66 | A Deep Dive Marketing Strategy with AI - Beyond the Basics with Liza Adams

Are you harnessing the full power of AI in your marketing strategy, or just scratching the surface?

While many see AI as a tool for content creation and ideation, its potential stretches far beyond. It's about strategic transformation, not just tactical efficiency. This episode dives into how AI can be a game-changer for your business, offering insights far beyond mere content generation.

Imagine conducting competitive analyses, crafting heat maps, and uncovering market opportunities with the precision of industry giants—on your own.

With AI, these high-level strategic evaluations are not just possible; they're accessible. We explore how leveraging AI for strategic marketing can dramatically alter your business trajectory, offering a competitive edge that content creation alone cannot provide.

Discover how to use AI not just for efficiency but for strategic advantage.

In this session, you'll discover:

• How AI transcends content creation to offer strategic market evaluations.
• Techniques for detailed competitive analysis and market opportunity identification using AI.
• Real-world applications of AI in marketing strategy, as demonstrated by our expert guest, Lisa Adams.

Our guest, Lisa Adams brings two decades of marketing expertise, having served as VP of Marketing across various industries. Recently, as a fractional CMO, she has been at the forefront of utilizing AI to craft and execute cutting-edge marketing strategies. Connect with Lisa on LinkedIn to delve deeper into her insights on leveraging AI in marketing.

Don't let your AI journey stop at content. Dive deeper, think bigger, and transform your marketing strategy with the insights from today's episode.

About Leveraging AI

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• Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/
• Free AI Consultation: https://multiplai.ai/book-a-call/

If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Transcript

Isar Meitis: 0:00

Hello and welcome to Leveraging ai, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Metis, your host, and have an amazing show for you today. Many people use AI for marketing. It's probably one of the widest spread use cases, but most people use it to create content or for content ideation, and it's great, right? it's a good use case and it provides results while reducing costs. So it's definitely not a bad thing to do, but it's. A tactical efficiency and it will not be a game-changer for a business. It will provide some added benefits. However, you can use large language models like ChachiPT and Gemini and Copilot, and whichever your choice of large language model is to do strategic evaluation of marketing, which if you do it right, it can dramatically impact the trajectory of your business. Now, this obviously provides a much bigger impact than generating content and gaining some tactical efficiencies, and so it's something if you learn how to do, is very exciting. You can create extremely detailed competitive analysis, heat maps, and identify real opportunities in your niche and in your market. Stuff that usually McKinsey and Gartner's know how to do, you can do on your own with those tools. Lucky for us. Our guest today, Lisa Adams, is an expert in doing exactly that. Lisa has spent the last 20 years as VP of marketing in various companies in different industries. So she's a real expert in developing and executing on marketing strategy. But in this past year and a half. She has held a fractional CMO position in several businesses, and in those businesses she's leveraging AI to identify and run marketing strategy. Now, as I mentioned since this has the opportunity to actually transform your business, I find this really exciting, and hence, I'm really happy to have Lisa as a guest of the show today. Lisa, welcome to Leveraging ai.

Liza Adams: 2:52

Hi, Azar, great to see you, and I'm so excited to be here. Same here. I

Isar Meitis: 2:58

listen, what you showed me on our prequel blew my mind. Like I've seen people, I use this with myself and my clients, but the stuff that you do is professional, top of the line level, like super Bowl kind of level. And I'm very excited to do this. What I wanna start with is really a step back, right? Because what you do is a strategic view of a business. How does AI even understand, like how do you get started? because it's, it has to really understand your business, your niche, your industry, like a lot more than a basic prompt. How do you get started in. The very beginning of the process to define the stuff that you need in order to feed it into ChachiPT or whatever tool you're gonna tell us to use in order to even get the results that you're getting. Yeah,

Liza Adams: 3:50

I have this philosophy that it's your domain expertise and your knowledge first. Rather than AI first, right? AI to me is a tool, much like a hammer is a tool, right? A hammer can be used to build, and at the same time it can be used to destroy, and it depends on the intent of the user. And the expertise of the user. The user might be a novice carpenter or an expert carpenter, right? the way I use AI is I think about my core expertise. My team, I. and my core expertise, what we do on a daily basis and do we try to infuse it with our work. And I tend to lean more on the strategy side of things. just as they say that you can't out exercise a bad diet. I. I don't think that we can out-campaign a bad product market fit, right? So it's first about deeply understanding the market, figuring out your top segments to go after that match your products, identifying the, value propositions when you get those right, and you get the ideal customer profiles, right? Then it gets so much easier to do the campaigns, the emails, the ads, the search strategies, right? So you will see, in this conversation that, I tend to lean more on the strategy side and we've infused ai, to help us with that work. Not just on the content creation of creating the strategy, but in ideation and collaboration, in automation, in deep analytics, data analytics and insights. And then last but not least, personalization.

