's gonna write the code and run it. It says this suggests the time series is stationary.

We do not need to difference it before fitting the AREMA model. That's good, good. Would the average person have known to do a stationarity test on this data? Probably not. No.

But because you just did this, if you just loaded this in as is and said forecast this, it wouldn't have done it either. The difference is when we primed it with the with its best practices, now it took that into account, all those best practices, it took into account as it starts writing its code. So it it by having that initial write your own prompt uh question, it's gonna generate a much more valid result. All right, so is it thinking okay? It's still thinking.

So in terms of us finding marketing opportunities, you know, the the purpose of today's episode was for us to take a look at the data that we've collected and figure out what we can do with it. How can we turn it into something valuable? And maybe predictive forecasting isn't going to be the thing. You know, so we could do our own, you know, internal, you know, SEO research, keyword research, forecasting, and then just start creating content. But what we wanted, our purpose was something a little bit more interactive than static content that we write and then put out on our blog or on social.

Um we wanted to really engage people. So I think that's part of our purpose statement is we want to create content that is engaging. Um, and for us, that felt like we could do a webinar to introduce, you know, our predictive forecasting services, our subscription. Um, for those who are watching, you're hearing it here. We actually did roll out our predictive forecasting subscription.

Um, the day before everything shut down for COVID. So timing-wise, it was poor, but we did not know, ironically, uh, we could not predict uh that that was gonna happen. We just didn't know, nobody knew. Um, that was what they would call a black swan event. But now, four years later, uh, we want to re roll it out.

And so the idea was that we would start with a specific industry, a vertical, um, whether it's agency owners uh or other consultants, and walk them through here's the power of using a predictive forecast. You know, here's what you get, here's how you use it, here's the results. And now you can subscribe to your very own forecast that's updated once a quarter. If you're interested in learning more about that um please feel free to contact us trustinsights.ai slash contact um you know so that's something that we'll be rolling out soon what we'll be doing is everything that Chris is doing now is what we would be doing behind the scenes but we felt like it was a good idea just to show what's in the box what it is that we're actually doing and what's in the box from what chat GP just chat GPT just did was it didn't do a great job. And it didn't do a great job because the initial data itself isn't robust enough there's there's very little variance in this data um to the point where I yeah it's not great.

It also crashed uh as I was doing that and this is the cautionary tale about using generative AI this specific task believe it or not is a poor task for generative AI. The reason it is is that language models are good at language language models are not good at math. This is a math task this is straight up statistics and there are limitations even with the best tools like this that no mat no amount of prompting is going to fix because they just don't have us those capacities the way that if I was starting from scratch today I would do something similar to this but I would have it actually be writing the Python code because that's the most reliable way to get working uh working data out of these tools. Uh that you doesn't require you to to basically hit regenerate over and over again in the hopes that that chat GPT gets it correct this time around. And this is true for chat GPT, Claude, Gemini.

There is no one tool on the market that does this amazingly well. Um they all do code generation reasonably well, but data analysis is not their thing. So the next step from for you know if this was your company and you were looking for, okay, well, when is PR agency as as a term going to trend? Uh it's it trends typically, at least based on this data. Um believe it or not, in the next four weeks, um, that is when it's uh you see the highest um the highest spike in this this very dense data set.

And if that was the case, then you as a marketer would be like, okay, well, we should get some kind of campaigns in the air sooner rather than later, because this is this is going to happen very, very quickly and we need to be uh uh in the market. If we're not, then the results we get from the rest of the year won't be as robust. Well, and one of the the one of the ways that you can use generative AI is if you have that forecast, you can use generative AI to help come up with ideas for what those uh more robust topics would be, what those campaigns would be. And that's where the ideal customer profile fits in. Um so a couple of weeks ago, and you can watch uh the episode uh on our YouTube channel, TrustInsights.ai slash YouTube, we did a live stream about putting together your ideal customer profile using your data and generative AI.

And so once you have your forecast, you can say, okay, great. These are the keywords that I want to be ranking for. Now, what the heck do I do with this thing? So you have your customer profile, and you can start to use generative AI to say, hey, generative AI, this is my customer profile. What do they care about?

What do they want to know? How do they want to be reached? What kinds of campaigns should I be creating around this set of keywords for this profile? And that's where that's sort of the advanced version of using these predictive forecasts. So Chris, are we able to do like a quick demo of what that could look like?

Yeah. So let's do let's feed in our ideal customer profile. We're gonna say today we're going to design a digital marketing campaign. What channel do you want to do it on? Um let's do LinkedIn.

We're LinkedIn. That's a good choice. We're linked in to appeal to our ideal customer profile. Let me give you this ICP to begin with. So you understand our company and what our ideal customer wants.

