In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for strategic planning. You will learn how to leverage AI to translate data into actionable insights, overcoming the common struggle of understanding what to *do* with your marketing data. You’ll discover how to use AI to identify the “why” behind your marketing results, going beyond simple “what’s working/what’s not” analyses. Finally, you will explore how to utilize AI even if you lack readily available data, uncovering creative ways to gather the information you need for effective strategic planning.
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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, it is now firmly Q4 we are, for many
folks, either winding down the remainder of the year, trying to squeeze
those last deals in because there’s something like 52 working days left
in the year. Or if you’re in B2C, you’re about to get into your crazy
season where you basically work 24 hours a day for the remainder of
the year. Either way, there’s a lot happening at this time of year, and
one of the things that people tend to do at the end of the year,
regardless of B2B or B2C, is try to do planning and strategy and
budgeting and get ready for the year ahead. I realize it’s not even
Halloween yet now we’re already talking about the year ahead.
Christopher S. Penn – 00:36
However, from a planning perspective, this is definitely when planning
season really kicks into high gear. So Katie, I want to ask you, as
someone who is a avid planner, how are you thinking about using
generative AI tools for planning season?
Katie Robbert – 01:01
I am planning the plan, no, so what I’m doing with generative AI is one
of the I said, I think I said this to you after a live stream, either last
week or a couple of weeks ago and we had done something. Of course
it’s very important because I can clearly remember what it was. I do
remember that we had done some kind of analysis that I remember I
said to you and John, this is much more actionable and useful than just
looking at a spreadsheet or a dashboard of Google Analytics data and
how many people came to our website. I understand what the data
means, but I’ve always struggled to figure out what the heck to do with
it.
Katie Robbert – 01:52
I personally am going to be taking those usual analyses and dashboards
that we typically look at and actually use generative AI to help me
figure out the so what. And so I will be using our ideal customer profile,
which we use generative AI to build, using that as the foundation and
say, this is our ideal customer profile. If you’re not familiar, the ideal
customer profile service that we built uses a very straightforward
process where it takes a look at a minimal amount of data, but it’s really
valuable data that tells you a lot about who you want to be targeting. If
you’re interested, just give us a shout trustinsights AI contact and we
can talk you through different options.
Katie Robbert – 02:44
So I will be using our ideal customer profile as the foundation and then
I will be giving it some additional information about here’s where we
want our services to go. Here’s our limitations in terms of marketing
our budget, our resources, here’s what we can and can’t do. And then
I’m going to give it our data and say, this is our website data. Or
actually, I shouldn’t say that. I’ll be giving it our funnel data. So here’s
our awareness data, because it’s going to be more than, like, one data
set. Here’s our awareness data. Here’s our engagement data, here’s our
purchase data. If we know that our awareness, our top of the funnel is
where we tend to get stuck, how do we, knowing our limitations and
knowing who our ideal customer profile is, what do we need to do to get
more?
Katie Robbert – 03:31
And the awareness step of the funnel, that’s how I plan to use
generative AI. I feel like it’s just a really good assistant. It’s not doing
the work for me because I still have to pull all the stuff. I have to pull all
the data. I have to put the plan together. I have to give it all the
prompts. So it’s, but it’s helping me get organized, and it’s helping me
think through, well, this is what I’m seeing, and I think it’s just sort of,
for me, I’m going to use it as that second set of eyes.
Christopher S. Penn – 04:00
No, that’s a very good way of doing that. When you, when you look at all
the data, how much are you having AI do the math? And how much of
the math are you doing yourself? Like, are you going to provide it all the
raw datasets, or you just going to take screenshots, for example, funnel
analysis by stage, and say, here’s like, we know this right here. This is
the part where we fall down between this stage and this stage so that
it’s not having to do something that it’s not particularly good at.
Katie Robbert – 04:27
So it’s likely going to be that. Thankfully, we’ve been collecting our own
data since day one. I have all of our data collection spreadsheets and
sources set up such that I can just take that information, and it’s
organized in such a way that it’s okay. This is our awareness data. This
is our engagement data. And we’ve been doing this long enough that we
know that it’s the top of the funnel that tends to be the weakest for us.
When someone is in the engagement phase of their journey with us, we
know exactly what’s going to happen. It’s getting people in the door,
getting the right people in the door. And I think that’s where I want to
use generative AI to help focus to say, this is what’s happening today.
This is who we want to bring in. What’s that middle?
Katie Robbert – 05:21
How do we bring these two pieces together?
Christopher S. Penn – 05:25
And will you be using just the actuals in terms of the inquiries that
people have submitted, or will you also be using the synthetic Personas
derived from the ISP to sort of talk through it with them, to say like,
hey, I know you’re all prospects. You’ve all signed up for the Trust
Insights newsletter. You all watch the live streams of what’s stopping
you from raising your hand and saying, I would like to buy something.
