In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to use the power of Ideal Customer Profiles (ICPs) to achieve product market fit and accelerate your marketing success. You’ll learn practical strategies for leveraging ICPs to validate your product ideas, ensuring you are building solutions your target audience truly desires. Discover the dangers of relying solely on your intuition as a marketer and how ICPs can provide invaluable, unbiased feedback, leading to increased revenue and engagement. Don’t miss out on the insights shared in this episode as Katie and Chris delve into the future of marketing with AI.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, marketing and marketers—and I certainly hold myself to my hand is up for this—tend to be a little self-centered. We tend to think, “Here’s what we think the customer wants.” And there is some precedent for that. Steve Jobs is famously quoted as saying, “I know what the customer wants, even if they don’t.” Henry Ford was famous for saying, “If we’d asked the customer what they wanted, they would have said faster horses.” So there is that aspect to it. But on the other hand, there is also the voice of the customer. And now, in today’s era, we have more data about the customer than we have ever had before.
Christopher S. Penn – 00:39
So, Katie, when we’re thinking about how you balance what you see as sort of the vision of the product, the service, the company as a leader with what the customer says they want and the voice of the customer, how do you find that balance?
Katie Robbert – 00:55
Well, it’s an interesting conversation because what we’re really talking about is innovation versus solution. And sometimes they’re not the same thing. So, Thomas Edison needed to create these inventions, but nobody was necessarily knowing that these were problems. He was just like, “Let me see what this does. Let me see what this does.” Much like his famous last words were probably like, “What does this button do?”
And so, you need those experimental mindsets, those visionaries, those innovators to keep sort of pushing what’s possible. But that doesn’t always translate into, “Okay, I’m sitting here at my computer every day. Do I need—” I’m just going to make this up completely—
Katie Robbert – 01:49
“Like a teleportation device that brings down a robot that actually serves me like a single serving of coffee that I didn’t even know I asked for.” Like, as I’m saying it out loud, I’m like, “Yeah, absolutely, I need that.” So it’s actually a pretty bad example.
The point being is the regular consumer doesn’t necessarily know that these are problems that we’re having. We talk about this in terms of our services, our data analytics services, that a lot of times people aren’t even aware that the problem they’re having is a governance problem. What they’re aware of is the fact that they want to make decisions and they want to use their data. “So my data must be bad.” We can see—we’re sort of like the doctors in that scenario—we can see the bigger picture.
They’re looking at the hangnail or the splinter that they have, not realizing that it’s actually still bleeding because it’s a symptom of anemia, which is a symptom of low iron, which is a symptom of malabsorption, which is a symptom of, like, an autoimmune disease, which is a symptom of bad gut. They’re not seeing this, they’re seeing what’s right in front of their face.
So, to go back to your original question of how do you balance creating solutions that are far ahead of the problems that people are stating they’re having? You need to have both. You need to offer, “Here’s the everyday, immediate satisfaction solution,” and then you need to keep pushing people forward. And that’s where, I think, using tools like your ideal customer profile comes in really helpful, because we get stuck in,
Katie Robbert – 03:38
“Well, I’ve heard three people say that they’re struggling with Google Analytics, so let me come up with a solution for that.” Well, they didn’t ask for that solution. They just said, “I’m having a problem,” but they didn’t ask us to solve it.
So now, with generative AI, we’ve been able to develop ideal customer profiles and interact with those in a synthetic way as a stand-in while we’re working on getting in front of our actual customers.
Christopher S. Penn – 04:05
So, it sounds like you’re describing product-market fit. So we have a thing. Do we have a market for it? Is it a product in search of a market? Is it a solution in search of a problem?
Katie Robbert – 04:19
What’s funny about that is: new tech, old problems. So, now we’re using generative AI to help us solve existing problems. We’re not creating new problems. This problem still exists: is there a product-market fit for the widget, for the thing, for the button, for the knob, for the service, whatever? We don’t know until we actually do our research. What’s changed is our ability to do that research faster, more efficiently. I was thinking about this the other day when I was driving and my brain was just sort of wandering,
Katie Robbert – 04:59
And the system that we’ve developed to synthesize data down into a really compact, but detailed, ideal customer profile would normally have taken someone at least a few weeks, if not longer, to manually collect all of that information, but then also go through and analyze all of that information and pull out the commonalities and figure out, “Well, where are the trends here? And what are themes here? And what are they saying here?” And now, with generative AI, we can take that same data that we would have used—not, nothing different, nothing crazy—and do it in about 30 minutes. Which, to me, was just sort of mind-blowing to have that realization.