And we'll add in our separators, put in that entire gigantic huge uh profile, and let's see what uh what first it will summarize. While it's doing that, I'm gonna say next. Well, let's generate five campaign ideas for our LinkedIn account in bullet point format that would appeal to our ICP. So campaign goals, awareness, lead generation, engagement, pillars, content targeting, content strategy, thought leadership, and so on so forth. Here's your link overall LinkedIn strategy, key messaging.

Again, this is all uh from the ICP. So let's see what it has to say. I should probably clarify. Wallets, so it's gonna come up with a list of ideas. I can guarantee the ideas are going to be boring.

The digital transformation readiness assessment, solve a common pain point series, mini case study showcase. Did you know industry insights and the future of whatever webinar? Those are the first five ideas. They're not bad. I mean, yes, fairly generic, but if you asked me for campaigns, I would not have come up with those necessarily.

I would have said, okay, well, let's do I mean maybe a series of some kind, but I do like a little bit of the specificity here. So I would say they're not bad, they're good starting points, and I think that that's sort of the caveat is they're starting points. They're right, they're sort of the kernels of ideas. So what I'm gonna do is I'm gonna tell it, I'm gonna feed it back to itself, literally saying this is a list of bad ideas. Do you think they're terrible, John?

The pain one is solid, and then you know, the future forward one is always works. Um, but yeah, yeah, I mean, it's it's on the generic side. This will be funny to see. Show us your ugly. Uh bad data practices.

Show off your your bad day, the myth busting debate. I like I tend to like myth busting. Five myths holding back your healthcare transformation, the real world transformation journey, competitive audit raffle, and a linkedin group takeover. So that's different. That's that is a bit more specific and a little bit less a little bit less generic by giving it a negative prompt.

I kind of like the show as your ugly because I feel like people would at least stop to figure out what does that mean. Like, but again, this is all because we did the work to put together our ideal customer profile, which we're not going to show all of that in this live stream, but we really tried to dig deep into the pain points. And so ideally, you have that customer profile with those pain points, goals, motivators, firmographics that are going to help prime this model to say this is what this person cares about, help us now match this to our forecast. Yep. So one of the things that we forecast for ourselves that people love to talk about is digital transformation.

It's yes, everyone loves that buzzword. Um let's ask this. Let's take a moment to reflect on the way our ICP might perceive content about digital transformation. From your knowledge of our ICP and the conventional ways digital transformation is positioned in the media. How might our ICP think about the topic?

So now we're gonna narrow down on one particular avenue, but again, using that ICP and what we've already documented for their pain points and things. This is what they would likely think about the topic. Positives, opportunity growth, problem solving, future proof and efficiency and innovation, potential negative perceptions, buzzword fatigue. That's a winner, disruptive and costly, uncertain ROI, lack of clarity, positioning to counter those negatives, focus on specific needs, data-driven results, phased approaches, focus on people, acknowledging the human side of transformation. Focus on problem solving, a human-centric approach, and industry specific insights.

So that would be if if you knew from our predictive forecast that digital transformation was going to spike in, say June of this year, and we want a campaign for LinkedIn. This would be sort of how the ICP sees that. Right. And so then, you know, we could say, yes, digital transformation is a buzzword, but you know what's not is the five Ps. The five Ps encompass everything that digital transformation gets wrong.

And so we could do a whole series about that, you know, going through each of the P's or, you know, to the other campaign of show us you're ugly. Um, which now I'm kind of like, I like that idea. Um, where digital transformation went wrong, and had you used the five Ps, or this is how you could use the five Ps to fix what went sideways. I think I have to start campaigning for digital upgrade because there's really no transformations left. It's not like there's these companies still working on paper and fax over in the corner somewhere.

Like it's already digital now. You just need it to be more digital. Well, ROI Garage. Huh. ROI Garage.

Mirth versus reality, the Data Whisperer series, and the transformation toolkit. These are still too buzzword laden and sound like a management consultant who had too much coffee, wrote them. Come up with ideas that will resonate with our ICP, but are practical, useful, and down to earth for our LinkedIn campaign. I want to emphasize one of the things that that generative AI is really good at is having conversations, right? We're not in search of the one magic prompt here to do it all.

I know you know today people have been posting, oh, Anthropic released their their 70 use case cheat sheet for prompts. They're all okay, but they're all starting points. They're all starting points. They're like they're like cocktail pickup lines, right? Or icebreaker lines, and not things you would have a genuine deep conversation with a person about.

