So What? How to Extract Insights from Qualitative Data With AI

Well, hey everyone, happy Thursday. Welcome to So What, the Marketing Analytics and Insights Live Show. I am Katie holding up Chris and John. There you go. Yeah, and down.

Exactly. And down. She's back after the kayaking over Niagara Falls. I'm back. You know, the biggest thing that we both got from vacation was cold.

So I am still trying to eradicate that. Um today we're talking about how to extract insights from qualitative data with AI. Now you might immediately think, oh, so we're talking about market research data. That's one of many kinds of qualitative data. Analyzing qualitative data is a specialty up until now.

And I say it's a specialty because it's not as easy as putting everything into a spreadsheet and writing a formula and making a chart out of it. That's what you do with quantitative data, your numeric data. Your qualitative data is your data that is more free form. It's sentences, it's feedback, it's opinions, it's survey responses. And that tends to be very challenging if you know it's customer service feedback and the responses are not normalized in a way that everybody is responding the exact same way, so that you can have one-to-one-to-one responses and then just sort of cluster them.

And so there's a lot of different things that you can do with qualitative data. So, what we want to do today is show you some examples of how to extract insights from qualitative data using generative AI. So, Chris, where should we start? Well, I suppose we should start with uh huge surprise, the five Ps, right? Which you can find out more about in our new AI strategy course.

Go to TrustInsites.ai/slash AI strategy course to have here, Katie talk about that for about two hours. Um why would we want to do this? What's our purpose, Katie? So, our purpose, well, it can go a couple of different directions, but for the sake of the show, let's say the purpose is we want to understand what our customers or our audience is saying about us on a social platform, or we want to understand the pain points of our customers. What are they reporting in our customer service portal so that we can fix them?

Those kinds of things. So essentially, we want to understand what people are saying so that we can resolve it, so that we can take some action with it. Got it. Okay. Let's do this.

Let's take a very concrete problem. Hey, we want to sell more of our AI strategy course. I mean, we want to sell more of this. And so, how would we go about finding information about this? So, we know what the course is about.

We know it is meant for leaders or people who want to be leaders in their companies uh around AI and setting strategy. So logically, we should probably understand who the who that might be and where they might be talking about this. Now, the good news is we've done a lot of this work already. So the second P people has largely been solved. We have solved that with our ICPs that we talked about in a past episode of the live stream when we built a sales playbook that contains our different ICPs.

So our first step in this process would be to say where would our ICP be asking about this? So let's go over into Google's Gemini. And I'm going to attach our sales playbook, which is this. We want just this one here. And we're going to say something pretty straightforward.

We're going to say, based on our sales playbook and the ideal customer profiles in it, we want to sell more of our AI strategy course, which is called the AI Ready Strategist. It's targeted towards leaders and people who want to be leaders in generative AI at their companies. To do this, we need to understand where these people might be talking about their AI challenges. What forums on Reddit, for example, would be places where our ICP might be having those conversations. Make a list.

Now, from this, we're going to start with our ICP. We're going to we're going to say, let's just see if we can, if there even is any credible place, you know, on Reddit where this might be occurring. And so, John, as our chief statistician and head of business development, aka, you're the sales guy. Um, if I said to you, John, we need to sell more of the AI strategy course, where do we start? So, AI aside, like where would you as the human start to look?

That's a huge challenge. You know, it's the kind of unconscious and competent thing of like, you know, the people that know it don't know that we're out there. So there's a huge awareness gap. I mean, it's easy. Normally the way we would go with that is just let's just start with the internal list.

You know, we would go with the people that we already have had some kind of contact and already know who we are. That's kind of the easy hunting. So, yeah, if there's any way to do some kind of mining of you know what's out there on Reddit or LinkedIn or whatever to find some new possible leads, that's you know, a great way to do some prospecting. And yeah, anything we could get some leverage on, you know, via automation would be fantastic because yeah, it's uh I don't know. We have found no easy answer to that problem, aside from let's continue to do content like this and speaking and stay out in front of everyone so we our reputation does the work.

But yeah, we've never found a way to kind of go mining for who those people are that are trying to get to this stuff. Yep. So we have a list of like nine different subreddits. Uh and for those who are not familiar with Reddit, Reddit has individual channels inside of it. So if you're familiar with Slack and you see channels in Slack, Reddit has channels, they're called subreddits.