Katie Robbert – 05:53
I think that’s exactly what I and others who are trying to figure out how
to use generative AI should be doing. Because if it’s just telling me,
okay, you’re doing email marketing, do more of that. That’s not really
helping me understand why people are not taking action. So my focus,
and this is where I feel like generative AI can help, you know, reverse
your thinking. We often look at what am I, what’s working? What do I
need to do more of? And then we also look at, it’s very black and white,
what’s working? What do we do more of, what’s not working? What do
we do less of? But we don’t really get into the why. And so now with
these synthetic AI agents, we can actually ask that question.
Katie Robbert – 06:41
Now, again, these agents are only as good as the information you give
them, and so they’re only as good as the ideal customer profile or
whatever customer data you have. But I feel pretty confident about our
ideal customer profile. And so when I give it to an AI agent and I say, so,
we’re doing all the things you want us to do and you’re still not buying
anything. Why not? That is the golden question. That’s exactly the kind
of questions we should be asking.
Christopher S. Penn – 07:13
How much, if any, third party data, quality of data are you going to plan
on bringing in? So, for example, would you bring in, say, the last 90
days worth of Reddit posts from the CEO forum or the marketing forum
to try and get a sense of the quality of language that our ICP may or
may not use? But we could, but you could use gender bias to say, like,
okay, which of these posts is likely from someone in our target
audience? Which one is clearly someone who’s an intern or whatever?
And then of the ones that are the likely from someone similar to our ICP,
how did they talk about these problems?
Katie Robbert – 07:52
I think it would be silly not to. The ICP as great as it is still has its own
limitations. And so the more data that you can provide as context, as
background, as foundational about who it is you’re trying to reach with
your services, with your products, the more accurate the
recommendations and insights are going to be. So if you have three
customers and you give that to generative AI and say, I want more of
this customer, you’re going to get very limited results. It’ll give you
some information. It’ll give you a starting place. It’ll probably say, you
didn’t give me enough to work with. Here are some suggestions of
where you can get more data.
Katie Robbert – 08:35
But if you have a lot of that data, if you have especially not just the
quantitative, but that qualitative data, it’s going to really be able to dial
in what kinds of things you should be doing. And so when I say what
kinds of things, I’m really thinking about getting a better understanding
of those pain points because everybody wants to know, how can you
solve my problem right now? This is what I’m experiencing. This is what
I’m feeling. How do you solve that problem for me? And that’s what we
as marketers need to be thinking about. We as product managers, need
to be thinking about. It’s not what we think you want, it’s what they’re
telling us they need.
Christopher S. Penn – 09:22
So suppose you are a similar CEO, but you don’t have this data, because
everything you’ve talked about sounds awesome. It sounds great. And I
know based on our capabilities and our internal processes, it’s
achievable because we know where the data is. We know how to get at
it. We know how to format it and properly feed it to generative AI to
make it work. What if in an alternate reality, we don’t have that? We’re
like, okay, well, I’m the CEO. I’ve got a great company. We do good
work. But our data is everywhere and nowhere. We don’t know where it
is. We don’t know if it’s in any good at all. Maybe the last four months
have just been a total crap show. Our web analytics tracking got
uninstalled by accident by some lunatic developer. Our CRM is broken.
We have none of this.
Christopher S. Penn – 10:17
What does that CEO do? Because something that you’ve said a lot is
that people think of generative AI as a magic wand, and it is clearly not
a magic word. It’s a prediction engine. And to predict, well, you got to
have good inputs. So what does the alternate reality, Katie, do with no
data?
Katie Robbert – 10:37
You know, I was originally going to make a joke and be like, oh, she
panics. But that’s actually not helpful because if you think about it,
Chris, we started with no data. When we started trust insights almost
seven years ago on day one, we sat there and said, okay, what do we
do? We don’t have data. So you have to look at what third party data is
available, and there’s a lot of it. So when you’re talking about your ideal
customer profile, for example, if you don’t currently have customers but
you have an idea of who you think your customer should be, start
looking on LinkedIn. You can download somebody’s resume as a PDF
and say, okay, this is the kind of person that I want as a customer, or
this is the kind of company that I want.
Katie Robbert – 11:27
Even if it’s not an exact match, it starts to give you some information.
Think about who you feel like you might be competing with. Who are my
competitors? And if you say, well, we don’t have any competitors, then
you’re probably not thinking about it the right way. So dig a little bit
deeper. Who are my competitors? And so you can use, again, you can
pull their data from LinkedIn. You can use other third party tools like an
SEO tool or a social media listening tool just to start to get a sense of
their customers. And so if their customers are who you want as your
customers, why wouldn’t you be looking at your competitors data again?
