So What? Using Generative AI for Competitive Analysis

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn how to use AI for competitive analysis. You’ll discover how to leverage readily available tools to conduct your own analysis and how to use the five Ps framework to stay focused on your goals. You’ll get a glimpse into the differences between your company and larger companies and how to make strategic decisions based on your findings. Download Chris Penn’s free ebook to get started with AI for competitive analysis today!

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In this episode you’ll learn:

• What kinds of competitive analysis AI is good at
• Why AI for competitive analysis works so well
• What data you’ll need to do AI for competitive analysis well

Transcript:

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Katie Robbert 00:33
Well, hey everyone. Happy Thursday. Welcome to “So What?”, the Marketing Analytics and Insights Live show. I am Katie, joined by Chris.

Christopher Penn 00:42
Hello.

Katie Robbert 00:43
John is off pounding the gavel of justice, putting away the bad guys, doing his civic duty on jury duty.

Christopher Penn 00:52
Oh, poor guy.

Katie Robbert 00:57
So, he is missed. But we know that everything is good with him. He is just doing the thing. So, this week we are talking about using generative AI for competitive analysis. So, this conversation started a few weeks back. By that, I mean we’ve always talked about how to do more efficient competitive analysis. So, it’s been an ongoing conversation for years.

But this specific conversation of using generative AI came about because one of the things that we’ve developed over the past few months is a process, a methodology, for putting together an ideal customer profile based on your data and some of your competitors’ data. And, every time we start with the ideal customer profile, which is the foundational analysis, we always think, well, where can we take it next?

Katie Robbert 01:49
And one of those places is, well, if we’re already giving the system instructions, your competitors’ information, what would a true competitive analysis look like if you were to say “Here’s my ICP, but now I also need to know what my competitors are doing.” And so Chris, where would you like to start today?

Christopher Penn 02:10
Today we should probably start by defining what we mean by competitive analysis because there’s a lot of ways to do it, and there’s so many really good, well-proven frameworks. There’s SWOT analysis, PEST analysis or PESTLE, BCG, growth share matrix, Porter’s Five Forces. And so, I would say I’d push it back to you, Katie, to stay with where—what kind of competitive analysis do we want to do today?

Katie Robbert 02:38
Well, I am going to—what I coined this phrase on a podcast earlier this week—I’m going to shock and dazzle with suggesting that we start with the five Ps. You can thank John Bailey for that one. He brought that out.

So, the old shock and dazzle, we’re going to start with the five Ps by defining what it is we’re looking for. So, to your point, there’s a lot of tried and true competitive analysis frameworks, and a lot of times the question that is asked is “What are our competitors doing?” And I mean—and so you really need to drill down into, well, what do you mean? Like, what are they having for lunch, how are they getting to work? Or how are they serving their customers? That could be our customers.

Katie Robbert 03:26
So, I would suggest starting with the 5P framework, which is going to help you get yourself organized. So, it’s Purpose, People, Process, Platform, and Performance. Starting with purpose, what is the question we’re trying to answer? And so, in this sense, when we’re saying competitive analysis, Chris, what I would say for us, we want to know how our competitors are messaging their usage of AI with marketing and analytics so that we can also make sure that our audience—our potential customers—aren’t getting confused by what it is that we offer.

Once we have someone in our funnel, once they’re in the engagement part of the funnel, they already know what it is that we do. They’re looking for more information. But for me, when I think about competitive analysis, the most important thing for us is that awareness stage.

Katie Robbert 04:23
We know that our services are solid, we know we get results. I’m not concerned about competing with people in that respect. I’m concerned about the general— awareness of what the heck it is we do. Are we reaching the right people? When we reach those people, are we being clear with what we’re saying? So, that sort of—that’s when I think of competitive analysis for Trust Insights specifically, that’s what I think of.

And, it’s going to vary from company to company. Chris, if you’re doing competitive analysis, perhaps for yourself as a speaker, you’re going to be looking at different metrics that are going to help you make decisions. But for Trust Insights, this is what I personally think we should be looking at.

Christopher Penn 05:09
Okay, so which framework do you want to use with that? And the reason I ask is because of all the different competitive analysis frameworks, some of them require more data than others. There’s, in fact— You know what, let’s—that’s actually not a bad place to start—is to start by saying, well, what are my options? What, what could we even be talking about so that we can go a bit deeper. So, I’m going to go ahead and shift over.

I’m going to start off in Google’s AI studio, using the Gemini model. And, we’re going to do a very straightforward model, prime. So we’re going to do “roll” action and prime.