And these are each self-contained communities that are moderated by different people, and it's all people based. And some subreddits are great, some subreddits are garbage fires, you know, dumpsters on fire. It all depends on the people who are moderating each one. But the big ones tend to be moderated pretty well. That's why they're big because they're they are able to sustain uh their audience as well.

And if you are a registered developer with Reddit, which you can do for free, you can get an API key that will allow you to download data from Reddit for free within reasonable limits. Like you can't just download all of Reddit and your computer would explode if you tried. But you can say, give me the last 90 days of posts and uh comments from the artificial uh subreddit or the marketing subreddit. And so our next step would be let's go and grab that data. Now, this is the hard part because you have to be good enough at generative AI to have Gemini or ChatGPT or Claude or whoever you use.

Write you the code to do this. You don't have to code it yourself, but you have to be able to articulate. I want you to build me an app in Python that will use my Reddit API key to download the posts and comments from the subreddit and put it in a form I can use. Is this where you would use a tool like Google Colab to write the code? You could um you can do it straight up in regular uh Gemini or Chat GPT as well.

Um or you could do it in a coding environment. So all the major services have dedicated coding tools. Gemini's is called code Assist, opening AIs is called codex, claud's is called Claude Code, not particularly imaginative, but uh there you have it. Um but all of them could you could do that. And even in regular Gemini, if I was to start a new chat here.

So it doesn't matter where you're doing it as long as you know what instructions to give. Exactly. So let's do a very quick example. Today we're going to write a Python script in Python 3.11 that's going to be a command line application. It's going to address the Reddit API.

So we're going to need a way to store our Reddit credentials and our API key to address the Reddit API. At the top of the script, we're going to have configuration variables for things like which subreddit Reddit we want to analyze and how many days of posts and comments we want to extract. We also want to store this information in a couple different formats. We want to store it in a SQLite database so that we don't have to keep re-grabbing the same information over again and over again. We also want to store it in JSON files to make easy for generative AI to read.

In the Canvas, first develop a product requirements document for how we might build this app. So based on that very large foaming at the mouth, we want to build a PRD, a product requirements document because you generally shouldn't start coding without it. Um, I just want to point out for those who aren't, but as we know, people do. So a lot of these uh generative AI tools now have the ability for speech to text, and that's what Chris is doing. So, Chris, rather than having us watch him type and backspace and fix spelling errors, which is what you would actually be watching me do.

Um, if you can see in the what do you want to build that chat dialog, there's now a microphone, and you can click on that and do what Chris is doing, which is Chris's favorite thing, and just yell into the void. Exactly. And so now we've done this requirements document, so we can now say, in a new canvas, build the Python script. So assuming the requirements document was good, and we skipped over a whole bunch on purpose, like going back and forth, gathering requirements properly and stuff, uh, which we've done on previous episodes. Yeah, human intervention, making sure that things are correct.

What did I miss? What about testing? You know, those kinds of things should all be in there. Exactly. So now it's going to try and build the actual Python code for us to do this task.

Now at this point, we're gonna we're gonna leave this behind because it's not a great use of time to just watch it code, but this is where you would start and you would go back and forth with the AI making corrections and things. And ultimately, what you should end up with at the end of the process is something that looks like a big folder of stuff. And each of these files that you see on screen here is a database of Reddit posts. And let's let's open up one of these here. Uh let's see, where are we?

Let's open up online learning. Actually, online courses. And so this is a database, and then here are the comments from different redditors. Here are the posts from different redditors, and now we have qualitative data. We have a lot of qualitative data in here.

But what we'd want to do next is figure out well, what does it say? A great question. Well, I think, no, but uh jokes aside, I feel like that's where a lot of people get stopped because it is it can be so much information. And trying to read it's one thing to read through it, it's another thing to read through it and also identify patterns and categories. And that's where generative AI is going to be really helpful.

Because you can read through a big body of text, which is essentially what you have, but then to make sense of it and to do something with it is the difficult part. Exactly. So in my folder, because remember, I said I want JSON exports of this data, which means it's something that a machine can read. If we look, if I just pop open one of these, you can see there's the Reddit comments, IDs, titles, authors, and it's all very nicely structured so that an AI, a large language model can read the language and see all the metadata in here as well, but not get confused about is this the person's name? Is it a comment they made?