Chris, you mentioned Reddit. I think the Reddit forums are certainly an
untapped, well, resource in marketing because I think Reddit tends to
scare people.
Katie Robbert – 12:25
But it’s so much qualitative data. And now with generative AI, analyzing
qualitative data has become a heck of a lot easier. You can also look at
industry trend papers and research papers, and you can look at Google
Trends and search console and other things. There’s a lot of different
resources that you can look at that you might not have initially be like,
oh, well, our CRM is broken, so we can’t do anything. But, yeah, you
really have to think through, like, well, where else would I get if I had
nothing? Where could I start?
Christopher S. Penn – 13:09
Yep, to your point, search is a great place to go look because people
search for things generally because they have a reason to do so. They
saying, like, how do I do this? Where do I do this? Looking at YouTube
data, one of the things that we just did for a customer that worked out
really well is we downloaded the top 40 YouTube videos about this
customer’s topic. Because what we wanted to say to the customer was,
here’s the media diet. Here’s what people are being told about your
topic. So if you know this, then you also know what maybe
misconceptions or oversimplifications or things like that people are
being told. And that gives you a content marketing opportunity to say,
well, actually, here’s how this topic really works. What so and so
influencers said on YouTube or TikTok or Instagram is wrong.
Christopher S. Penn – 14:01
And here’s why.
Katie Robbert – 14:03
Yeah, I. I think we tend to, when we think of planning, we tend to get
very stuck in, well, I have to look at my web analytics data, I have to
look at my channel data, and that’s it. And that’s going to tell me my
marketing plan. But now I can understand. I worked in market research
when I was a product manager and I can understand why looking at
qualitative data is so intimidating. Because it’s unstructured, it can be
unwieldy depending on how you’ve collected it. If it’s not in a survey
form, or even if it is, if you’re giving people open text boxes to just
respond, it’s really up for debate as to what the information means.
Katie Robbert – 14:47
And so pulling all of that information together and to make sense of it,
yes, there are tools out there that do that, but they’re really expensive
and they’re not accessible to a lot of companies, or they’re just not
aware that’s how you would handle that kind of data. So back to your
original question of planning. Chris. Now, that generative AI should be a
standard piece of software in your toolbox, in your toolkit, in your tech
stack, using qualitative data, trying not to mess up the two of them.
Now using basically people’s feedback, that unstructured
conversational data should absolutely be a part of your planning.
Katie Robbert – 15:32
And so we should be looking at our own podcast and livestream and
other video feedback on our talks, feedback on, you know, when people
write in and give us feedback on the newsletter, anything people have
responded, our testimonials, all of that information should go into the
planning process because now we have a more efficient way of handling
that information.
Christopher S. Penn – 15:57
And to your point, if you don’t have that data, do your competitors have
a YouTube channel? Do your competitors have a TikTok channel?
Etcetera? Do they get comments? Do they get engagement? And if so,
what is it even something as low tech as just copy pasting your
competitors top ten videos? This is not an hour long chore. This is find
the top ten videos your competitor by views. Copy and paste the
comments. If there are any. If there aren’t any, then that tells you
something, too. That tells you that they may have a passive audience,
but they do not have an actively engaged audience, at least on that
channel, which means that channel might or might not be the right
channel for you.
Katie Robbert – 16:35
Mm. Well, and if you think about it, too, you have, if you have those
different kinds of content, the, you know, you have your I written blogs,
you have your audio files of a podcast, you have the videos. AI tools can
easily handle turning those into transcripts and text files and using that
information. So again, if you think about, if you go back to that example,
Chris, of what, if your whole martech stack blew up and you have
nothing, then you can start to look at, okay, well, what are my
competitors doing? What are they writing about? What words are they
using? You know, what topics are they getting the most engagement for
when they talk about this in their video, what happens?
Katie Robbert – 17:18
I always go back to, and this is a little bit tangential, but I always go
back to my very first marketing profs b two b forum, where Avinash was
the keynote speaker. So, Avinash of Google, and he was talking about
Google Analytics and he was talking about engagement. And this always
stuck with me because of the way that it was presented. But he’s like, if
you look at your Facebook page and you have a million followers, but
only one person ever interacts with your content, you’re doing it wrong,
because that doesn’t mean that you have an engaged audience. It just
means that you have a bunch of people that followed you and are either
passively looking at your stuff or ignoring it altogether.
Katie Robbert – 18:05
And I think that taking a look at those metrics when you’re looking at
your competitors is going to be really key because they could have a
million followers. But if you’re just looking at in terms of what topics are
they producing, then you’re missing the point, because you want to
know what topics are they producing that people actually do something
with?