Christopher Penn 05:49
And I’m starting off by saying “Hey, you’re a business strategy expert in the vein of Michael Porter, Clayton Christensen, Rita McGrath—” all the people that we know and love from business school. We know business strategy—business strategy, management consulting—and the strategies of top firms like McKinsey, Bain, et cetera. We’re going to examine well-known competitive analysis frameworks. What do you know about this topic?” And so, immediately it goes into things like Porter’s Five Forces, critique and evolution, SWOT analysis, competitive positioning, strategic game theory, Blue Ocean strategy, dynamic capability.

So, we’ve got a lot of really good options here of these. Our next question would be like, “What data can we bring to the party?” Because, one of the fundamental rules about AI is the more data you bring, the better it’s going to do.

Christopher Penn 06:43
So Katie, what are some of the things that, for the purposes of understanding say, a competitor’s— messaging around AI, what data would you want to have, and do we have access to it?

Katie Robbert 06:57
So, I would probably say I want to take a look at their search data. So, how are people finding them? What are they looking for, and what are they finding? So, I would probably want to look at some SEO data, which I know is publicly available. I would want to take a look at perhaps things that my competitor is writing about. So, perhaps some of their blog or podcast content, maybe their social media content. If they’re posting long-form content on LinkedIn, or if they have a LinkedIn newsletter, that’s where I would start because I want to see what their perspective is. But not only that, like, how well it’s resonating with their audience.

At this point in the process, I’m less concerned about—”Okay, they’re closing 50 clients a month and we’re only closing 49 clients a month.”

Katie Robbert 07:55
How do we get to that 51? So, we’re beating them—like we’re so far away from that part of the analysis—that first I need to know what our competitors are saying about AI and if it’s landing. Because, what if they’re saying things about AI and people are rolling their eyes, then I don’t have anything to worry about. But if they’re talking about AI, and it’s getting reshared, and people are finding them, and they’re showing up in search, and they’re beating us—for the same terms that we’re trying to rank for—that tells me that we’re not getting it quite right or we need to do more.

Christopher Penn 08:34
Okay, so unsurprisingly I’m going to ask you to pick a competitor, specifically so that we can start to gather this data and do this analysis. And ideally, it’s something—it would be something, maybe, aspirationals. Because, the more aspirational competitors, chances are, the more public data there’s going to be available around it. If you pick— you know—Bob’s management consulting firm down the street that has— you know—one employee and it’s Bob in his basement, there’s probably not going to be as large a footprint as like a Deloitte or an EY Parthenon, for example.

Katie Robbert 09:07
Well, and so to that, for the sake of ease, a very big aspirational competitor would be a McKinsey or a Deloitte, or a Boston Consulting Group, one of those who offers consulting, who offers— you know—those services similar to what we do, but they just do a heck of a lot more because they are maybe a bazillion times bigger than we are, to be statistically accurate.

Christopher Penn 09:35
All right, so where would we go to find that? Well, as you mentioned, SEO data is probably one of the better places to look. So, we could look on—we have Ahrefs, we have Semrush, these are two tools that are very well known. Let’s take a look at—let’s look at McKinsey, take a look at their profile, and then go into their top pages and then let’s see if we can narrow this down. Let’s see. “URL contains AI”, any rule, artificial. Actually, let’s go to intelligence.

Katie Robbert 10:28
And then I would also do analytics.

Christopher Penn 10:34
Okay, let’s apply this and show the results. Yeah. 4,650 pages. Oh, we need to— AI needs to be—you can see it’s picking up a bunch of. Yeah. So, let’s just take out—take that out and just use “intelligence, analytics”, and maybe “prompt engineering”. 532 pages. “What is prompt engineering?”, “analytics and insurance”, “general intelligence”. This is actually pretty solid. This is pretty on the money.

Let’s go ahead and export this. Now, here’s the challenge that we’re going to run into right away. SEO tools are optimized for SEO. They are really good at SEO, which means that you’re going to get in the data export file, you’re going to get a pile of URLs. There’s all the URLs, there’s the traffic they get. This is, again, pretty cool, but this is not helpful by itself.

Christopher Penn 11:38
What we would need to do next is actually figure out how are we going to get those individual pages’ content so we can read the text of what it is they’ve written. This is not a problem AI can solve. This is a practically—it’s a data and mining problem if you have the capabilities to do so. And, granted that can be a big—if you have the capabilities to do so. There are ways to have AI write you the software that you need to do exactly that.

Let’s try to remember what is my input. Now, let’s take those McKinsey URLs, and we’re going to visit McKinsey’s website page by page from this list of 500 URLs and grab them all, or at least grab the majority of them. This is in descending order by traffic they get.