What is it? By having in this very structured data format, generative AI is like great, I know what to do with this. I know how to read this. And I feel like that's a pro tip that you've given before is how to structure the data correctly. I believe uh, if I'm not mistaken, Chris, you gave that in the Trust Insights newsletter in one of the data diaries, uh, which you can subscribe at trustinsights.ai slash newsletter.

Um, because structuring the data correctly is more than half the battle. Exactly. And so the best all these files you'll note these files are like you know a megabyte each um there's these are fairly large files if i were to zip into one it's about 150 000 words each file right that's a lot that is gonna be more than any one general chat window is going to be able to have however the place that can handle this is notebook lm notebook lm is ideally suited for having uh being able to process these large plain text files because that's what a JSON file is it's just a plain text file it's nothing fancy so if we were to go in here and based on we remember what um Gemini said about the the forums and things that uh our target market might be using they might be for example interested in the in the marketing subreddit right because that's that's a lot of our audience is marketers um the for AI stuff specifically they might be very interested in the artificial intelligence forums so I'm gonna add the artificial intelligence forums in these just more whoops uh I missed them there we go um let's add that try that again and remind me remind us there are there's a limit to the number of files that you can or sources rather uh because it could be any kind of file sources that you can include in a notebook LM notebook. That's correct. For the free version, it is 50 sources at a time.

For the paid version, including the Google Workspace version is 300. Oh. For some reason, I thought it was like 20. A long time ago it was 20 went back in the in the bad old days when it was like, oh, you know, we're we're just trying to figure out this thing even works. Gotcha.

Now that it's a an actual production product, um, it is considerably more generous. And the other thing we'd probably want to have in here as a source, um, just for reference, would be our sales playbook, right? Because that would make logical sense that if we want to be able to have a conversation about what our ICP, who RSP is and what they care about, uh, we'd want to have that background data in here. So we can now go ahead. I have a question though.

If this is for selling the course, wouldn't you have background information about the course in here as well? Or no? Like, I guess I'm maybe I'm jumping ahead. Let me know that type. That no, that's per I that's perfectly fine.

If you wanted to have the course information in here as well, that would be good. I would say maybe we should add that a little bit later on because we want to try and get as much of the original words of the people as we can first before I gotcha. But we're gonna condition, we'll we'll definitely do some prompt conditioning along those lines. So our next step would be to say um we want to understand from the Reddit form feedback that we've loaded in here what people are talking about in terms of their frustrations about AI strategy, particularly AI and marketing, but AI and business in general, based on the conversations that I've provided and the ideal customer profiles in the sales playbook that I've provided. Explain what the frustrations of leaders in organizations are about AI specifically, about AI strategy especially, and what they're doing to address those frustrations, and we'll give you a format that looks like this.

And so I've got this first part of the prompt here, and then we're going to give it a very simple outline. And the outline is basically just make it look like this. So, what is the frustration? Why is this a frustration? How is how is somebody working around it?

And this is now going to go into all the sources we've uploaded and try to pull out the qualitative insights from what real people are saying. Now, one of the cautions we have to have here, the people who are on Reddit may not be our ICP. They may not be our customers. So we have to take that with you know their feedback with a grain of salt. Right.

Well, because we don't actually know who these people are. A lot of people are using handles. Um, a lot of people just like to, you know, incite conversations that are derisive or you know, they like to cause problems. So I would say I think it's a good starting point. I don't think it's something that we would use as the end all be all.

I wouldn't say to John, okay, I have a list of you know, pain points that we need to address with our services, go out and start selling these exact things. But I think it's good directionally. And that is exactly where we're going to go because you've identified um a pretty clear, you know, direction that you want to head. Like we want to know if it's our ICP. So here comes the list of frustrations.

And let's just take a look at these really quickly and lay hide our side panels so that we have a bit more viewing room here. Uh lack of clear AI strategy and overwhelm is leaders often overwhelmed by the rapid pace of AI, frequency high, frustration, difficulty demonstrating AI ROI and measuring impact, AI reliability, accuracy, context, and ethical concerns, skill gaps and talent upskilling in AI data, internal processing efficiencies, and conflicting stakeholder feedback. So those are the things that it identified from the conversations is like, yeah, this is what people are pissed at about their AI strategy. Now, what we would do, go let's go back to our regular Gemini, attach our um sales playbook, and say from the following qualitative information that we received from Reddit forums, analyze it from the perspective of our ideal customer profile interested in buying our AI strategy course called the AI Ready Strategist. Re-rank all of the complaints in the most likely probability that they would be relevant to our ICP because what people say on Reddit may not reflect what our ICP actually cares about.

