So What? How to Make Your Own 2025 Wrap Up

Well, hey everyone, happy Thursday. Welcome to So What, the Marketing Analytics and Insights Live Show. This is our last show for 2025, fellas. How you feeling about it? The holidays are right.

That's all I'm um it's it's been a good year. Well, good. I'm glad to hear it. Um also fun fact, apparently StreamYard now has timers on their banners. Uh and that's really annoying.

Uh so happy new year, everyone. So we thought we would end the year doing something uh a little bit more fun. So we always try to do something practical and useful to teach you how to use all of the tools that have been thrust upon you. And this week is no different, but what we want to do since it is the end of the year, is how to make your own 2025 wrap up. And so, you know, you can use what we're gonna show you to here's everything we did this year.

You can use it. I'm assuming there's a lot of different use cases where you can say, This is our end of quarter wrap-up, this is my own personal career wrap-up. But basically, you know, you're seeing, you know, Spotify and Apple and LinkedIn and everybody has their your year in X and what you did. Wouldn't it be great if you could build your own? So, Chris, where should we start today?

The hardest part of this entire thing is the data. Getting the data and getting it into some format that a machine is able to process. Once you have that, the tools to do a wrap-up are actually pretty straightforward. And and we'll show probably the easiest one for folks to get started with. But the hard part is where's the data and what data do you want to do use?

Uh, in rehearsing for this episode, I did a version where I used some of uh Trust Insights internal data to do an internal wrap-up. And I'm like, I can't show any of this on the air because it has tons of confidential information. Like, oh, you won this client for this amount of money. I'm like, that's true, and we celebrated that. It was a good thing, but I can't show that on the air.

So I said, well, we'll do uh the analytics for marketers slack group, sort of a group wide wrapped to see uh or or any of your end summary as a way to uh do that as an example because that's safer data uh than it is our proprietary confidential information. So yeah, first part is you gotta get the data. Now, if you are an admin of an uh an environment like that, you can literally just go into the admin portal and hit export the year, and it will just give you a big file. And that's what we did. If you don't have that, then you need to figure out how you're gonna get a hold of the data and what you want to use.

Say if you wanted to do your own version of LinkedIn wrapped uh with stuff that you actually cared about. You could go to LinkedIn, go to your settings and privacy and say export my data, and then obviously go through the spreadsheets that are generated, cut bring all the stuff that's 2025 out, and then use that. You could, I don't know how good an idea this would be, uh, but you could go into your email and put all of your emails for 2025 into a folder, export that, put that into a system, and you know, here's the greatest hits in my inbox. Well, I mean, you're sort of like poking fun at it, but there are certain people that you know, think about like sales, for example, that your KPI is how much outreach did you did, did you do? And so if they're not tracking things in a system like a Hubspot or a Salesforce, that is a really good way to get your hands on that data.

So think about the KPIs that you're being held to uh as an individual and where that data might live. So I think email is actually not a terrible use case, it's a matter of the context of what it is uh that you're looking for. Like, so for John, for example, like your whole role is making those connections and getting the contracts in the door and closing them. So looking at the emails that John has sent, um, I think is actually not a terrible, like, we're not actually gonna do this, John, don't worry. But uh, you know, it's not a terrible idea because if we were a larger organization that were not intimately aware of what we're all doing at all times, it would actually be a great way for John to present, like, hey, here's everything I did.

Look at all of the cold outreach I did, or look at all of the connections that I made. So therefore now, and it's a great way to sort of say, like, hey, and now moving into the next year, here's what I want to make for money, and here are the goals that I want to set. So I just shot down your idea that it was a bad use case. It's not a great use case for something I would do on the air. Um that's okay, but I think in general, when we talk about the 20 your own wrap up in general, but yes, on air.

Like I'm interested to see what you pulled for Trust Insights privately. Um, but yes, publicly, I agree. Yeah. Um, and you're absolutely right. So a big thing that we do that for uh with emails is for client review.

Uh at the end of the month or end of the quarter, we'll export the the uh uh client mailbox within our system and say, you know, what did we do with this client? What were the greatest hits of the quarter? Uh particularly when you know you have clients who say on a regular and frequent basis, like, what have you done for me lately? Um, and we can say here's here's what's happened. So uh it's a great way to do that.

So let's take a look at uh we'll go we'll walk through the example of how to do this. I will say that again the data manipulation part is the hardest part. Everything else after that is easy. So we're gonna use Slack, the analytics for marketer Slack group. When you go into uh uh Slack as an admin, you get a zip file, and when you open up that zip file into a folder, you get all of your Slack channels, and then you get a JSON file for every single day of every single channel.