Christopher Penn 12:40
So, if we don’t want to wait for all 523 pages, we might consider doing some early stopping and maybe go at the top 20, like that. That’s a good place to stop.

Katie Robbert 12:50
And—and so I think you’re bringing up an important point, Chris. So, when we look at doing work like this, and we say “Oh, it’s not available in— you know—the system, it’s not the data that’s being extracted”—unfortunately, one of the first things that people do is they’re like “Okay, well who can just start copying and pasting this information into a document?”. So, you give them a list of URLs and you say “Go find the content on each of these pages and copy it into a document so that we can take a look at it.”

And, we’ve talked with a lot of our clients, and a lot of our peers, and the challenge is that the assumption is “Well, I thought AI could fix that. I thought AI was going to make it more efficient.”

Katie Robbert 13:39
And when we really dig into what it is they’re trying to do, we’re like, “Well, this is not an AI thing.” There’s efficiencies to be found, but AI is not the solution. And so, I just want to call that out because it’s something that—in other conversations we’ve been talking about a lot—is people are turning to AI to solve the problems when the problem—it’s not an AI thing.

Christopher Penn 14:03
Right, exactly. And so, you will need some level of plumbing to do the competitive analysis this particular way. To Katie’s point, if you don’t have access to these tools, yeah, copy and paste works, it’s going to be slower. You’re not going to do it on the live stream, but it does work.

So, we’ve already got the first 20-ish or so articles here that McKinsey has written, the ones that get the most traffic. So, our first step should be, let’s start a new chat because we don’t want to do frameworks. Yes, that’s fine, just leave. And, because this is just digestion, I’m going to switch down to the “Flash” model, which is faster than the “Pro” model and also costs less.

Christopher Penn 14:48
We’re going to start by saying we want to do an analysis of language that McKinsey uses to talk about artificial intelligence, data science, and analytics. I’m going to provide you with a few dozen pages of content from their site. What you need to do is outline the major themes that they talk about in descending order by frequency. So, let’s start with this. This is a pretty straightforward ask of this tool, but it is a lot of information.

Katie Robbert 15:38
So, while that’s processing, Chris, to answer your earlier question, in terms of a competitive analysis framework, my go-to is usually a SWOT analysis because it’s—it’s tried and true. It’s easy to understand by a lot of people even if they haven’t done a SWOT analysis before. And, it—I like how it organizes the information into four major components.

So, you have SWOT, you have Strength, Opportunities, Threats, and Weaknesses. Or Strengths, Weaknesses, Opportunities, and Threats to actually spell SWOT. But— you know—they’re all there. And I like a SWOT analysis because it’s, well, “What are your strengths? What are the opportunities? What are your weaknesses? What are the threats against your company” like? Those are very straightforward things that, once you have that data organized, you should be able to take some action on.

Katie Robbert 16:36
And so, I find a SWOT analysis to be, number one, universally understood, and two, very actionable. So, that would be the framework that I would choose for this exercise.

Christopher Penn 16:46
And I think that’s a great way of doing it. I would refine that even further to say that strengths and weaknesses are internal to you and your company. Right? Your strengths and your weaknesses, those are things you have control over. Opportunities and threats are externalities. When you’re doing a competitive analysis against someone, their strengths are your threats, their weaknesses are your opportunities, and vice versa. Your strengths are someone else’s threats, your weaknesses are someone else’s opportunity—an opportunity for them to take advantage of a gap. And so, I completely agree with you. I think it’s a phenomenal framework because it allows you to understand very deeply how you appear versus a specific competitor.

Christopher Penn 17:25
One of the things that I do see people do very wrong with these—we saw this a lot back in our old agency days—is people confused with the landscape analysis. A SWOT is one-on-one, you versus a different—another competitor. If you want landscape, you’re talking either Porter’s Five Forces or PESTLE, not a SWOT analysis.

Katie Robbert 17:46
I would agree with that. And I think your point about internal versus external needs to better understood. I’ve seen a lot of people who are putting together a SWOT analysis get that wrong. Also, fun fact: I’ve seen people call it a “SWOTA”, and I don’t know what the “A” stands for, but— you know—here we are. So anyway, I would use SWOT with an “O”, “O” for opportunities. So, what do we have? What has Gemini given us?