Christopher S. Penn – 05:49
Yeah. One of the things I was playing with over the weekend—I was actually getting ready for a talk this coming week, “The Gender of AI for Hospitality”—and I realized that I still, even after doing the standard process, did not have as good an understanding of the market as I wanted. By the way, you can see the videos preparing for the talk on our YouTube channel if you go to Trust Insights, AI YouTube. And so, I found the industry podcast for that industry and said, “Okay, let’s grab all the episodes from the last year on that.” And that was very, very informative because it was—it’s a weekly show, so there are 30 episodes just from this year alone on everything that is happening in that industry. And you’re right,
Christopher S. Penn – 06:34
Even just hiring a human just to listen to all those podcasts, even at 2x speed, would have been hours and hours, much less trying to synthesize all the insights, when I was able to pull together all of the major issues from this industry in under five minutes. With Google Gemini, you go from hours and hours to five minutes. That’s a huge time difference.
Katie Robbert – 07:00
Well, and I think—not to get too far off track—but that sort of goes into the, “Oh, will AI take my job?” Well, no, it’s taking that task because what it’s allowing me to do, what it’s allowing you to do, Chris, is, instead of us being bogged down by the research, by the summarization, by the analysis, we’re actually able to do higher-quality things.
And so, by you being able to really dig into what is going on in the hospitality industry—not just broad strokes of, like, anecdotes and a couple of, like, social media posts—like, you actually got into the research, you can then create a super tailored, personalized webinar for the hospitality industry and deliver that high-value analysis and message, and it can be actionable for them.
Katie Robbert – 07:57
And then you can continue to move on to other industries. You can continue to take that research and turn it into other things. And so, what it’s done is it’s freed you up to do all of that really high-quality, high-value work, which otherwise you would have still been like, “All right, we got to push out the webinar. I’m not done with the research yet.” Or you just would have been like, “All right, let’s just kind of wing it. Let’s hope that we know enough about the industry to say something credible.” And so, as we think about innovation versus the solution, I think that that balance really comes down to: we need to make sure our foundation is solid. That’s what—those are our solutions.
Katie Robbert – 08:41
We’ve always said that. So our data governance, we need to make sure that’s in place. We need to make sure we know—oh gosh, I guess probably the five P’s if I’m really being honest about it.
The five P’s, for those of you who aren’t aware, is the Trust Insights framework. It’s purpose, people, process, platform, performance. And so your solutions come from the five P’s. So what is the problem you’re trying to solve? Who are the people involved? What is the process to do this in a repeatable, scalable way? What are the tools and platforms that you’re using? And how do you measure success? And so, I feel like you can have both tracks running in parallel. So you have your five P track of, “Here’s what people need. Here’s how we’re going to solve that problem.”
Katie Robbert – 09:26
And then you can take that and say, “Okay, we’ve solved the problem, what else can we do?” And that’s your innovation track. And I feel like companies today need both tracks because the consumers have expectations of generative AI. They don’t know what those expectations are. They just know they want to see the flashy thing, the brand new thing, the thing that they don’t need, the shiny object, but at the same time, they still need their regular problem solved as well.
Christopher S. Penn – 09:59
So dig more into that because I think that’s important. Product-market fit is about saying, “Here’s the thing, and then here’s the market, and does the thing do what the market needs?” So you have the purpose, which is essentially the market, and then the fit is the performance. Does the product fit the market? And then you have the pieces that align to that.
We were recently working on a very detailed analysis for the pharmaceutical industry and one of the things you had pointed out was, “I don’t know how this product that we’ve created fits the intended market, which is the executive of this pharmaceutical company.”
Christopher S. Penn – 10:39
When you look at that example and you go through the five P’s, where do you think, using the five P’s as a diagnostic tool, how would you say, “Okay, this is or is not a good product-market fit?”