Present your results in the exact same format as the forum results, but re-scored and re ranked by our ICP's relevance. So we're gonna put that in, and then we just copy and paste from Notebook LM. And you might say, well, why can't you do this in Notebook LM? At this point, we don't want all those con other conversations in there. We don't want the context room to being polluted by all this conversation.

So we're now just taking that distillate, that extract, plus the original sales playbook, and have it play that back and thinking about now with the data being much more heavily weighted our sales playbook. This is now the time for barbecue recipes. Yeah. I was well, and you know, John, I always, and we're when we're talking about like the sales playbook and sales in general. You know, my first question is always like, does this jive with the process that you use or that you were taught, you know, how jarring is it to be using generative AI for your prospecting when, you know, for the past however many years that you've been doing it, it just hasn't been an option.

Yeah, well, and that's the thing. Like, this is just completely revolutionary. It's just a matter of time before people figure out how to really take full advantage of it. But this is the kind of thing that, you know, a a C level person would go and just read 10 articles and then make up their whole opinion on how it's all gonna go. So the fact that you're digesting hundreds of thousands of posts and actually getting, you know, an accurate picture of what the world is like, that's it's just a whole different world, you know.

Um, because yeah, so many sales decisions in the past as far as prospecting, you know, people won't admit it, but it just is going solely on gut. You know, they're they're just like taking a random sample of five and and going from there. And so that's why there's so many crashes and burns, you know, in that process. And so this can yeah, can change everything. So we have our results now.

We have from our ICP strategic Sarah, which is one of the ICPs in our our R of sales playbook, but the re ranked frequency by the pain points that our ICP would have lack of clear air strategy and overwhelm. Difficulty demonstrating ROI, reliability, accuracy and context and ethical concerns, skill gaps in upselling, upskilling, and internal process inefficiencies. So it's interesting that those are in a slightly different order, but it makes sense because this is our ICP that's talking to us now. So now we can go to the actual course page, right? If we go to our AI Ready Strategist page.

How well does this landing page address the frustrations of Strategic Sarah as outlined? If Strategic Sarah read this landing page, rate it on a scale of zero to ten about how relevant it would be to her frustrations and how compelling it would be for her to take this course. And we're gonna see what strategic Sarah. Wow, I didn't know I was getting graded today. Man.

But no, so here's the thing, and this is what Generative AI doesn't pick up on, is that I would actually argue. Oh, I got a 9 of 10. You know what? Forget it. None of my arguments are valid.

I was getting so ready to like go on the defensive and defend my choices, but it's a nine out of ten. We're good. No, what I was gonna say, in all honesty, is what generative AI or what you couldn't pick up on and put in, but we could get was the transcript from the overview video that's on the landing page that explains a lot of these pieces. That's what's not in here, but we could add that in. Exactly.

So it says overall relevance, nine out of ten, compelling it's nine out of ten. How the landing page addresses it, lack of clear AI strategy, the TRIPS framework, difficulty emersting ROI, the ROI calculator, AI reliability, the 6E framework, right? Uh skill gaps, um, driving adoption, uh, internal process efficiency, the 5P framework and stuff. So what this tells us is from qualitative conversations that we picked up from Reddit, no influence of our own, through a notebook LM distilled through our ICP to our landing page. There's very little we need to do to make changes, which answers a critical question for us as marketers, which is is it just that you know, if you have any product that's not selling, there's a million reasons why it might not be, right?

We've all had those conversations. Isn't the economy? Is it price too high? Is the value not clear? What this tells us, this knocks some stuff off the table.

This says the course is relevant, it's clear to this person who is our ICP that we validated through data in the past. The material in the course is not out of alignment with what they want. And so now we can take that whole lingering doubt like it was this any good? It's off the table. It is good.

It's a very clear fit. Whew. Thank goodness. I really, you know, it I really was like gearing myself up to get all kinds of defensive. Uh so I'm glad that I don't have to do that now.

That takes a lot of energy. It does. Now, here's the question. What if it's price? How would we know?

John, sales guy? You know, the sales guy takes a hard line on that, and it's just like the only way you do that is you know, your wins and losses, you know, with actual people with actual money. Um, you know, you can do studies and figure out uh you know what the odds of it are there or compare it to other things that are out there and sold, but there's kind of no substitute for actually beating on it. So I'm interested if Chris has got a take on this, if he's got an angle that we can run with this. Oh, I'm sure he does.