If you think of generative AI, and we say this, we said this uh in our in our webinar. If you think of generative AI as the world's smartest, most forgetful intern, starting with a canned piece of text instead of actually sitting down with that person and talking to them, you're gonna get much better results by having a a real conversation. And you can see just from the the prompting we've done here for this LinkedIn campaign, we're getting better stuff when we are when we have that that interactive back and forth conversation. So uh let's do a toned-down approach. Work workflow confessions.

That's kind of cool about the most frustrating, inefficient, or ridiculous workflows anonymously that people have. Like, oh yeah, I mean, we used to see this at the old PR firm. There was one person on staff whose job it was to copy and paste results from Google to a spreadsheet. Like that is a ridiculous workflow. That that's not even AI, like that.

You should have automated that years ago. The metrics that matter challenge, tech stack audit snapshots, spotlight on small wins, and office hours with an expert. And that's something that we actually do for our clients. We actually have office hours uh with them, so they can just bring anything, uh anything and everything. So these are campaigns for LinkedIn that I think you know, if you did a LinkedIn live that was office hours with an expert, that would actually be pretty cool.

Yeah. That's and that is a really light lift because really it's just a matter of scheduling the event and letting people know that it's gonna happen. The prep itself is minimal because you're saying these are the things I'm an expert on. So ask it's a it's an AMA session. So you can say, you know, so I mean, that is a really smart idea because it's engaging, it's not people being talked at.

They can bring their questions, their questions about digital transformation, their questions about how to use generative AI with all of these things, their questions about process. And it's an opportunity to showcase the things that we are the most knowledgeable in, and that we have solutions that we can answer questions. So that when someone goes, huh? I wonder who knows the thing. Where can I turn to to actually hire someone to do the thing?

We've shown that we can do the thing. Exactly. So let's quickly recap. Um we started with survey data. We said, hey, here's who our audience is.

We then went to trend data to say, okay, well, we know who our target is. What are the kinds of things that they would be searching for? We went over to our SEO tool and just took a look there. From there, we we've got past data, five years worth of back data that we were then able to have generative AI forecast forward, finding, you know, in the next four weeks the that term is going to trend. And then we spot we we took that information and we fed it to generative AI with our ideal customer profile to turn it into actionable stuff.

And this is the part that I think everyone has missed with generative AI and with analytics. Analytics tells you what happened. Surveying tells you why. Predictive analytics tells you what's likely to happen. Generative AI tells you what to do about it.

Generative AI will help you close that gap because for uh trusted insights has been in existence for six years now. And we have seen more analyses just go on the shelf and stay there than ever being used because people were never sure. How do I take this analysis and bring it to life? How do I take this analysis, do something with my data? Generative AI is that next step.

That is that we used to have this whole hierarchy of analytics, and you know, there's prescriptive analytics. Well, that's what generative AI is. Generative AI is prescriptive analytics. And so we want to beat to death the fact that if you've been doing all this stuff so far, right? You've done your diagnostic descriptive analytics, you've done your diagnostic analytics, maybe you have predictive analytics.

Now it is time to bring it to life with generative AI. And that's what this can do for you. That's what we just did today. And you know, we sort of went through in the scenario that we have the ideal customer profile using that information. If you don't have that yet, but you do have a predictive forecast, you could even use generative AI in a very uh simplistic way to say, I have these keywords.

What are some suggested topics that I could write out? Some suggested campaigns. I know at a high level, my audience are, you know, marketing managers or marketing directors in, you know, the mid-range size companies. You could just at a very high level list out that information without going through a larger um ICP uh exercise and say, you know, I want to target people who are, you know, in these size companies in these roles in this geographic location with these titles. So you could have those four pieces of information.

Give that to generative AI and say, and these are the keywords I want to rank for. Help me put together a content plan. Yep. The other place to look for that data, your inbox, right? You have a customer service inbox, right?

You have a call center, you have something where customers have talked. I hope customers have talked to you. Copy and paste all that information into a secure system, please. Um, you know, use make sure you're using systems, you know, you will see at the very bottom of the screen here. It says your trust inside we're using Google Workspace.

So your trust insights chats are not used to train our models. That's that's what you want to see. Um, because you you will be using using customer data. But copy and paste a few pages of that data in and say infer a customer profile from this information, right? If you go into your customer service inbox, you will see things.

People have their name, their title, and position and the organization in their email signatures, right? John Wall, business, you know, business partner at Trust Insights, Katie Robert, CEO at Trust Insights. You don't have to go through a elaborate exercise. Just gather up 15, 20 emails from your inbox and say, okay, this is the collection of people and let it build a profile for you. Um The other thing I would say is if you have someone like a John Wall on your team, just start a recording and ask John Wall a bunch of questions about what people are saying, get that transcribed into something like otter, and then give it to Generative AI to say, help me make sense of you know all of this information, like start to categorize it.