Christopher Penn 18:14
We have four major categories. Of these 90,000 tokens that we fed, which is about 60,000 words. “The crucial role of data and analytics, data-driven decision making,” and stuff, is the most prominent category within McKinsey’s top ranking. “AI and analytics content”, followed by “talent and organizational transformation”, followed by “AI and its applications”, and then the “strategic consideration, business value and usage” and stuff. So, these are the four major themes and sub-themes within their content. So, this is a good snapshot of broadly what their content looks like.

Katie Robbert 18:50
And because I know our ideal customer profile so well, I know without even looking at the document, these are the things that our ideal customer cares about, number one being data-driven decision-making.

Christopher Penn 19:09
Mm. Okay. So, let’s now start to build that strategic analysis. We’re going to say next, “We’re going to perform a SWOT analysis using this data plus data about our company, Trust Insights. Trust Insights is a direct competitor to McKinsey. I will provide you with information about Trust Insights as well as the Trust Insights ideal customer profile. From this information, construct a SWOT analysis according to these guidelines:

Strengths: These are the things that Trust Insights does well. Internal.

Weaknesses: These are the things that Trust Insights does less well. Internal.

Opportunities: These are McKinsey’s inferred weaknesses based on the content program provided. External.

Threats: These are McKinsey’s inferred strengths based on the content provided. External in your analysis.

Return an outline of the strengths, weaknesses, opportunities, and threats from the point of view of Trust Insights.”

Now, let’s go ahead and get our knowledge blocks.

Christopher Penn 21:19
We will need the “Trust Insights About Us” knowledge block, which I will pop in, which is like four and a half pages of information about us and all the things that we do, and the “Trust Insights Ideal Customer Profile”, and put that in as well. This is a huge amount of text. We’ve added another 3,000 words of information. Now, the one thing that we probably should have added but we did not, and we didn’t add any of our own content in here.

Katie Robbert 21:52
You mean stuff that you and I create outside of Trust Insights?

Christopher Penn 21:56
No, I mean like—oh, you mean our actual—

Katie Robbert 21:59
Sorry, I was like, “Wait, what?” But we did. So, you mean actual Trust Insights content, which I would say, honestly, probably the easiest thing to do would be to— you know—I think you put together an ebook of your newsletter, and then you put together an ebook of our newsletter. And I think that’s fairly representative of our collective expertise. I would say, for the ease of— you know—the live stream, that’s probably going to be good enough.

And so, for those who are wondering, at the end of every calendar year, because we publish what, 52 newsletters for Trust Insights and 52 newsletters, Chris, for your own personal site, Chris has started putting together ebooks of all of those cold opens from those respective newsletters. And so, those are then compiled into one place so that you have all of those writings.

Katie Robbert 23:03
And so, that’s what I’m suggesting Chris use because I write the cold open for the Trust Insights newsletter, and obviously Chris writes his. And, we always write about things that are important to our brand, our business, what our Trust Insights customers are going to care about. So, I feel like it’s a good representation of our content. It’s not everything, but at this point, we don’t need everything.

Christopher Penn 23:29
Right. So, I did “Stuff, Letters from the Corner Office”, the 2023 edition, which is about 45,000 words in here. And so, that is in addition to our ICP, that is in addition to our company. And, hopefully, Gemini will come up with a better SWOT analysis by giving— you know—more parity to our content so that it’s not just two pages of “about us” versus the— you know—90,000 words—it’s 60,000 words—of McKinsey stuff. So, let’s see what we got here.

“The SWOT analysis performed from the perspective of Trust Insights considering McKinsey’s capabilities and weaknesses as inferred from the provided text.”

Christopher Penn 24:06
“Strengths: Internal: Actionable insights. Focused niche expertise in marketing analytics. Strong AI/ML capabilities. Effective content marketing. Community building. Flexible service models. Strong leadership and reputation. Data storytelling. Communication.

Weaknesses: Internal: Smaller scale and resources. Brand recognition within its niche, but its overall brand awareness is substantially lower. I mean, yeah, geographic reach pricing and potential over-reliance on founders,” which that is interesting because that’s not something that we explicitly called out.

Katie Robbert 24:37
No. And for those of you who don’t know, you are looking at Trust Insights. It is myself and Chris— you know—we also have John, who does business development, Kelsey, who’s our account manager. But, the reliance on the founders is—

Christopher Penn 24:52
A thousand percent accurate. So, that is a weakness.

Opportunities, let’s see: “McKinsey’s broader focus is an opportunity for us because they’re all over the place. McKinsey’s difficulty scaling analytics. Challenges in outscale, high-level, less actionable approach. Talent acquisition challenges. Lack of niche, community building, and higher costs.” And, those all seem about right.