Um, and of course I do. Of course you do. And okay, so is this where I get defensive? No, this is where we go back to the well, and we set and we go back to notebook LM, right? Let's um let's that is really large on my screen.

Um, let's start a new notebook, and now instead of having the marketing stuff, let's take in stuff from the online courses forums for people who make courses, the e learning forums for the people who are designing courses and things. And we're gonna start with a very simple question. We're gonna say based on the Reddit conversations from these different forums, what are course creators discussing and debating about pricing? How do they set pricing? How do they determine what a fair market value for their course is?

And what strategies do they seem to follow? So let's start with that. And let's just see what the conversations that because this is about 90 days worth of conversations. That's what I have my script set to download because I feel like anything older than that, you and given the economy and the macro perspective, things change really fast. So we want to figure out like is it a is it a case where we're we're out of alignment with pricing with what the rest of the industry is doing?

And if so, can we justify it? So we see impact of price on student commitment and completion rates. A theory that courses priced too cheaply might lead to lower student commitment compared to more expensive degrees. Let's uh make this a little bit bigger here. There we go.

Low payouts from marketplaces like Udemi, where one user report making an average of $4 a sale and questioning the product's worth, price hikes and hidden fees from SaaS platforms, perceived pricing plans, the overall expense of high-tier LMS solutions, how they set pricing, focusing on business outcomes, market research and competitor analysis, direct user engagement, um, cost of platform features, perceived value for content presentation, tiered offers and bundling, leveraging free content and trials, platform choices and hosting models, diverse marketing and sales approaches, enhancing engagement to justify value and stuff. So we have we now have a wide variety of different ways to look at this issue from what core other course creators are saying. So let's go hit copy, go back and hit paste and say, let's say we want to sell more of our course. It is currently priced at a premium value of $14.99. Uh $1,000.

I knew it was going to do that. That's not premium. Based on this, we know that there are certain segments of the audience that will not be able to afford this. So what we want to do is understand strategic Sarah and what she might be willing to pay, and then some of the feedback from other course creators about the challenges they've run into in course pricing and try to reconcile the two perspectives for the purposes of understanding how do we price this course to maximize the overall revenue generated from it. So we put in that prompt and then we add in our uh Reddit conversations and we see what generative AI comes up with at to see if there's a middle ground.

And so I can tell you a little bit of my thinking about why it's priced at $14.99, because you know, I have heard, oh, that's a lot of money, or you know, is it fluffy and am I gonna get something for it? If you are new, if you haven't heard of this course, in the course, uh, there are over 20 downloads, and by that it's frameworks, exercises, checklists, uh templates, plus there's about eight hours of instruction walking through with exercises. And so if when I did basic, like super basic back of the envelope math, and I said, if I take one download, like the ROI calculator, and I sell that for $99, which is a really inexpensive price, or even like uh $59. If I take $59 as a price point or $99 as a price point for each individual download, multiply that by how many downloads, that's way more than the price point of the course. And so, in theory, you're actually getting everything bundled at a discount with instruction versus buying everything individually.

So that was my logic for coming up with the pricing, in addition to uh Chris, the some of the research that you did internally, uh, in terms of course pricing, those kinds of things. But just as some context, it wasn't an arbitrary, I'm just gonna sell it for this much. There was thought that went into why it's priced the way it is. Exactly. So Gemini says, based on the profile of strategic Sarah and the ancestors of the course creators, the current price point is well positioned for the core target audience, right?

It's insufficient on its own to maximize total revenue. It says take a tiered pricing structure, strategic Sarah's perspective, why it works, right? It's priced on based on business outcomes. What course creators can recommend for B2B sales? It's for a corporate budget.

Sub $2,000 course is a standard professional development expense that often doesn't require complex approvals and like a ten thousand dollar consulting engagement, which we also do. Um perception of quality, the price, premium price signals a premium strategic product. Cheap, cheaper course might be perceived as tactical rather than strategic, making it less attractive to her. So the price for strategic SARE is not a barrier. Do not lower the price for this ICP.

Woo, thank goodness. Now, when we look at the uh course creators, tiered offering and bundling, this is the key to unlocking new revenue, different access levels or extension courses. So the AI RAID strategist, this is the flagship product, the strategic AI toolkit, which essentially is taking just pieces of the course and essentially unbundling it into its own thing, or having accelerators and bundles like, hey, get five seats for the price of four, et cetera. So it goes through and says here's what we know the market is working, is working in the market based on Reddit conversations, but reconciled with the fact that this is squarely targeted at our ICP. Which makes sense.

