In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how you will overcome creative blocks while navigating information overload and create new content ideas. You’ll discover why AI keeps offering generic advice and how you can bypass those predictable suggestions. You’ll learn to pull unconventional strategies from unrelated fields and apply them to your marketing plans. You’ll identify two distinct thinking patterns that unlock fresh content ideas when your usual topics feel stale.
00:00 – Introduction
03:15 – The challenge of information overload
07:40 – Why AI defaults to common advice
12:20 – Deductive versus inductive thinking
16:50 – Finding psychological safety for exploration
21:10 – Cross-pollinating ideas from other industries
26:35 – A practical research workflow
31:00 – Call to action
Hit play to uncover how shifting your perspective transforms stale ideas into bold marketing breakthroughs.
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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:
In this week’s In Ear Insights. This is the age of AI, where there is more information than ever, more data than ever, more everything than ever before. And yet behind the scenes, when we were prepping for this episode today, Katie, I said, hey, what would you like to talk about today? And your response was, I have no idea. Let me ask you a couple of different questions. One, is it that the material recovering feels like it’s been done before, or is it also that there’s just so much to choose from that you end up with what Barry Schwartz calls the paralysis of choice?
Katie Robbert:
Yes. So let me pull those apart. So much is changing in the field of AI and where we focus down in on with analytics, marketing, operations, and helping businesses. We do such a good job of thoroughly exploring topics, and the process that we have for creating content is so efficient that we can churn out a lot of it very quickly. So I feel like personally, the things that we would cover that are relevant to our business and relevant to our audience now there’s always another stone to turn over. But then you start getting really deep into the weeds. And where I wrestle is do you start alienating parts of your audience to really focus on those very specific niche pieces? To the other part of the question? Yes, I do feel like there’s so much information. Again, this is sort of the side effect of generative AI that you sort of become numb and blind to all of the information because there’s so much. I remember when we worked at the agency and you used to produce this report, I think it was yearly, on the number of pieces of content created. And you know, this was prior to generative AI, this was just the number would go up exponentially year over year. And you know, I can’t even imagine what that number would be now. And just sort of seeing the uptick. And so I don’t, I feel like I’m deep in the weeds of stuff that I’m working on day to day in the business. Part of it is I’m like, I don’t know if anybody would even care to hear about this, if it even resonates or if it makes sense or if we’re supposed to be doing that big thinking and like introducing new concepts and you know, doing the thought leadership of what’s happening with AI and analytics. Here’s the thing about analytics. There’s only so many ways. There’s a lot of ways to analyze data, but there’s only so many ways to analyze data. And so I feel like at this point, unless a brand new analysis methodology is invented, which I could happen, but I feel like we’re pretty good, we’re solid, you know, it’s not worth talking about again. But I could be wrong. I could just be having a very blah Monday morning where I’m like very feeling very existential of like, what’s the point of any of it?
Christopher S. Penn:
Which, given the macro picture, which is a totally separate podcast, is true. In the catalog of analytics that we use, that I use, there’s about 1400 techniques that span 22 disciplines, everything from weather forecasting to agronomy and agriculture and stuff like that. And one of the things that I was talking about this weekend in my own personal newsletter was how limited marketers are in their choice of analytics when you use AI, because AI naturally gravitates to what we’ve already written about. So we’ll talk about attribution analysis, we’ll talk about social media marketing, we’ll talk about impressions. Our advisor, Ginny Dietrich’s most hated measurement of all ad value equivalents. And these are all associated statistically, probabilistically in these AI tools with marketing analytics. So if you say, hey ChatGPT, I need some marketing analytics help with this, you know, Google Analytics data source, it will give you the most probable things which will be old hat. Like, it will be like, oh, have you tried doing, you know, basic trend analysis? Like, yes. Have you tried doing time series analysis? Yes. Right. And that is a blind spot that AI can never overcome because of its nature as a probability based tool. You, the human, have to have the largeness of vision and the vocabulary to say, hey, you know, chat GPT or co pilot or whatever. I’ve got this issue here and I’m having trouble understanding what’s driving the set of changes. What analytics techniques from agriculture and agronomy might be a good fit for this data and suddenly be like, I know all about agronomy, which it does. And have you tried pairwise combination holdout? And you’re like, what is even? I don’t even know what those words mean.