Isar Meitis: 5:32

Amazing. Okay, so first of all, I'll say two se 2 cents, and then I really want to dive into your process. yeah. But my two sense is I agree with you a hundred percent, I, in addition to teaching courses, I consult to businesses like I meet with a lot of business, owners and leaders and work with them on how to implement ai, and I tell all of them the same thing You can. There are low hanging fruits and you should do them in parallel to the whole strategy thing because the, they're quick and you're gonna get some fast wins and you get, get buy-in and people are gonna get excited and you should do them. But the real difference is understanding how to better define strategy. I. Because that's gonna, the gains from that are in a completely different scale than gaining day-to-day efficiencies on tactics that you're doing. So we're a hundred percent on the same page. Let's dive into your process. Yeah. Show us your magic. tell us how to get started.

Liza Adams: 6:20

Yeah. so let me, why don't we start with a competitive analysis use case.

Isar Meitis: 6:25

Sounds amazing.

Liza Adams: 6:27

Okay, so let me share my screen, here with you.

Isar Meitis: 6:34

For those of you who are listening to this on the podcast, first of all, you can if you, I suggest you keep on listening, which is probably walking your dog or driving or doing something that does not enable you to look at the screen. But if you wanna see the screen, you can also follow us on our YouTube channel. It's Multiply spelled with AI in the end, instead of Y. Multiply ai. That's the channel. But we are going to share with you everything that is on the screen in words, so you can also follow up with us this way.

Liza Adams: 7:02

Awesome. All right, Isar, can you see my screen? Yes. Fantastic. All right. This is an overview screen and what we're going to, what I'm gonna share with you first is a summary of the steps that we take, that we have taken in my team, on how to do a competitive analysis and positioning. And in this particular case, we are going to pretend that we are a prospect or a potential customer of project management software. Okay. And, we're going to just see how a prospect might be evaluating various vendors in that space. Okay, so in, in this use case, there are three steps. The first step is that the prospect goes to a review site where customers review different pieces of software. So like G2 or one of those, right? G2 Captera Trust, radius, Gartner, peer Insights, those kinds of, sites. this one happens to be G2. Okay. And in the project management world, just as an example, we're going to compare Asana versus Monday.com. So in step one, in G2, you can actually say, Hey, I want to compare these two vendors. And, in that comparison, it gives you a feature by feature comparison side by side of those two, two vendors, And the scores

Isar Meitis: 8:25

for each one from the different people. Yeah, that's right.