Katie Robbert – 10:53
I would want to really double down on the purpose and people. And so pharmaceutical—it’s not a small industry and they’re not uncomplicated companies. They have—they are these mega-enterprise global companies that have multiple CEOs and multiple branches and multiple disciplines and multiple product offerings and, subsidiaries and affiliates and sister companies and DBAs and all of these things. Like, so when I was looking at that sort of the blanket statement of, “As the CEO, I want to,” I was like, “As a CEO of what, the pharmaceutical company as a whole?” Well, I can tell you right now, that guy, even if he reads this report, who is he delegating it to? Because he’s not also the one executing it.
Katie Robbert – 11:52
And so that’s where I kind of got stuck in that specific example. But I think it’s a good example because it speaks to this conversation of innovation versus solutions. And so, I think that when we take a look at that offline, the first thing we’re going to focus in on is really digging into the user stories of, “Who is this really for? Who’s going to take action on it?” from the performance and then work backward from, “Okay, if it’s the marketing director, then who does the marketing director report to? Who’s giving the marketing director their feedback and their action plans and their strategies and their competitive analysis? Like, what does that look like?”
Katie Robbert – 12:40
And then, sort of, work up from there. And that becomes the people of like, “Okay, who are the people who need this information so that they can give whoever’s taking action that direction?” And that’s the way I would approach that. But at the same time, I do think that there’s a place for giving this information to somebody who’s more on the innovator side, but we have to change our expectations on the action that they’re going to take with the information.
Christopher S. Penn – 13:10
Okay, so if you were to go back to the five P’s, and you would say the fit is the performance, the market is the purpose, and then the product would be the people, process, platform used to serve the market.
Katie Robbert – 13:26
Possibly. I mean, and I say that because you’re trying to box it in a very black-and-white way. And there’s a lot of things that go into determining product-market fit. And so, right here, right now, I’m going to say maybe. And then, of course, I’m going to go away offline and, sort of, say, “Where does this actually work?” But I think it’s a good enough way to think about product-market fit. So you’re saying what—product is the performance market?
Christopher S. Penn – 14:02
The fit is the performance, like, it’s a good fit. The market is the purpose: like, why are we doing this thing? Because the market wants—has this purpose, has this problem that needs to be solved. And the product is the people, process, platform. We need to make the product for the market. And hopefully, it fits.
Katie Robbert – 14:20
Yeah, okay, that makes sense. So then when you have—so if it doesn’t fall in line with the five P’s, then what you have is a solution in search of a problem, right?
Christopher S. Penn – 14:31
Exactly, where the performance doesn’t fit. You’re like, “Hey, market says one thing, people, process, platform, we use that to make this thing.” And performance is, “Yeah, but the market—”
Katie Robbert – 14:39
“Doesn’t want this.” Which is really interesting because I think that—and that sort of goes back to our original part of the conversation, where we, as marketers, even with all of the data that we now have access to with voice of customer, we still think about ourselves, whether we realize it or not. We’re still like, “I think this is what our customer needs. I believe this is how our customers would use this.” And by starting that with “I think, I feel, I want,” we’ve already biased the solution to something that works for us, not what works for them.
Christopher S. Penn – 15:18
And so going back to what you started with, which is the ideal customer profile fits into the performance slot because it’s a way to synthetically measure the fit. You say, “Okay, ICP, I think here’s who you are. What are the problems you face?” The ICP spits out, “Here are my purposes, here are my user stories. Hey, I need to know what my competitor is up to. I need to increase revenue. I need to reduce costs.” Whatever. We use people, process, platform to identify the pieces that need to be built. And then you feed that to the ICP. You say, “Hey, I built this thing. Does this solve your problem?” And it can go, “Yes, that solved the problem,” or “No, that’s not what I was looking for.”
Katie Robbert – 16:01
What I like about the way that we’ve started building out the ideal customer profile is that it takes us out of the conversation altogether. It has zero to do with us other than fit, which is what we want. But it takes our thoughts and opinions and what we want to do out of the conversation in terms of the feedback that you get.
And so, for those of you who are wondering, we’ve developed a large language model that can take your data about who you are, your services, your customers, who your sort of dream customers are, who your competitors are, and it can very quickly turn that into a very compact and efficient, but detailed, ideal customer profile that you can then interact with.
Katie Robbert – 16:59
You can either give it to a large language model and say, “This is my ideal customer profile,” or you can build that ideal customer profile as a large language model and interact with it. You can ask questions. So one of the ways that I use it all the time, every week, is when I write the newsletter, I’ll say, “Have our ideal customer profile read this newsletter. Does it resonate with them? Would they take action on it? What am I missing?” And it will give me an alignment report to say, “It’s good, it’s bad, it’s indifferent,” whatever the thing is. And then also, “Here’s recommendations on how to fix it.”