Katie Robbert:
Nope. And I hear what you’re saying and I feel like we’ve talked about versions of this where in order to get the most out of the tools you have, you really have to be curious and thinking outside of the box and that’s something, Chris, that you do very well. That’s one of your strongest professional attributes is you say, where can I look outside of this lens and see like and pull out like agriculture. So I don’t think that’s the default thinking for most. Take marketing out of it. For most people, it’s like I’m looking at a problem. I’m trying to figure out why my bread dough isn’t rising. Let me look at the fashion industry and see what I can learn from that. About, like, it doesn’t make sense because it feels like putting square pegs in round holes and I don’t know, maybe that’s sort of where I’m stuck, is maybe I’m not someone who’s good at looking outside of. Like, I’m a very rules and process oriented person. I like things to be predictable. So perhaps for me, that’s where I struggle with this concept of I’m trying to analyze my marketing data. How does agriculture do it? Like that to me is just like, why would I ever do that? That seems ridiculous to me. That’s not how this rule works. And so that’s definitely not one of my strengths. Which again is why you and I are very complimentary in our strengths and weaknesses. And you know, I just went. When we come back to the purpose of today’s podcast, I feel like, you know, maybe that’s where thinking about topics to talk about is where I’m struggling because I feel like I’m a fairly creative person. But I also, I remember I started taking art classes in first grade and went all the way through 10th grade of high school. And I, my skill set really didn’t progress. It really wasn’t something that, like, it was something I wanted to be good at. But I just, I’m not an overly creative person. I’m creative when it comes to problem solving. But I feel like I also have a really Good toolbox. But if you start to say, okay, your toolbox is now going to contain these things that belong in, you know, biochemistry and welding, those are not industries I know a lot about. I’m like, nope, I don’t want that in my toolbox because I can’t, I don’t understand it. I don’t get it. I don’t know. There’s a lot of like therapy for me this morning.
Christopher S. Penn:
Well, what you’re describing is the difference between inductive and deductive reasoning. So as a rules based person, you would be a deductive reasoning person. You have a set of rules and principles and concepts in a domain that you break down to specific conclusions. If we think about the 5P framework by Trust Insights is exactly that. Five different areas, purpose, people, process, platform, performance that have governing principles for each of these. And you could take that general thing and distill it down for any problem that you run into, right? And say, okay, how can I make this. We’ve you’ve talked about even making it a prompt framework for AI because it’s so good at it. The opposite is called inductive reasoning where you say, I got a bunch of stuff, it’s all very confusing, it’s all not related. What is in common? And so for example, if you look at agronomy and weather forecasting and econometrics and marketing, what is the through line, the weird thread that goes through all of them for marketing analytics, and that is math, right? The mathematical techniques that each discipline has to use that are different from discipline to get to the answers. And so inductive reasoning says, can we find those threads from these crazily not related fields, hold on those threads and turn them into something like, oh, we can use this in marketing. We have data that resembles this. How do we adapt it? And so that’s the difference. And so when we look at like content creation, saying what are we going to talk about today? If we start from that deductive reasoning perspective of, well, we’ve done AI 18 ways to Sunday. We’ve done prompting, you know, 22 ways to Sunday. Well, they’re left to do that is a deductive perspective. And there’s, that is a great way to do it because you cover everything. The inductive way is okay, say okay, this is what you flip to when you’re like, we’ve done it all, what’s left? Okay, now you flip to inductive reasoning. Say what about this weird edge case over here? Let’s pull that apart and see where it leads.