Liza Adams: 8:28

And you can actually tell, which feature might be rated higher. By vendor based on the customer reviews. And it also shows side by side the demographics of the customers, reviewing the vendors. So it could be verticals, it could be size of the business. So it, it's a, and it's also locations, right? where are they, based, in Americas, in EMEA and so on and so forth. So this example. we are able to take the CSV file or the PDF of those side-by-side comparisons, or you could even take screenshots of those side-by-side comparisons and plug it into ChatGPT. I'm using GPT-IV, which is a$20 per month version, and ask it to analyze those two competitors. The second step of this, and I'll show you each one of these steps. but I'm just giving you the overview right now. The second step is to ask ChatGPT to go browse the web and find any third party resources out there, whether it's buyer's, guides, analyst reports, blogs, articles that might be comparing Asana and Monday.com. With that browsing capability and with that analytics capability, we then get an analysis, based on those third-party web resources. And then finally, the last step of this process is to combine the output of the customer review analysis from G2, with the third-party web resources analysis that the, the ChatGPT did. Then provide us a combined analysis that shows in what dimensions or in what key parameters one might be better than the other, and identify what is the ideal customer profile based on what we found on the web and those reviews. So I'm going to show you what, how we did this. So here's step one. Where we've taken screenshots of the side-by-side comparisons. or we could take the CSV file, PDF or Google Sheet. We uploaded that into ChatGPT. So you can see here that we've uploaded the PDF and we prompted it. I'm a potential buyer of project management software. You are an expert in this space and have knowledge of various competitors. Attached is a PDF of a summary of customer reviews from Asanaandmonday.com on G2. Please review and deeply understand it. What can you tell me about. Asana and Monday based on this information. So then you can see the chat. GPT analyzes that information and then it gives us an analysis, high level right in many dimensions from satisfaction to pricing to project management capabilities through remote work, compatibility and so on. So that was the first step. Then the second step, as you can see here, I prompted it and we said. Please browse the web and find any articles, buyer's, guides, analyst reports, blogs, social posts, from third-party resources that compare Asana versus Monday. Please show me the list of resources and the links to the resources that you use for the analysis. And the reason I'm asking that question is I want to make sure that it's not hallucinating or just making up things. So we need to make sure to double check again, human oversight. This is just a tool we are ultimately accountable. So you'll see here that it came back with a number of, sources, and that's what in the citations, which are the quotes in blue. And it provides an analysis, again, in multiple dimensions, right? But what I didn't like about this one is it actually didn't show me the list. When I asked it for the list of resources, so I challenged it. Yep. So we said please provide a list. I want to see the list, of these third-party resources. So now it did give me the list. And then, I actually visited these, links that you see on the list and verified that these are truly comparisons and articles that talk about Monday and the sun.

Isar Meitis: 12:37

I want to pause you just for one second. Yeah. First of all, I think this is brilliant. Like the approach itself is amazing. I want to add, two more things about this very last thing about getting resources and reducing hallucinations. It's a key thing, right? It's these. Systems are amazing. They can do amazing stuff, but they will make stuff up in the same level of detail and confidence as the real stuff. And in some cases it will combine the two. Like it will give you some real stuff and then make a few things up in the same answer. And so the only way to verify this is to do exactly what Lisa suggests, Show me the links that I can go to the articles and see that it's actually talking about the topics and it's covering the things that you're telling me that it's covering. So that's number one. That is a very important point you take out of this two. I have another resources. Yeah, go ahead. Just a second. I'll let you. But two other resources that I like to use when I'm looking for links. one is perplexity. So perplexity ai. if you listening to this and you haven't used it yet, yet, is if. Google search and chat. GPT had a baby. perplexity is what it is. So it's like a. a large language model meets a search engine. It's currently probably the best implementation of that, and I'm sure over time a lot of them will go in that direction. But what it does out of the box is it gives you the links to the resources of anything you search. So it gives you. An AI summary of the questions that you're gonna ask, but it's also gonna give you the actual links of where it found the resources even without asking for it. So that's right. Another resource. And this third thing that I found that is very useful, and it's recently, and I don't have a lot of experience with it, but every time I tried it actually worked well is using Mistral, which is an open source, very good large language model. And it's also very good at giving you actual, real. Sources from the internet on things that you're looking for. So these are just another 2 cents on how you can do part of that process. You wanted to add something?

Liza Adams: 14:31

Yeah, I was going to Great list, by the way. that's fantastic. Isar. the other technique that I use to mitigate or at least minimize hallucinations is I work with two AI assistants in simultaneously in combination. So if, let's just say chat, GPT gives me a response and gives me some insights, I would then feed that exact same answer into Claude or. Gemini or whatever you got, Bing chat, and say you're prompted with, your goal and the role and all sorts of things. So it understands and say, by the way, this is the response from ChatGPT. Claude, what do you think of ChatGPT's response right now? Tell me the pros and cons. What might you add? What is missing? What are some of the gaps? And then Claude will tell me, oftentimes they agree, but sometimes it has new ideas, right? And when it has those new ideas or it has disagreements. Then I go back to ChatGPT. ChatGPT I fed this to Claude had a different opinion or it suggested a few more things. What do you think about these new things? So again, you know it. One, it mitigates hallucinations. And then secondly, when it comes to collaboration and ideation, ideas get better because you now have perspectives, more perspectives from, I don't wanna say humans, from more machines to, to help our ideas. Yeah. ba basically be better.