So you get uh 1731 files all nice and neat put together in in one massive folder. This is kind of a pain. Um if you're gonna use a tool like Google Gemini or ChatGPT or whatever, 1,731 files is a lot. However, if you are skilled at the command line, uh skilled in using a terminal, on most modern computers, you have the ability to uh uh process this data to get it glued all together and then put in more manageable chunks. Um, and if you if you're not sure what your computer has, this is a great opportunity to talk to a chat GPT or a Gemini or whatever and say, I've got 1700 files in nested folders.

Help me write a command line command that will glue them all together into one text file. Um so I'm gonna go ahead and do that. Um the input folder is AFM, the output folder is afm.txt. And now I have one great big honking file. This thing is 27 megabytes of text.

Y'all were real chatty this year. And if you want to join the conversation, you can go to our free Slack group at trustinsights.ai/slash analytics for marketers. It is a very chatty group, and it's something that I think that we really take pride on. It is it's a very engaged group. So I'm about I'm very happy to see that.

Exactly. Now, here's the problem. If you try and load that file into just about anything, it is going to blow up. I'll give you an example. Let's go into Google AI Studio because you can see just how big that file is.

If I just drag that text file in here, um, AI Studio is Google's developer uh AI portal. Uh it allows you to have the same access to all the all the models that you know uh in the Gemini ecosystem. 8.7 million tokens that exceeds the memory of Gemini. It it can't process that. There's no AI model on the market that is generally accessible that can process that much information.

So as ridiculous as it sounds, we now need to take that big file and split it into smaller files. But fewer smaller files we start. We start with 1,700 files. Uh again, using the commands that are available built into your computer. We're gonna say, well, let's split this into files of 20,000 lines each, which is about for most plain text, it's gonna be about one megabyte.

Um we're gonna take AFM text and we're going to let's see. What is the command to do? Oh, the initial suffix.txt. So while Chris is doing that, um, I think it's a good opportunity to remind people that this is why you always want to do some semblance of requirements before you start exporting things and putting it into other systems. And so, you know, Chris, one of the things I noticed that when you exported the data from analytics for marketers, it exported everything, every channel.

I would argue that we wouldn't necessarily need to look at the data from every single channel. I know that there's a few channels in there that are for specific uses that we don't necessarily need to bring in. So, you know, I think that that's why you would want to at least have a user story. So, you know, as the admins of analytics for marketers, we want to know what the top conversations were for 2025 so that we know how to engage with our community in 2026, for example. Um, it having some kind of requirements is gonna help focus down the data that you're looking at because we all have a lot of data, it can get really unwieldy.

Um, you know, John, Mr. Chatterbox up here, probably has the most data of all of us. Uh, so we want to make sure that he's focusing down his conversations so that we can do uh justice for his 2025 wrap up. All right, so we end up with 35 files. Each file is about 800 kilobytes.

That's a lot more manageable. 35 files is something that Gemini still can't handle because it's the same amount of text, but it's split up into pieces. So we would use a tool like Notebook LM. Notebook LM can handle that much data very, very easily. And so what I've done is I've gone ahead and put in all those files into Notebook LM.

You can see they're all listed here as sources. And then we start off by saying, I'll make this a little bit bigger. This corpus represents the analytics for markers, Slack group, private community operated by Trust Insights. Uh, 4,500 markers who have regular conversations about analytics, data science, and AI, communities open to the public. We want to provide a summary of the top 10 most impactful conversation topics in the corpus in descending order.

Impactful means that many members participate in the conversation or conversation threads were unusually long. Show your results in a markdown list in descending order. So it goes through, processes all 27 megabytes of data, and says, hey, your three topics are AI practicality and tooling, operational and strategic challenges, and non work and fun conversations. Those are the big three. For those who missed previous episodes of uh our live stream, you can go ahead and create media on the right hand side.

And the media is those are great things to do, things like slide decks and stuff. So a prompt for the slide deck might be something along the lines of let me bring this into a new window here so we can see it. And make it bigger so that we're not going blind trying to read it. Say make a top 10 topics of analytics for market slack group slide deck using the trust and states brand standard guidelines. Uh count down the topics in reverse order from 10 to 1 each slide should cover one topic.

And then I actually pasted the output because you can so that you get exactly what you want as part of that prompt. So are you ready to see the wrapped for analytics for marketers, Katie? I am. All right. Let's go into what analytics markers really talk about.

The top 10 things. AI practicality dueling, operational challenges, community connection, of course. Those are the things. Number 10. AI tools for content creation and adoption.

20 replies, 14 users. Evaluate the feasibility of local AI setups, sharing which models are best for which tasks, uh, initial presentations structuring essential documents. Right? So that's number 10. Okay.

Any reactions. No, I mean it makes sense. Yeah, believable. Number nine, outdated sales and marketing tactics. Interruptive tactics.

I remember that thread. In fact, John, this was something that you put it in the rants channel. Yeah, yeah. This is like my weekly complaint posts. So yeah, I'm fully unsurprised there.