And then threats: “McKinsey’s brand and reputation is obviously a threat to Trust Insights. Global reach and resources. Diverse service and portfolio. Established client relationships and potential enter our niche.”

So, those, I think, from a very limited amount of data, is—is pretty good.

Katie Robbert 25:37
I agree. Nothing stands out as “Wow, that is egregiously incorrect.” So, then the question is— you know—and this is often what happens. So, I said a SWOT analysis is very actionable. But I can think—I would imagine a lot of people looking at this going, “Well, now what the heck do I do?”

Christopher Penn 25:57
Exactly. So, what the heck do we do?

Katie Robbert 25:59
Katie, honestly, if this were just me on my own, I would—my next prompt would be great, “Where do I start? What the heck do I do?” I would probably even say, “What the heck am I supposed to do with this?”

Christopher Penn 26:15
I think it’s better to say, “Given this—”

Katie Robbert 26:19
All right. But in re—but in actuality, what I would likely do is— you know—say “Let’s develop an action plan, and here’s my ideal customer profile. Here’s what they care about, here’s how they want to be communicated with. Here’s the topics that they specifically want to hear about from us. Help me put together an action plan.” But you can also just say, “What the heck am I supposed to do with all this information?” And, the AI will guide you to an action plan.

Christopher Penn 27:00
So, what I’m saying is “Given the SWOT analysis, let’s develop a strategic plan for Trust Insights, leveraging the Trust Insights ideal customer profile for Trust Insights to mitigate their weaknesses and bolster their strengths. Everything should be from the customer’s perspective. Given the smaller size and scale of Trust Insights, prioritize the strategic plan based on the most achievable to least achievable priorities. Return your results as an outline.”

Now, I’ve also— Because we’re now going from summarization to thinking, I have switched models. I switched from Gemini Flash to Gemini Pro. Pro is a more expensive model. It takes longer, it consumes more resources, but it is much better at thinking and reasoning. Flash is better at saying “Hey, here’s the data you give me. Here’s a— Here’s a—”

Smaller amount of this, as you’re using generative AI tools, particularly if you use—

Christopher Penn 28:07
If you have some say over which model you’re going to use, choose the model that fits not just the overall task, but individual parts of the task. So, if you’re using, for example, ChatGPT, you have your choice, right, in the ChatGPT interface, between “01 Preview” and “GPT 4.0”. Knowing which one to use for which part of the task is important. Same is true in Anthropic Claude. You have your choices between “Opus 3”, “Haiku 3.5”, and “Son” at 3.5. Knowing which one to use for a specific task, A, will consume less resources, and you’ll hit your limits on your account faster, slower, but B, choose the right model for the right task.

Katie Robbert 28:47
So Chris, we have a comment. “Wouldn’t it be ideal to put the ideal customer profile before the SWOT we—”

Christopher Penn 28:56
Did that. That was earlier on. The other thing that would be helpful to do in an “I—” in a situation if we were getting paid to do this, and we are not getting paid to this— Well, I mean technically it’s for our company, but if we’re getting paid to do this in a thoughtful manner that was not trying to cram it all inside of 45 minutes on a live stream, we would build an ideal customer profile for our competitor as well so that we could compare. Because let’s be—as much as I love us, let’s be honest—our customers are not McKinsey’s customers.

Katie Robbert 29:32
That’s—that’s fair. You know, for the sake of the example, McKinsey can stand in because you and I know enough about the kind of work and services that McKinsey offers, and they have enough similarities in terms of—they have perspectives on the topics that we care about. So again, yeah, you’re right. For the sake of the live stream, we’re using them as a stand-in.

I also just want to put out there, as much as we share the work that we’re doing for Trust Insights, how we approach it, we also protect our own IP, and we don’t share like “Here’s our real competitors, here’s our real customers.” McKinsey is big enough that I don’t think us taking a look at their publicly available content is really even going to get on their radar.

Christopher Penn 30:19
Exactly. So, let’s see the strategic plan. “A customer-centric approach based on our ideal customer profile and what we can do. Enhance customer experience and value: most achievable. Develop more targeted content and resources: in-depth case studies showcasing successful implementations of our solutions. Improve customer communication and support: more proactive consumer customer communication strategy. Comprehensive knowledge base, personalized onboarding.”

A lot of that we actually do. Again, we didn’t provide that information so the model does not know that strength.

“The data storytelling and visualization, leveraging this in community and thought leadership. Expanding our reach is a priority: to developing targeted marketing campaigns based on the ICP’s pain points, building strategic partnerships, public relations and media outreach and refining pricing and packaging, and then least achievable scaling operations, developing an internal pipeline, expanding our geographic reach, and building long-term brand recognition.”