Isar Meitis: 16:07

Two, two more ideas. I love everything you're saying. I saying I think it's absolutely brilliant. I'll add two more ideas of stuff that I'm doing. first of all, you can do this within GPT within whatever tool you're using right now, right? You can use the answer from ChatGPT to send it back to ChatGPT to evaluate itself. I do that a lot. Like I, I played against itself and say, okay, here's an information I got and I do this in a different chat. Like I open a different chat and say, here's an information I have about this and that. Can you evaluate it, figure out if it's real or not. Look at the sources that are mentioned, check that information is accurate and let me know what you think. Or if you have anything to add or if you have any questions that weren't asked, blah, blah, blah. And then you get that. The other thing that, that is really cool to do, and I do that more and more recently, is I use a plugin for, for the browser that's called ChatHub. And it's not the ChatHub tool that is like a tool for chatting. It's not ChatHub allows you to view multiple LLMs at the same time, giving them the same exact prompt. So it's as if you copy paste, so you can run your entire chat. On 2, 3, 4, 6, large language models at the same time and see the differences in the results. But you also see going back to reducing hallucinations, you see the overlap. If it overlaps from four different sources, it's probably real'cause the chances that four different large language models are gonna make up the same exact information. Is relatively small, so That's right. It's just another way to deal with that. But let's jump, I, we, you and I can geek about this probably for the next hour just on this. So let's jump into the process. So now, yes, I wanna remind everybody where we are in the process. You've done the initial step of taking the data from Gtube and asking to analyze that. Now we ask it for a list and you have a list of resources of articles and other, internet resources that compare the two tools you wanted to

Liza Adams: 17:54

compare. And it's analyzed the data from those third-party resources. So now we go to the third step, which is combining everything that it's analyzed, and we're basically asking based on the combination of the customer reviews analysis and the third-party reviews analysis, please show me a comparison chart for the two vendors in the key areas analyzed and key takeaways for each area. Then provide a summary depicting the ideal customer and needs for which each one is suited. Think about this step by step. Take a deep breath. There is no rush. I have full confidence in you. Again, do you have any questions? that is just part of the prompt. And I find that when I give it the time to really think about it and also give it an opportunity to ask questions, the responses are a lot better. And it also helps me figure out whether I prompted it with enough information for it to do its job. So here you can see that it created a radar chart for us. I didn't even ask for it. Yeah. Amazing. So this radar chart,

Isar Meitis: 18:59

so those of you who don't see it and don't know what a radar chart is, it's like a circle, which is divided into several different sections. Each section is a vertical of what the thing can do, and then it shows in each and every one of those segments. Which tool does better on that particular topic? So you can see which areas are overlapping and which areas are done better by, by one of the products. And again, it's like Gartner style kind of information and you can do it yourself, which is mind blowing.

Liza Adams: 19:28

Yes. Yeah. So this is just one way that it's representing it, right? You could actually pump this thing to represent it. Numerous ways. visually I have done some, experiments with it where it's given me 18 different visual representations of the data. Yeah. I had used publicly available Disney World, wait times, posted in actual times data and determine, whether it can produce different kinds of visuals. So very cool. Anywhere from bar charts to radio, radar charts, to violin charts. I didn't know violin charts exist, so play with it and see what kind of visual representation, is best suited for whatever analysis you're doing. So here in this, In this use case, we see the radar chart. It then provides an analysis of each one of those dimensions. We happen to have eight dimensions here, and we could see where one vendor might be better than the other based on the reviews and the third party data. And then what I love here is it also talks about the ideal customer profile. So here it's saying the ideal customer profile for Asana would be teams and organizations looking for a robust portfolio management. and there's extent and they want extensive, integration capabilities. the strengths are in task, project views. Workflow automation and it's suited for small to medium sized businesses, particularly those needing a free entry level option. On the other hand, on Monday.com, it's saying that it's suited for businesses of all sizes. Especially those requiring a more budget friendly option with strong collaboration and task management features. So you could see like the way a potential customer might go through this process to evaluate vendors. However, the other use case. Basically says, as a marketer, if I was with either Monday.com or Asana, I would be looking at this deeply because this should tell me, am I positioning in the right way? Am I being perceived in the market in the way that I want to be perceived? And if not, maybe we need to have more large enterprises putting reviews in here. Maybe we need to have a campaign to get, more reviews about security because we're all about security and it's not showing up in any of the different analysis. So this helps us not just understand our competitors, but also understand customers' perspectives. And it gives us a. a good pulse on how the market is perceiving us and whether or not our positioning is in the right place.