And so if you go to that second um option of unbundling and rebundling things, I that's really interesting. Uh the way I had been looking at it was I would take the individual modules in the chapters and record re-record those as mini courses, mini lessons. I actually, this is much in terms of overhead and resources, this is way easier and something I could probably put together tomorrow. Um, you know, so if we want to sell just the toolkit, we could absolutely do that. So, you know, just for context, for those who aren't aware, this all started with our AI toolkit, which is a white paper download, but it doesn't go into the detail in the templates that this would, that this uh strategic AI toolkit would.

I that's actually really smart. I like that. And stay tuned because that's coming. So I have a question about that because I've been wondering about this. Yeah.

Here is a copy of our lead magnet, the AI ready marketer, which is a precursor to the course that contains a lot of these materials that you targeted for the strategic AI toolkit. Should we stop giving this away as a lead magnet and instead charge a much lower price for it? Because it does contain substantially similar content to the actual course. Let's see what it says. In the free download, you have to do the work.

In the 399, I've already done the work for you. I'm wondering though, and this is just my personal curiosity whether we should or not. It says, No, you should absolutely not stop giving away the AI marketing strategy because a free lead magnet strategic value. Charging for this kit would be like a chart high-end restaurant starting to charge for its menu. I think I love that analogy.

Someone who downloads the 26 page PDF is not a casual browser. They are likely your ICP. It builds brand trust and authority. So the free kit gives them the what the paid toolkit gives them, the how. So reposition the free uh Mac lead magnet, keep it the exactly as it is, call it the DIY toolkit, define the paid product, which is the accelerator pack, and then that leads into the upsell for the full course.

I like it. I could also see if somebody downloads or somebody purchases the accelerator pack. The accelerator pack, and then they're like, okay, I want I still want the course. They could get a bit of a discount on the course because they got the accelerator pack. But I mean, those are all things that we could work out in the background.

But I hadn't, I was thinking along these lines, but this is a different angle. And I really like this because it actually takes a lot of the burden off of me to recreate the wheel. Exactly. So putting the course aside for a minute, what we've done today is identified from our ICP where the qualitative data that we might want lives. Then we've extracted it using code generated by generative AI to build the tooling we need to actually grab the data and pull it in.

We then have generative AI process that data, we put it into a system like Notebook LM, which is like a library, to extract insights from the data because the data is just too large for a regular AI model to handle. We take those insights, we put them back into regular generative AI against our ideal customer profile and say, validate these insights. Are they real or are they fake? Right? You know, or do they not align with what we know to be true about our ideal customer?

It came back and said, Yes, this these insights are relevant, but in a different order, uh, because of the nature of the strategic strategic SARA, as she's called in the in the playbook. And then we say, here's the product we want to optimize based on the insights from conversations. Is this product aligned with the marketplace? And that's where we got the tiered bundling and stuff like that. And then we went back and we validated, you know, more pieces of our marketing strategy against both our ICP and the real-world conversations.

What's useful about this approach is that it is grounded in reality. We are not asking generative AI to make things up. We're saying here's real data from real people. And here is our ICP from real people and real things. And here's our real actual course with all this real information, create strategies, tactics, execution methods, and measurements of success.

And if you want to get the course, go to TrustInsightai. Exactly. So that's one of many ways to extract qualitative data. Now there's other, there's so many other ways. Let's say you're a company that maybe you're a B2C company and you have a restaurant, right?

So an example, let me pull out uh Ostanya Barricada in Kiev, which is a very popular restaurant. Um, it is half restaurant, half museum. You'll notice on here that there's Google reviews, and these are you know uh all the different reviews that have happened on this site. Guess what you can't do from Google Business? Export the review data.

Exactly. It sucks, it's awful. You can't do that. But what you can do is you can turn on your screen capture utility. I'm gonna turn on, I'm using Camtasia snagged.

Uh, I'm just gonna hit screen record, and I'm gonna click, read, click, read, and so on and so forth. And we'll just do a page or two of this. Uh, not a ton, but we can look here and read this. Most relevant reviews. Did I miss any there?

No, no, no. Okay. Let's hit stop. Now I've got qualitative data. So I'm gonna take this.