Christopher Penn 31:15
So, that’s sort of what a model based on the data we’ve given, that thinks is our roadmap for the future.

Katie Robbert 31:22
It’s a good starting point. It’s still fairly high-level, pie-in-the-sky. And I would say— you know—we don’t necessarily have to do it on this show unless we have time, but the next step would be to give—start to give—the model some of our actual data, like our web analytics data, or our social media data, or email marketing data. Put limits on—”These are the channels that we operate on. These are the channels that we at this time either don’t have the resources to do anything with or have chosen not to do anything with.”

And so, you getting more specific with the data that you have and what you can and can’t do within the company is going to help you get a more tailored and more realistic action plan.

Christopher Penn 32:08
Exactly. So, one of the things you could do, for example, in Semrush, if you pay for the traffic analytics package, which is an add-on, and it’s not included in the base subscription, you can give a competitor’s domain, and then if we switch—swap from “Device” to “Year” here—am I in the right spot? Oh no. “Traffic Channels” down here, for this past month, this—let’s go “Last 6 Months”, there we go.

We can see the general channels where McKinsey is getting its website traffic, and then we could obviously pair that with our own, and say like, “Hey, they’re doing, you know, 150,000 visits in the last six months from paid search, we’re doing zero.” Right. We do we—so there’s clearly a gap there. And, then start digging into, okay, the traffic sources, where are they getting their traffic from?

Christopher Penn 33:04
This is very interesting. Look here just “ChatGPT”, 1.6% of that. So, part of understanding competitive analysis will be taking things like screenshots of this stuff and dumping it into the model and saying, “Hey, here’s some more things for you to pay attention to as to where you’re getting your traffic for them. 26—21%—of their traffic comes from Google.”

Katie Robbert 33:28
Yeah. And that’s where— you know—we started the conversation of “What do we mean by competitive analysis? What does that mean?” To us, and for us, it means how do we get that awareness? How do we get that market share of people having conversations about the topics that we want to be known for?

So therefore— you know—if we say our competitors are getting the majority of their traffic from search, then we would start to dig in deeper, going back to our original purpose, making sure we’re not losing sight of it and saying, “How do we improve our search so that we can compete?” And so, then you’re doing, now you’re digging into an SEO audit and analysis with your competitors.

Katie Robbert 34:13
And so, it starts to lead you down these very tactical things that you can do to make improvements where it matters the most. So, for us, organic search is always going to be a big channel for us. We know that email marketing is a big channel for us. We know that referrals is a big channel for us. And so, it’s a matter of prioritizing and saying “Where do we want to spend our energy?” And, I would say focusing on search is not a bad idea for us because it includes just making sure we’re keeping our SEO clean, that we’re writing about things that our ICP cares about, that we are having unique perspectives on the topics that our competitors are writing the most about.

Christopher Penn 34:56
Now, this is very interesting. If you look at the traffic journey for our domain, “substack”, which is 31% of our traffic, that’s email. Google is 27% of our traffic. YouTube, at least through from what Semrush can see, is 19% of our traffic and things. So that—and if you think about— you know—just a few minutes ago, when we were looking at McKinsey, these are very different channels. So, from a competitive analysis perspective, we have a very different marketing mix.

Katie Robbert 35:26
Which we know, given that our audiences aren’t exactly aligned, and our marketing tactics are not identical to theirs, completely makes sense. You know, you would wonder why a company as big as McKinsey isn’t showing up necessarily for YouTube, but also at the same time, given how much content they’re generating, and how much traffic they’re getting, YouTube might be on there. It just might be so much smaller than Direct and Google that it’s not—it’s just not showing.

Christopher Penn 36:01
Right. So, I’m going to say just—I’m just curious. “Now, refine your analysis based on these traffic breakdowns from Semrush over the past six months from these two screenshots.” So, let’s see what—if our—actually refine your SWOT analysis. So, I want it to go—I want to go back and have Gemini think this through based on what it can see in these two screenshots.

Katie Robbert 36:35
And so—but this is why we started with the five Ps, because— you know—it’s all really interesting information, but you can very quickly go down a rabbit hole of research and forget what the question was that you were trying to answer in the first place.

Christopher Penn 36:51
Exactly. I have fallen down those rabbit holes.