Isar Meitis: 22:20

Brilliant. I will say something in addition to what you're saying, because I find this very interesting. I'm a big believer in, category design. I dunno how much you familiar with that whole theory, but the idea is instead of being better be different. Because being better is a, in many cases, a race to the bottom. And it's a race against somebody who's already bigger than you and your chances are very small to actually win the race. And so even if it's perception, like you could have a better product, but somebody perceives. The current, king of the category as the better product. Your chances of actually taking that position are very small. What you can do, just looking at the graph we're looking at right now, there are three areas in which both solutions are not doing very well. So if I want to develop a new product in that category, I will focus. In these new, in these three specific dimensions that the other leading products are not actually doing well because now I have a differentiator and I have a reason why they would use my product versus Monday or Asana because I'll be better fitted to the people who look for those very particular dimension. So this kind of information is extremely useful. Whether you are one of the companies or whether you're a potential competition, that you're trying to figure out how to highlight specific things that will make your business more competitive.

Liza Adams: 23:43

Absolutely. I love it. I love that we can use the same use case, same analysis, and get different perspectives. Yep. in, in, in the real world. I'm happy to go through the next use case and since you went down the category creation path, maybe we'll talk about, category creation and positioning in a category. That's good. Would that be, okay, great.

Isar Meitis: 24:06

Yeah, I think so. Just to summarize this is really an amazing way to harness three different things that large language models do very well. One is data analysis, right? We started literally by pure data analysis. Here's the data from G2 or any other source and analyze it for me. Two is research. Go and grab data from multiple sources that I don't have to find. Go find them for me that are relevant to the thing that I'm searching. And three is, summarization. Okay? Now you have 40 pages of data that you need to put together into. Five paragraphs and categorizing these bullet points that will make sense, which again, you can give a human to do. It will just gonna take a lot of time. And so really leveraging very well the capabilities that large language models have to create extremely valuable competitive analysis information. which again, I find absolutely amazing. So yeah. Now let's go to the next use case. You said category design. It's one of my favorite topics to talk about.

Liza Adams: 25:09

That's great. All right. This is, I've actually created a GPT for this. So for, oh, cool. Your audience GPT stands for generative pre-trained, transformers. and, it allows you to create an AI tool designed for a very specific task, right? and this particular one determines. Whether your positioning is sustainable in the market based on the value that you deliver compared to your competitors and how different and how hard it would be for your competitors to copy your differentiators. Phenomenal. we have an XY chart. Basically on the Y-axis we have value delivered and you could have the same value that you deliver compared to your com competitors incremental or an order of magnitude better. On the x-axis, we have the defensibility. Is it the same or is it unique and sustainable? Or in other words, are your different differentiators easy to copy, or is it super hard to copy? Super hard, meaning they probably need to hire your top engineers. Or they need a lot of money to replicate what you're doing, or they may even need to buy you and acquire your company to, to

Isar Meitis: 26:37

do it. how, how serious is the moat,

Liza Adams: 26:39

right? That's right. That's right. And to your point of how serious is the moat in the world of ai, just being AI enabled. May not be serious enough, right? Yeah. We're all, building on top of the same la large language models.

Isar Meitis: 26:56

Yeah. Yeah. I'll say two things about the moat with ai, and I talk a lot about this in my courses and with my clients, but there's really two ways, and it's 2 cents. I really wanna see your flow here, but there's two ways to win with ai. One is if you have proprietary data, right? If you have data that your competition doesn't have. You can now do things with these models that other people can do because you have the data and they don't. So this is way number one. Way number two, to win with AI is to just learn how to use these tools much better than your competition, and that's not defensible. So going back to what's defensible and whatnot, learning how to use these tools very effectively is not defensible. Everybody can learn, but if you do this faster or better, you're gonna get a benefit that some people don't have. But if you have. Your own data and people say, I don't have data. I'm not Google. I'm like, yes you do. You've written 600 and fifty-three proposals in the last year. You won twenty-five percent of them you lost seventy-five percent of them go analyze them. That's data that nobody else has because you're the only one that written and received responses to these proposals you've had. If you're recording them, X number of hundreds or thousands of sales calls that you can transcribe and analyze to see what you're doing, Everybody has data. It's just figuring it out. Now, in many cases, the data is messy and it takes time to put it in a way that AI can use. But most companies above a certain size have a lot of proprietary data that they can use in order to get defensible positions because they have the data and their competition doesn't. yes.