Let's take this to. I'm gonna use Google's AI Studio just because I know it's very reliable about this sort of thing. And let's take this video file. And let's drag and drop it straight into Gemini. I need to drag it to my desktop first.

There we go. And desktop. There's my file. I'm gonna say, from this video, create a transcript of the reviews. The transcript must contain the reviewer's name, the reviewer's star rating, the estimated date of the review based on today's date.

Today's date is September 11th, 2025. The review text and the review sentiment. Produce your results in JSON format with each of these fields as a key and the appropriate corresponding value. Let's see. Did my thing go to sleep?

It did. It went to sleep. Let's try it again. It turns out that the application I wrote to do that, um, yeah, if it doesn't fall asleep, it eventually consumes all the available memory on my computer. This is a slight memory, we haven't fixed yet.

Transcribe the following reviews from this video. The reviews include the author's name, the review text, the date inferred, and the star rating, and the sentiment of the review. You'll have to infer the sentiment and calculate the estimated date of the review based on today's date. Today's date is September 11th, 2025. Return your results in JSON format with each key and value corresponding to the fields I specify.

And let's go ahead and hit run. I think Google Reviews is a really good use case. The one that always springs to mind for me is if you have a customer service like support system. You get a lot of emails back. Even if you don't, we get emails back about the newsletter.

We get emails from people who just want to tell us off. We get emails from people who want to tell us things that we're doing well. All of that is qualitative data that we should be able to do something with. And I think that's where we kind of get stuck as well. It all just lives in my inbox.

So what do I do with it? Exactly. So here we can see it has gone from a video of me scrolling into a JSON uh file that we can now work with in generative AI. If we didn't want to do that, we could say now reformat the JSON as a CSV. And from here, we uh it's gonna take that reorder the data, and it's gonna put it into a CSV format, which you can then take into your Excel spreadsheet or the or your Google Sheets or the system of your choice.

I like to start going with JSON first because I it tends to perform better and have fewer errors, but you can try to skip straight to CSV files, but it's generally better to start with JSON first. And so now I've got my CSV file. I could just download this sucker and start working with it. If you are a SaaS company, right? And uh you know very much, you care very much about Captera.

Um, and maybe you maybe let's say you work for comp oh, let's just pick ZipRecruiter. Um ZipRecruiter is on here, they've got these reviews, 4.2 stars with 11,000 reviews. You would follow the exact same process and just scroll through the pages of reviews and grab that data. This anything you can see as a human being, you can extract data from. So if I was a product marketing manager and I didn't have access to expensive social media management tools or review management tools, this is at least the way I would get to that data.

What do you think, John? Where are you gonna start? Man, I'm gonna go get my free API key and pull down all the garbage I can from Reddit. I didn't realize that it was just that easy. So it's just what is you have you ever heard of the limits?

Is it like you know, a gig a day or uh let it's about 10,000 posts and comments per day per subreddit per subreddit, really? For subreddit. Oh wow, so that's but I think you limit it's like 150 or 200 subreddits a day. Oh, okay. All right.

So and then what does it hit a paid tier or do they just shut you off and make it? They just shut you off. Uh-huh. And if you don't respect the back off after a certain number of uh retries, then your key gets canceled. Uh-huh.

You'll get completely cut. But I would imagine that that's a process that you could probably automate as well. Like once a day, go pull some like the previous days uh something, so that you're not hitting those limits. Mm-hmm. Yeah, the one we have for trust Insights clients, because we do this for a few clients, uh, runs every Monday and Friday.

So it just catches up for like the last four days. That's not bad. Mm-hmm. I like it. Yeah.

So that is qualitative data. Again, if you can see it, Gen AI can process it, and then it is up to you to figure out uh how to calibrate it against reality and ultimately how to make use of it to do something productive. So I I think this goes back to what we've talked about on previous episodes of the podcast with live stream, is AI does not replace the critical thinking. So what you did, Chris, was piece together a lot of different things to come up with a full story as the human because you use critical thinking of all right, let me think about what I'm trying to do. Let me think about where that data could be.

See it, and so those are if you're using your critical thinking skills. And that's sort of why we keep saying critical thinking is what you really need to refine versus worrying that AI is gonna do the stuff. Exactly. And and being thoughtful and methodical in your process. Yeah.

All right, that is gonna do it for this week's show. Thanks for tuning in, and we will 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.

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