Christopher S. Penn – 18:26
And one of the easiest things you can do from a competitive standpoint
is that the gender of AI is superbly good at. You have to do the math
part, so you have to get a corpus of their posts and whatever public
metric views, et cetera, engagements on it. And then you slice off the
top ten, you slice off the bottom ten and you say, here’s the top ten post
my competitor, here’s the bottom ten post my competitor scored by
whatever number of views. What do they have in common and what
separates the top from the bottom? And when you ask generative AI
that it doesn’t really, depending on the model, it does a really good job
of inferring like, okay, these are the things that your competitor, this is
why it resonates. Now, you will have to fact check that.
Christopher S. Penn – 19:06
But it is phenomenal at picking up those hidden patterns, particularly in
qualitative data that we can’t see. If you were to take the top ten
YouTube videos, for example, of a competitor, and grab the closed
captions files for them, which you can do for free, you can analyze those
videos in depth and say, here’s not just the topics, but here’s even how
they talk about it and what makes it powerful and compelling or what
doesn’t.
Katie Robbert – 19:33
And all of this goes back to knowing who you want to target. So you
could say, oh, I think McKinsey is my competitor. Great. Well, who are
they targeting? Who is their audience? Just because you look up to and
admire a large firm like that doesn’t mean that they’re necessarily going
to align with who you want to be targeting. And so it goes back to,
again, even if your tech stack blew up and you have nothing to work
with, you can start to sketch out and put together. Here’s who my ideal
customer profile is. Here’s roughly the information about them. Here’s
who I think, here’s where they work, here’s how much money that
company brings in. Here’s what I think their pain points are.
Katie Robbert – 20:20
Here’s think about from their perspective, not what you solve for them,
but just solely focus it on them and then start doing all of this data
analysis, data investigation and exploration. But use that ideal customer
profile as.
Christopher S. Penn – 20:38
Your anchor, maybe not today, but maybe on a future live stream. We
should do a competitive analysis exercise, maybe for this podcast, and
say, let’s take some of the other podcasts in the space, the ones that are
a gazillion and a half viewers or whatever, and run through this.
Because I think it would be interesting to see what we’re doing, what
we’re not doing, what they are doing, what they’re not doing, et cetera,
and feed that all through something like Gemini 1.5 and get a sense of,
okay, these are the things that we could do to make our podcast more
effective.
Katie Robbert – 21:17
And I think that it’s now with how accessible generative AI has become.
Even if you’re just using in a very basic way, there’s really no reason not
to be thinking about doing this kind of analysis. You don’t have to go
super in depth, you don’t have to build a scoring rubric you don’t have
to have pages upon pages of prompts, but you can absolutely use
gender divide to start to look at. Okay, here’s the last five videos we did.
Here’s our ideal customer profile. Why are they aligned or why not?
And just, you can keep it that simple. Like, here’s the last, here’s the
one single blog post I have written all year. Here’s my ideal customer
profile. Why didn’t my customers do anything with this content? Why
didn’t this content bring anybody to my contact form?
Katie Robbert – 22:15
It’s very simple, very straightforward, but it’s going to tell you a lot.
Christopher S. Penn – 22:19
Exactly. Exactly. So stay tuned, I guess, for that upcoming livestream
where we will talk, we’ll walk through doing maybe a podcast
competitive analysis. We could do one for us, we could do one for
marketing over coffee and see what the podcast landscape looks like for
us and how we might be able to compete on it. But I would encourage
folks, get to know your tech stack, get to know where your data is and
get to know the different AI tools and what they can and can’t do when
it comes to strategic analysis, because they are properly prompted,
properly fed with the right data. They are world class consultants. They
are incredibly good at what they do. Again, like a real consulting firm,
as long as you give them the right materials.
Katie Robbert – 23:03
Yeah. And it really, so, you know, what we haven’t really touched upon,
but just a good reminder is this all starts with really good data
governance, period.
Christopher S. Penn – 23:16
No one wants to hear that, Katie. Let’s say we have to eat our
vegetables and exercise.
Katie Robbert – 23:21
I know there’s no way, you know, it’s, I saw this really funny video that
sort of relates back to this and it was like, it was these like fitness
instructors doing the most bizarre things. And it was like people will do
anything except actually work out and e write. And they were like, on
these like, you know, jungle gyms doing like weird things and whatever.
And it’s like they will literally do anything except what they know
they’re supposed to do. And I feel like the same is true here, where it’s
like people will literally try to do anything except have good data
governance. And if I ever figure out why I think I’m going to be a
bazillionaire, I’m working on it.
Christopher S. Penn – 24:06
Highly likely if you are using generative AI to do your strategic planning
or to help with your strategic planning and you want to talk about your
experiences, or maybe you’re trying to do it and it’s not going so well,
pop by our free slack group, go to trustinsights AI analytics four
marketers where you and over 3500 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’d rather have it
on. Instead, go to trustinsights AI tipodcast. You can find us in most
places where 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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