Catarag is categoric is oh my goodness. I can never say categorization. Thank you, is something that generative AI is really good at. Yep. Um there's a very good chance if your organization is mid sized to enterprise that you have laying around recordings from a focus group or a customer advisory board or one on one-on-one interviews or surveys or polls or quizzes or any of those things, somewhere in your organization, somewhere in the the bowels of your service, uh, your server room, uh, is that data.

Go get it, right? Remember that the power of AI is is twofold. Like your the differentiating factors for any organization to win are twofold. One, the quality and quantity of your data. You have this information somewhere in your company, and the quality and quantity of your ideas, right?

So hopefully we've given you some ideas today on this live stream about how to tie in the four layers of analytics. We started with with diag descriptive, like here's the survey data. We went to diagnostic, right? We uh we started pulling together things like search data. We went into predictive to forecast what's going to happen, and then we went to prescriptive, which is the use of generative AI.

You have for sure descriptive analytics data all over your organization. You may have diagnostic. You if you you might have uh predictive and you have the tools for generative. So if you if you are not sure how to do this, let us know. This is something we just did it for ourselves.

Um, but we can help you do this. But if you've got the stuff and you just don't know how to put it all together. John Wall, hot takes, thoughts. We are the worst case scenario for this, really, right? Because we're under 20 million dollars, and we don't, you know, all of our products are bespoked in custom consulting.

So yeah, to really hit the point home of for any business over 20 million dollars, you already have more than enough data lying around, you know, just point us in the right direction because you've got you know, support logs, or you've got customer service contact info, you've got product feedback, all that stuff that gets ignored in lot large organizations. That is all data that can be taken advantage of and leveraged to actually make positive change. Yeah. And it's B2B and B2C. I'll you know, we have one B2C customer.

We look at Google reviews, right? And Yelp reviews and stuff like that. And we gather hundreds of these things every month, and we can infer customer profiles from that. Like, hey, you know, Sally Salad Lover loves, you know, really disliked the anchovies that were on the salad bar last month. Like, and and you know, and Sally's Scott, come on, Sally.

Um, come on, Sally. But that information is stuff that can go into customer profiles and then use generative AI to do things like if you had a restaurant, okay. Here's the here's we've noticed the Sally Salad Lover profile. Yeah, seems to be frequenting the audio the restaurant more. Here's our current menu.

We've got we know what we've got to have a new menu for April. Take that pro ICP, feed it to generative AI and say, here's some ideas, or here's some things we know we can make. What other ideas could we put on our menu for next month to appeal to Sally Salad Lover? Sally salad lover, Sally Salad Lover, Sally Salad Lover, Sally Salad Lover. That's all I can think is that it's a tongue twister.

But no, I mean you're absolutely right. Not to take away from you know the importance of what you're saying is that there's a lot you have a lot of data that you can use to find those marketing opportunities, and you can use generative AI, even if it's hey, I want to find some marketing opportunities within my own data set. Where should I start? You can ask that question to generative AI and say, help me figure out how to even approach answering this question. And it can say, Well, do you have this?

Do you have this? And you can say, I have three of those eight things. Or you can say, I have CRM data, I have marketing automation data. How should I be looking at that data to find marketing opportunities? So, you know, have that conversation with generative AI rather than taking it upon yourself to say, Oh, I don't know how to do this analysis.

I don't even know where to start. Let the machine figure out the hard stuff. You take all the credit. Machine's not going to be mad. At least not today.

Katie, I mean, you volunteer at Bay Path Humane Society. You volunteer at a local animal shelter. Uh, one of the things you could you could the shelter could do is just do a quick demographic profile, even from the Google Analytics data. Here's who visits our website. Boom.

Feed that into a generative AI. Build us an ICP from that. Um compare it with the actual adopters of dogs and cats. Say, does our website demographic match our adopter demographic? And if it does, great.

Now, here's my demographic. Help me write some fun descriptions for the animals to help them be more appealing on the website so that the probability of someone booking an appointment to come in is greater. Mm-hmm. Absolutely. There's there's no at this time, there's really no limit to the types of ways that you can use generative AI to just figure out how to solve help solve a problem.

Right. You know, if you've done an analysis, give it the analysis and say, what else should I do? What am I not looking at? What am I not asking? To find those marketing opportunities.

Like, so here are the five campaigns I've done. Here's how they went. What else could I do? Find those marketing opportunities. Exactly.

Um so that's gonna do it for this week's show. Uh, thank you for tuning in, and we will see 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.