Katie Robbert:
Well, I mean, and this aligns with what we always joke about, that you know, what will be written on your tombstone is what does this button do? You know, and there’s a part of me that wishes I was a little bit more open, more curious, you know, I feel like I am, but at the same time, not nearly as much as I could be in terms of, you know, in this context of like breaking out of what I know. And so what kinds of advice would you give to someone like me who, to your point, like, if I do the deductive reasoning and I cover all the different known angles, you know, and I think about things from a risk averse lens, what advice would you give to someone like me to then find ways to turn on some of that inductive reasoning when I need it?
Christopher S. Penn:
You can generally teach inductive reasoning in a couple ways. The one of the things, and this is very important, what you just said, you said from that risk averse lens, that’s the first switch that has to flip is to say, okay, you can do exploration if you can cycle, give yourself a sense of psychological safety that you’re not taking a risk. Like going out and reading random stuff on Reddit and things or people you don’t Normally follow on LinkedIn is not a risk. It can feel like a risk because it can feel like distraction, it can feel like confusion, but it’s not a risk. And so, one of the things you have to find for yourself, I don’t know how to do it, is to say, okay, let’s create a sense of psychological safety that says it’s okay to go look under this, under the stone. You may not like what’s under there, but it’s not dangerous to you. And the second thing is those fringe edge cases where, and this is where, like, LinkedIn is a terrible place to spend time because everyone is stuck in the same bucket, right? We’re all talking about, oh, AI is going to do this. Sam Altman did this. And it’s the same theme over and over again. You have to leave that and go to the fringes, go other places. Reddit is a good place because there’s, it’s a different set of fringes. Talking to our friends and colleagues, right? Chatting with them in group texts and stuff is a great place to do that, where you start to hear very specific edge cases. You’re like, oh, we’ve never run into that before one of the things. So it was really interesting. I did a drawing for my newsletter for filling out a survey back in July. And the giveaway was a one hour. Ask me anything private. And the conversation I had with the person was an industry that we don’t work in at all, like heavy industrial marine stuff. And just. And I learned as much as I talked, like, oh, I didn’t know that was a thing. And those, all those weird little edge cases was like, that’s interesting. And so that might be a possible antidote too, is to say, like, you know what spend. Say, I’m going to spend an hour talking to this person over here. I. Who we’re probably not going to do business with them. We’re pro. I’m. I’m not there to drum up business. I’m there to say, what kind of weird stuff are you running into that maybe wouldn’t cross my desk.
Katie Robbert:
H. I hear you. I think, you know, I like the idea of, you know, doing more reading outside of what you’d normally read. I agree that LinkedIn is very homogenous, very bland. The challenge. And, you know, this could be again, my own personal bias and my, you know, I like the term that emotional safety. Because if I’m being honest, that’s a big part of it is it’s not that I don’t feel safe talking about things outside of what we’ve talked about. And perhaps this, you know, this is all just sort of like a, you know, a horse of a different color is that I get concerned that if we stray too far from what our audience wants to hear about, they won’t come back. And that’s, you know, that could be my own personal concern and anxiety about it. And maybe they’re like, no, give me something different. You know, there’s really only one way to find out and that’s to ask them. So if you want to pop over to our free Slack community analytics marketers and let us know. But to your point, you really. What I’m hearing you say is it’s breaking outside of those comfort zones, those routines, which admittedly is not something I am personally great at. I’m very introverted. But reading outside of what I normally read and interacting with people online is not so uncomfortable that I can’t do it. I just need to make more of an effort to do it because then it’s, you know, it’s making sure that we’re staying in touch with things outside of our own little bubble. It’s a big reason why we have the community that we have. So know a little bit of tongue in cheek like join our free Slack community but at the same time join our free Slack community because we need it just as much as the people who are joining need it. So that we’re not just locked into well this is what we’re doing. This is how we’ve always done it. So this is how it shall be. End of sentence. Like I think that by asking the question of the day it that’s partly to understand like what’s going on in your world, what are you dealing with, what are you, what’s your perspective? And I’m always not. A question goes by and we ask them every single day that I’m not like huh, that’s interesting or I never would have thought of it that way. And I think that’s such an important thing. But I never thought of that in this context. Chris of that inductive reasoning of trying to find that sort of like through line through different topics.