Liza Adams: 28:33

I agree. and I think the other dimension to what you just said there is we need to do this more often than not. Some people believe that we just do this when we're launching the company. Yeah. At the beginning. Let's figure out sensibility. Let's figure out product, market fit In the world of ai, we have to do this consistently. I don't even think that a year is good enough. even quarterly is, may not be good enough in some markets. if we look at what's happening with AI technology, it's advancing so quickly that it's changing user behaviors, it is changing their expectations. And even big companies like Google their market for, let's say ad search, that's potentially in jeopardy. So that product market fit, that defensibility is in question. So I think, whether you're a small company startup or a very large entrenched, established company, looking, taking a look at defensibility more on an ongoing basis is going to be crucial in the world of ai. Okay,

Isar Meitis: 29:37

agreed. So let's dive

Liza Adams: 29:38

into the actual process. yes. So we're gonna dive into the actual process and I'm actually gonna show you the GPT, but you could also do this without the GPT'cause I'm going to show you exactly what the process looks like. So it's called the competitive Defensibility Analyzer, and it will ask you a number of questions, and that it will determine whether you are a category maker, meaning that. You have a lot of value, order of magnitude, of value delivered to your customers, and your differentiators are truly, hard to replicate and are sustainable over a long period of time. Or you could be just me too plus, or the next release, maybe the next release of OpenAI feature set. You are no longer as relevant to the market, right? So we need to analyze how long, we would be relevant. Okay, so here's the GPT. Basically the competitive, defensibility analyzer will ask you what space are you in? So in this example we're saying we're in the employee experience solution space, so that's what we inputted there. And then it will then ask you in that space, compared to your competitors, do you have value that is the same as others? Is it an incremental improvement compared to others, or do you provide an order of magnitude more value compared to others? And I just said, you know what, it's an incremental improvement, so I put B in there. And then here's, what you were saying Esar on, what you can use as a moat, right? Typically technology or just having AI is not a defensible mode. The model, this GPT is basically going to ask, give me your top three differentiators. And I give some examples in here, but you could actually put anything in here. it could be packaging, it could be pricing, but some good examples are unique and proprietary data, innovative technology, customer experience. Verticalization or, and or specialization. End-to-end workflows. Your partner ecosystem, your distribution channel could be your customer base, your community brand equity, and so on and so forth. You might have a unique business model. Funding is even could potentially be, defensible mode, right? So you're going to choose three of these. So in this case, we chose end-to-end workflows, partner, ecosystem, and technology. the GPT would then acknowledge that and ask us, Hey, how would you weigh each one of those differentiators? Would you weigh them equally in terms of value that you deliver to customers? Or do you wanna apply more weight to one versus the others? So here I said, end-to-end workflows is 30%. Partnership ecosystem is 40%, and technology is 30%. And then it will then ask, for each one of those differentiators, tell me, Lisa, is it easy, moderate, or hard to replicate? So we said end-to-end workflows is easy to, replicate. Partner ecosystem is harder to rep is moderate to, and then the technology I'm building on the same. Tech as others, and it's probably just gonna take a few months. It's gonna be fairly easy to replicate. It does a calculation and it's not rocket science. You can actually see, when you use the GPT, you can see that it's doing a defensibility score and it's also doing a value score for the X and the Y axes, right? and when it did the calculation, we have a defensibility score of 1.4 and a value score of two. And then it puts us on a chart. And in the chart it basically says you're not a category maker. you, this is not a defensible position long term. You are a next release a, a company, right? You need to really think about either adding more value and or figuring out what other ways you can defend your position in the market. Lemme pause there.