And so, think about using your ideal customer profile with your new products, your solutions, your innovations, your dreams, your ideas.
Katie Robbert – 17:45
Chris, one of the things that I think you would put together at one point was something along the lines of an automated way to curate a Spotify music playlist, or something along those lines. But now, instead of you just running it past me and me being the n of one, the representative of the customers—which is not a great representative, because, again, I’m an n of one—you can now interact with the ideal customer profile and say, “I have this really cool, innovative idea. Is it a fit with our ideal customers?” And it’s going to give you some feedback and say, sort of—or “Yes, absolutely.”
Katie Robbert – 18:34
And then me, the n of one, is like, “All right, well, I guess my opinion doesn’t matter because that’s what our customers want.” And that’s exactly how it should work.
Christopher S. Penn – 18:43
We had that experience recently with the video I made for the Macon Conference, the music video. You’re like, “I don’t like this.”
Katie Robbert – 18:53
But I immediately said, “But I’m an n of one.”
Christopher S. Penn – 18:57
Right, and so how do you convince a marketer then, who is self-centered, “Hey, buddy, you are an n of one. Maybe you should try using an ICP instead of just going with your gut.”
Katie Robbert – 19:12
It all comes down to performance. I mean, we all have those same KPIs of: what is it? Better, faster, cheaper, all of those things. That’s how you get there, is you actually deliver things that people want. They’ll buy more of it. They will interact with more of it.
So, using your ideal customer profile to test your thoughts and opinions about where your marketing should go, where your sales should go, is going to refine it and focus it and—theoretically, but also in reality—make it better. And so, your numbers, your metrics, should go up. And that’s really—if you’re not someone who cares if you ever sell anything, then great, don’t use an ideal customer profile.
Katie Robbert – 20:00
But if you want to increase your revenue, if you want to increase your engagement online, your impressions, your comments, likes, whatever, then use your ideal customer profile before putting things online. Unless you’re someone who is really fortunate and has access to your customers as a community, in real life, on demand, at any time, and they are fine with you asking them about everything you’re about to do. I don’t know a situation where that’s true in real life. Like, asking your customers to give you feedback on something once in a while? Absolutely. Asking your customers all day, every day, to give you feedback on things that you’re about to do? Not a likely scenario, but you can do that with an ideal customer profile built into a large language model.
Christopher S. Penn – 20:51
You can. One thing that you could do, if you already have at least one of your ideal customers, is, in addition to using the standard process, take things like call transcripts and emails and stuff and add that to your ideal customer profile to say, “This is how they actually talk.”
I was listening to a podcast this weekend about a phone company that makes secure phones specifically for criminals and how they zeroed in on their ideal customer profile. Want like drug traffickers. It turns out, amusingly enough, that the phone company was owned and operated by the FBI.
Katie Robbert – 21:32
I mean, there’s a lot to unpack there. But, come on, like how would they not know that?
Christopher S. Penn – 21:42
If you’re curious—it’s called—in fact, I’ll put a—I’ll put a link to that topic in our free Slack community. Go to TrustInsights.ai/analyticsformarketers, because there’s a three-year operation that was wildly successful and is one of the most interesting use cases of influencer marketing that I’ve heard in a long time. But it is using things like understanding your customer, your ideal customer profile. Who is our customer? What do they care about? And how can we make a product that they desperately want? And they did. They did a really amazing job.
So, I think, to wrap up, the ideal customer profile is a good mental check to say, “Am I doing this for me, or am I doing this for my customer?” It’s a great way to do product-market fit.
Christopher S. Penn – 22:37
It’s a great way to understand the purpose, the people, the process, the platform, and the performance to make sure that you have product-market fit and you’re not just making things that nobody asked for, except for you. If you’ve got some stories you’d like to share about product-market fit, and perhaps how you’re using artificial intelligence to [do] it, pop on by our free Slack group. Go to TrustInsights.ai/analyticsformarketers, where you and over 3,500 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you would rather have it instead, go to Trust Insights, AI podcast, where you can find us on the places that podcasts are served. Thanks for tuning in, and we’ll talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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