Christopher S. Penn:
I will show, I will share with you an interesting observation about our analytics from Market Slack group. There’s a, there’s a topical split by gender. When you look at the questions people ask, even that itself is interesting. You know the, the people who are male identifying tend to be asking very technical questions. Hey, I’m doing this with this agent framework and you know, what’s the best? And the people who identify as female typically asking about outcome based things like you know, what’s better for getting this done. I need to get, accomplish these things. And so one of the things because obviously, you know, equality in general is important to us, but gender equality is very important to us as a company. You might want us to do privately, quietly reach out to some of the individual women in our community to say like hey, I’d love to have a one hour private, ask many things, I just want to know what’s going on. Talk to the folks who are already reasonably talkative in the community and just see what’s happening at the edge cases of their Worlds. You have Dr. Leslie who spends a lot of time on psychology and research. You have Hannah, you have Minion who spends a lot of time on writing in AI. And that’s where you can be reasonably assured of a psychologically safe experience and still get some really cool edge cases.
Katie Robbert:
Yeah, I think if I’m being honest, I would probably start with Reddit forums or something like that just because that’s for me the path of least resistance and doesn’t take extra coordination and time to figure out how to, you know, book this conversation with someone. But again, that’s all, you know, minor details, but. In general, you know, so let’s say every week we sit down to record the podcast. And every week I know you’re going to ask me, hey, what do you want to talk about this week? I mean, that’s just how it goes, you know. And I appreciate that you give me the opportunity to propose a topic versus it just being, you know, led by whatever you’re reading that week, which often happens because I’m like, whatever, sometimes I have a topic. You know, tell me a little bit about your process for like you mentioned agriculture and some other industry that I’ve never heard of. And I’m guessing I haven’t had a chance to read your newsletter yet for full disclosure. No, I mean, and I do. It’s on my list for later today. Is it more that like you look at like a list of something, you go, I don’t know about that. That’s interesting. Let me learn more. Or is like does it sort of organically just come up because you’re like searching for something and it just sort of leads you down a rabbit hole? Like what does your process kind of look like?
Christopher S. Penn:
It’s both. It is both. This past week though was on idea transfer. So I ha. I shared some prompts, some basic prompts to look at four level naics codes which are like profession codes, oil and gas extraction for example. And I said to the soft to I was using deep seek for this. Go and find the professional academic conferences for each of these professions out of the 340 professions and then find the award winning papers from 2025 and 2026 for each conference if they had one, and end up with like 300 some odd papers that all won gold medals at their events. And so download those papers and then start extracting out what are the key lessons that we could transfer into marketing and marketing analytics, especially from these award winning papers. And some of them there wasn’t, you know, some of them it was like a position paper. But there were a lot of very technical research studies like oh, that’s really cool. Like how the European. I forget what the organization name is, but it’s the European Council of Weather Forecasting and their new neural ensembling system for better weather forecasts. I’m like, that’s clever. Instead of trying to fit a neural model on an overall forecast to fitting neural models on individual components of forecast because they can forecast better those specific components and then glue the forecast together instead of trying to ensemble the whole thing. It’s like, you know, instead of trying to. People screw this up a lot. Westerners tend to try and make fried rice all in one big wok and your home stove can’t get that hot. So you cook each set of ingredients individually and then you mix it all together. That’s this, essentially this version of ensembling. Don’t try to make fried rice all at once. Make each of the components, get them each right individually and then you can just literally mix it all together.
Katie Robbert:
Well, at least I know I’m making fried rice correctly. Thank goodness for that. I’m taking the win today. But that’s an example. So the topic was how would you make it a. A fully AI written newsletter with no human intervention whatsoever that would still be valuable. And so I had it to a test run and the example was that pairwise combinations for agriculture. I read the newsletter of IT generator. Like crap. This is a really good idea. Like I gotta go figure out how to do this. I was reading the results as I was recording my, the newsletter on video. I’m like, this is really smart. Like it should be. It won an award. But like I can totally see how this applies specifically to social media marketing.