Isar Meitis: 33:57

I love this. I want to touch on a few things here that are very important, connecting some of the dots of the things. First of all, for those of you who are listening to this who have not built GPTs, it's available to any paid customer of OpenAI. There's now attempts to do similar things on other platforms. You can do this somewhat, do this on the backend of Gemini. I don't remember how they call their sandbox, environment, but you could do it over there. it's not still the same, and you can do this. On open source platform as well. Again, not as good as open AIs right now. They're probably all gonna catch up, but what it allows you to do is it allows you to build your own process, right? So what Lisa built is a generic tool that anybody can use, but you may have stuff that you may not want to share. With the world as a GPT like Lisa shared, and you want to keep it in-house, you can do that. You can build a process analyst product analysis, next feature research, like whatever you want for a very customized, process that you have in your business that will also use AI to ask the right questions. Make sure that everybody actually follows the process that you want them to follow. provide additional information from. Your company from third-party sources and so on, and build this flow that will give you an outcome that then people in your business can use again and again. So this is one really important thing to understand about this. The other thing that I really like about this, and I was thinking about this as you were talking about this, you can combine the two examples, right? So what we did here. We answer the question the GPT asked us. I'm like, okay. I could be lying to myself like, oh, we have a huge differentiator in the market. It's fully defensible. I'm like, okay, let's see what our customers are saying about our product or service, or what they're saying about the competition that has similar products and services. And now you get a more holistic view. Off the same thing of like, how am I really differentiated? What are other people saying about my product or my service when I'm not in the room, which is what they're doing on G2 or other review, platforms. So I think combining these two things could be extremely powerful in crafting the next marketing strategy based on here's what we think, here's what. The other, the rest of the world thinks, here's where our competition is looked at for here is the outcome as far as Okay, what we probably need to do. absolutely amazing. I absolutely love this. I, that's awesome. Listen, we, like I said, you and I can geek about this for probably two more days, going back and forth. What you're doing is obviously. I'll go back to what you said in the beginning. It shows that you've been doing this for a while, right? Not the AI stuff, but understanding how marketing strategy actually drives decision and actually drives a business. And how is that the most important thing? Because like you said, and I love the way you framed it of you cannot fix this with good tactics. good tactics will get you some of the way, but if you have bad positioning, if you're not addressing the right. People then your campaigns in the long run are not gonna help your company grow. And in many cases it will be the demise of your company because you're aiming in the wrong direction. E

Liza Adams: 37:14

exactly. Let me just add to that, Isar, AI actually doesn't fix what's broken. Correct. It amplifies what's there. So if we have a bad product market fit, and we do a lot of campaigns using ai. It will actually amplify that misalignment. So it's like taking a product to the wrong market or taking a great or taking, the, customers that are not going to be relevant or are not going to be attracted to the product. And you'll be spending a lot of money on those customers, on those potential customers, So

Isar Meitis: 37:52

yeah, let's put more gas in the car so we can drive faster in the wrong direction. That's basically exactly, basically what you're saying. all right. go ahead. Listen, this was amazing. I wanna allow you to tell people where they can find you, work with you, follow you. Collaborate with you? what are the best ways to connect with you?

Liza Adams: 38:12

Yeah, so I am, I'm on LinkedIn, so it's LIZA and it's Lisa. And you can also find me on my website, which is growthpath.net. And I do this all the time. I'm a fractional CMO, but I also do workshops just to show people what's possible, right? It's to inspire them, what's possible. And it could be in any function in marketing, from product marketing to digital, to demand generation, and then across the business.'cause I truly believe that AI is now elevating the role of marketing, right? And it's elevating it in such a way that people are realizing the need for deep customer insights. Deep understanding of the market and the competition, and then we get that right. Everything else gets so much better. It can help us personalize things. It can help us become more efficient. We've only shown you, what we're doing on the strategy front, right? there's so many more things that we do downstream or upstream, however you wanna think about it. And, I'm really looking forward to. just sharing. I'm prolific, I share everything, and if I can help others, do this in their own business and realize what's possible, that's like the best

Isar Meitis: 39:35

thing for me. Amazing. We're very much alike. I think we feel the same way about this whole thing and about helping other people. I really appreciate you taking the time. I really appreciate you sharing with us. Thank you so much. Thank

Liza Adams: 39:48

you. Have a great day.