Katie Robbert 36:58
But, this is why having a clear purpose that every action you take can tie back to, helps keep you focused. It’s—it’s hard. I’m not going to pretend like “Oh, well, I define my purpose so everything’s going to fall in line.” It doesn’t, because it’s never a straight line from your purpose to your performance. You’re going to have— you know—these side quests, you’re going to have these detours. But you need to be able to say, “Okay, is this—am I getting too distracted? Can I get back to the original purpose?” And so, for this, you’re looking at their traffic sources, and we’re saying with this additional information, refine the SWOT. So, now we’re getting back to the original purpose of the competitive analysis, which is useful.

Christopher Penn 37:43
Exactly. And, the other thing you can do is, if you think through the five Ps, you can refine, even upfront, what frameworks you might want to use. So, while this is churning away here, I’m just going to bring up a stock version of Gemini. I’m going to go through the same exact prompting process we began with, which is just like “Hey, what do you know about this topic?” But, I want to follow up by asking it this set of questions. I want to follow by saying, “Make this a little smaller here so we can zoom in on it.”

Let’s think through these major frameworks, specifically think through the data requirements for each of them. Assume we’re the average boutique consultant with no access to private data.

Christopher Penn 38:29
We have tools like Semrush, Ahrefs, Brand24, and other digital tools that can look at the landscape broadly. We have access to public records like SEC filings, job openings, company blogs, news articles from the GDELT database, et cetera. Of these frameworks, then what framework should I even use based on the data?

So, the refined SWOT, by the way, is done. It says “Effective use of content marketing. So for us, the traffic data confirms effectiveness of Trust Insights’ content marketing strategy with a substantial portion coming from Google organic searches, results in YouTube.”

Then, weaknesses: “Enhanced shows a significant weakness in brand recognition. McKinsey receives 50% of its traffic directly in case strong brand awareness and recall, where Trust Insights receives only 6%.” Right?

So, underscores the importance of brand building over Trust Insights over-reliance on organic. There’s a—there’s a risk there.

Christopher Penn 39:25
“Lack of identified top destinations includes a potential weakness: converting website traffic.” That’s fine. That’s because Semrush can’t see that.

“Opportunities: capitalism. McKinsey’s lack of focus on organic search—” that’s really interesting. “Target McKinsey’s outbound referrals and then high direct traffic reinforces a significant threat of their brand.”

So, the key changes for our structure strategic plan: brand building becomes a higher priority. The traffic data underscores the urgent need for Trust Insights to invest in brand-building initiatives.

Katie Robbert 39:58
I mean, that’s pretty solid. That gives—if someone only had 40 minutes, and they had the information saying like “it’s me against McKinsey”, they followed the same steps. Like I could walk away and say this is a great action plan.

And so, to a question that was asked of—”Because we didn’t go through all five Ps, can you elaborate on ‘performance’?” It’s not the speed of the results that are obtained. And so, where is it the five Ps, so: Purpose, People, Process, Platform, Performance. In this framework, your performance is, “Did I answer the question being asked? Did I meet my goal?” If your purpose is efficiency, then yes, the speed would be part of your performance.

Katie Robbert 40:49
In this example, we said our purpose was to do a competitive analysis, a SWOT analysis, specifically with McKinsey as the stand-in for our competitor to understand how we can increase our awareness for our potential audience using our ideal customer profile.

So, the performance is now we—now that we have the action plan based on that analysis—we need to start taking those steps and measure: are we increasing our awareness? Are we making a dent in our brand awareness? Are we getting more direct traffic? Are we taking traffic away from McKinsey? The answer is probably not— you know—that’s—it’s a nice lofty goal but, given our resources, likely impossible.

But, if we then take McKinsey out of the equation and focus specifically on our metrics, and, are we increasing our own brand awareness? Are we bringing in more of the right customers?

Katie Robbert 41:46
Are we increasing search for the topics we care about, then yes, we’re doing the things, and we’re reaching our performance. Answering the initial purpose: What is the question being asked?

Christopher Penn 41:57
Yep. To go back to Gemini, it said “This is a fascinating challenge. Here’s each framework that the data suitability, the data sources,” et cetera.

And, the conclusion it reached at the end of looking at all these different frameworks, given your data constraints, “SWOT analysis and strategic group analysis are the most readily applicable frameworks.”

And so, as we’ve spent the last 40 minutes talking about, and we start with the SWOT analysis because we know from experience it fits readily with pretty much any data source. If you only had, for example, a tool like Talkwalker, one of our friends and one of our favorite tools, you could do social media only. You could say, “I want to do a SWOT analysis just on social media.”

Christopher Penn 42:39
If you’re like our friends over at B Squared, Brook sells the shop, and you had a lot of customer care data, you could refine that even further, and say, “I want to do a SWOT analysis just on customer care interactions on social media.” Maybe I go into a couple of subreddits, my company’s subreddit, my competitor’s company subreddit, and say, “Okay, how are they doing for customer care? Are they responding at all?” kind of things.