Christopher S. Penn:
Interesting. Which comes back to you have to have expertise in something in order to be able to, you know, apply it. So you know, I know what I know about running a business. I know what I know about operations and process and all that. So it’d be interesting to see what I can find outside of the lanes that I know, you know, because it tends to feel like, well, process is process. There’s only one way to do process. And I’m assuming that’s just not true. And so I need to look at what that could mean. I have no idea. And I think this is where my risk averse brain starts to kick in. I’m like, well if you have no idea where you’re going, you don’t have the time to waste to just sort of start exploring. You have other things that you have to do to keep the business running. And I think that’s just sort of a time and resources but also a mind shift thing. Because yeah, I have to keep the business running. Do I have the luxury of time to just sort of do some exploration? I don’t know. I’m hoping to find some because I feel like that would make me a better leader if I could.
Katie Robbert:
And you’re like, yeah, you could be A better leader. Way to go.
Christopher S. Penn:
Everybody has the opportunity to better. You could, if you wanted to and you didn’t want to spend a ton of time on it directly, you could purchase. Well, you know, you don’t have to purchase it, but yeah, the new deep research suite from the Trust Insights Academy, which helps automate a lot of the research process. And where I would suggest that you start is in the epidemiology around opiates and stuff like that. Because you used to work in that field. You know it like the back of hand. But it’s been 20 years. Yeah. What’s, what’s happened? What papers have come out in that field in the last even five years that, where the math has changed, where AI has changed that field. And you can go, oh, I know how we used to do it and I know how AI does it now based on these award winning papers. Holy crap, things are different. And I can take this, what I know from back then and what is steady state now, current state now, and go. I can apply that learning of how AI has changed that field to what, running our business.
Katie Robbert:
Yeah, that’s interesting. That actually already gives me a couple of places to start because I think about a few of the clinical trials that were running and they were so manual because it just was, it was just manual. That’s how it was 20 years ago. And so like hand coding things and human raiders and all of that, which at the time was fine. We didn’t know any different because there wasn’t anything different. This morning we had a lead file of 150 leads that needed augmentation and I had an AI go in and label it all and it took under 20 minutes. And I ran it locally. It didn’t have to use the cloud at all for it. And so that’s today. Yeah. It reminds me of when I was trying to do lead gen for the sales team at that same organization and I stumbled across a publicly available database of substance abuse clinicians and it needed cleaning up and it needed like all this stuff and it was just a manual process and it took me a couple of weeks to do it before I could even hand it off to the sales team. And then those ungrateful jerks were like, what do you mean there’s not an email address? And I was like, you got a phone number? Pick up the phone. What do you want from me? But anyway, yeah, it is definitely interesting. So I think I don’t do that often enough. I really try to stay grounded in like what’s right in front of me. And I think that maybe looking in the past of like what I had done, what I have accomplished might help at least get some of this started.
Christopher S. Penn:
Exactly. There’s a lot of. There’s a lot that’s changed. But you having that previous subject matter of expertise means that it will be easier to dip your toes in that specific angle on that discipline rather than going to like agronomy where you’re like, I don’t even know what any of these words mean because like what is this? This yield, you know, yield per field thing. If you’ve got some thoughts about how you are approaching deductive and inductive reasoning concepts for content creation or ideation, pop on buyer free slack. Go to TrustInsights AI analytics for marketers where you and over 47100 other marketers are asking and answering each other’s questions every single day. And if there’s a channel you’d rather have this show on that instead, go to Trust Insights AI TI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.
Katie Robbert:
Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable Insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing roi. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and martech selection and implementation and high level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google, Gemini, Anthropic, Claude Dall E, Midjourney, Stable Diffusion and metalama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community sharing expertise through the Trust Insights blog, the In Ear Insights Podcast, the Inbox Insights newsletter, the so what Livestream webinars and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data driven. Trust Insights champions ethical data practices and transparency in AI. Sharing knowledge widely whether you’re a Fortune 500 company, a mid sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.
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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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