So, to your point earlier, Katie, SWOT analysis was very flexible across many different aspects from the biggest level of “How well is our company competing?” down to, “How good are we at this one thing versus someone else?”

Katie Robbert 43:15
And I would go—I like even a step further to say—if— you know—you’re sitting in a meeting and someone looks at you and says “I want you to do a competitive analysis, what does that mean?” You can go directly to generative AI, and use it as that sounding board and say “I’ve been asked to do a competitive analysis, where should I start?” It’s going to walk you through the process of questions that we’ve been answering on the show, and it’ll say, “What data do you have? Who are your competitors?”

It’s going to start to ask you, piece by piece, “Help me fill in the blanks so that I can help you get to whatever it is you’re trying to do.” You can say, “I don’t even know— you know—what the purpose of this is.”

Katie Robbert 44:03
And, it’s going to give you—it could be this. And so, it’s going to give— you know—a lot of suggestions, and you still have to do the guiding, you still have to say “Yes, I want this.”, “No, I want this.” But, it’s going to guide you through the process of putting one together, especially if you’ve never done it before.

Christopher Penn 44:23
If you do want to save yourself a lot of headache, I would suggest having the person asking fill out a user story to say— you know—”As the CEO, I want to understand our strategic positioning in brand against McKinsey so that I know how to invest my brand-building dollars in 2025.” Suddenly, that one statement really narrows down, “Okay, well, what specific data am I going to need?”

Katie Robbert 44:49
Yeah, I use a story as a simple three-part sentence: “Persona” being the audience, “want to” being the action, and “so that” being the outcome. And you’re absolutely right, Chris, that is a great way to position it to someone to say, “Help me understand what it is that you’re trying to get at.” You could even put together a few user stories if you know that the person that you’re working with is better at reacting to things and saying what they like and what they don’t like, versus coming up with it cold. And so, it’s a really great conversation tool to help get to the heart of the matter.

Christopher Penn 45:22
Exactly. So, that would be the place I would say to wrap up is to say, “Look, competitive analysis is a broad term. Use the five Ps, user stories. Use generative AI to figure out what are we doing? Give generative AI your list of—’Here’s the data that I can get.'” It might just be public data sources, but you might have industry reports. Maybe you buy— you know—a Forrester report or a Gartner report or a G2 Crowd report for competitors.

And, if you buy those reports and you have a license to use them, you can stick that in generative AI as well, and say like, “Okay, we want to specifically—” Which competitor should we even be paying attention to? Is a question we didn’t even tackle. We just named a competitor. But right. Choosing a competitor is a separate thing entirely.

Katie Robbert 46:17
And one of the follow-up questions was, “Would be great to get a recommendation as to when to follow up and then set follow-up schedule with the AI to see after the recommendation schedule to see if the recommendation made an impact.”

Yeah, it will— you know—here’s the thing. It depends on the purpose of doing the competitive analysis in the first place. And so, if your purpose is “I just want to see where we stand in the market”, that might be something that you don’t need to do more than once or twice a year because the work that you’re doing from the action plan is going to take some time to take effect. SEO is not an instantaneous fix. It takes a little bit of time for it to catch on.

Katie Robbert 46:56
See, the metrics change versus if you’re like “I’m suddenly going to start doing thought leadership”. You can start to measure very quickly, “Is it having any kind of impact on the brand?”

And so, it really depends on why you’re doing the competitive analysis. Probably a good place to start is at least once a year, but definitely every six months. Maybe run it— you know—at the beginning of your fiscal year, and at the middle of your fiscal year, just to do a gut check.

And then if you have very specific goals around competing with— you know—your peers and your competitors, it’s going to be more frequently. Once a week, too much. Once a month, depends on the goal. Once a quarter, probably a nice sweet spot.

Christopher Penn 47:43
Exactly. And shameless plug, it’s getting towards that time of year. So, if you have some analysis you want done at the end of this year, in the beginning of next year, obviously we do that, and we’ll do it in slightly longer than 45 minutes because we want to be thorough and take some time, but you have a sense of the process. So, that’s going to do it for this week’s show.

Thanks so much, and we will talk to you all next time.

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, the AI podcast, at—a weekly email newsletter—at TrustInsights.ai newsletter. Got questions about what you saw in today’s episode?

Christopher Penn 48:31
Join our free Analytics for Marketers Slack Group at Trust Insights, Analytics for Marketers. See 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.