Number eight, non-AI marketing tactics. The irreplaceable value of direct authentic conversations, listening first, uh, things like that. Um I think this was one of your threads, Katie. I mean, to be fair, they're all my threads. Um, I ask every question of the day.

But yeah, these are this is something, you know, a topic that obviously I know more about. So this is one that I likely weighed in on more. So it makes sense. Yep. Number seven, marketing analytics terminology and confusion.

Uh frequent confusion between different terms, differences between metrics and KPIs, frustration with Google's inability to name any product extensibly. Yep, that also sounds correct. Yep. This is like 2025. Like it's gotten considered like it used to be bad, but this year is just like times 100 as far as like horribly named stuff.

Yeah, they've just given up. All right. Number six, frustrations and critiques of AI, concerns over hallucinations, complexity of model naming conventions like Quentin 3235 BA22B thinking. Yeah, good job. Um, and societal environmental impact of widespread AI adoption.

We've heard a lot of uh a lot of our members have chimed in about that, which is a big topic in and of itself. It is. We could spend uh entire shows just on that. Number five, technical SEO and large data corpus processing, leveraging tools like notebook LM, technical tips for managing context windows, advanced techniques like vibe coding for exploratory research. Mm-hmm.

It's been a minute since we've talked about vibe coding, but technical SEO, GEO, AEO, those are all very top of mind. So I'm not surprised. I'm gonna keep saying it because it makes you disgusted. I have to find my joy where I can, Chris. Number four, AI automation tooling and implementation challenges.

Workflows, automation, core technical problems, agent to agent agent, and tools like Opal and Claws agents. Yeah. I mean, I'm definitely seeing a theme, and I'm sure it has a lot to do with the types of questions that we ask. Uh, because we want people to respond with their shared experience. And a lot of times, the shared experience is I'm struggling with this thing.

Can somebody help me do it? Or does anybody have their version that will help me? Exactly. Number three, the great Thanksgiving side dish debate. So we did have a comment.

Um working fun. I thought the Friday question of the day would crush all of the topics. Uh, we are seeing, I'm guessing it's the top three. Because those uh, if you don't know, on Fridays, we always ask a non-work related question. So it's as excuse me, as professionally as unhinged as we can get, while still sort of keeping it on the rails.

And people in this community love to debate things about food specifically, which is hilarious. So if you want to ever join those, again, it's free to join. Trust insights.ai slash analytics for marketers. This community is very passionate for good or ill about their food. Exactly.

It's not a real holiday meal if you can't argue passionately about the merits of green bean casserole. It's true. Which was a marketing trend, Patrick. Number two, operational bottlenecks and constraints. The biggest tendencies to workflow efficiency, internal political whole uh hurdles, and feeling overwhelmed with constant news cycle stuff.

Makes sense. I love the quote about agile being way too rigid. That's awesome. I think that was actually I think I said that. It's true.

It is so funny. There's it, you know, it was like the spirit of agile, but now there's people going around like if you don't do it this way, it's wrong. You can't change agile. And we could certainly, that's another topic that we could, you know, highly debate for a long time, but it is uh the true agile practice is very rigid, very rigid. And so, you know, anyone who wants to say like we have to do uh true agile methodology, buckle up.

It is not easy. It can work, but you have to have everyone in lockstep. Exactly. Um, does anyone want to take a guess at what the number one topic of the year was? If it's Taco Bell, Kelsey is so fired.

It is fast food fries. I think well, that is was that that was the Taco Bell thread, I think. That was the Taco Bell thread, yes. She has turned every thread into the Taco Bell thread. Um, I mean, I do remember asking this question, and you know, it's it is it is very funny to me how polarizing certain foods are to people.

Uh, you know, and it's just it just cracks me up, and I'm it I appreciate that everybody in the community comes to the table with an opinion and feels comfortable sharing that opinion, knowing that it's a safe place to do that. Exactly. And I think that's probably the most important value of the community, particularly this year, which was a very stressful year for a lot of people for many different reasons. Um, is having a safe place where you can kind of just uh well, I'm not in my case, but you can let your hair down, um, and and be among peers. Mm-hmm.

So uh these are the patterns, right? Practicality and tooling, operational challenges, a community and connection. And unsurprisingly, this pretty much mirrors who trust insights is, right? You know, Katie, if you look at this, this is basically us. Oh, yeah.

I mean, you know, again, for those who don't know, I am the one in charge of asking the question every day to start the conversation. So obviously, I'm gonna be asking questions that relate to the work that we do at Trust Insights. I do try to make sure that I'm trying to give a well-rounded set of questions so that everybody feels like there's a place for them to participate. But it's, you know, even when we say, like, tell us the good about AI, for example, it always naturally organically turns into here's my frustrations and critiques of AI, because that's just the human experience with these tools right now. Um, so it is it's very interesting.

And as someone trying to figure out how we can best support, you can't beat this kind of you know, free market research. This is directly from the mouths of people who are in it, and it further confirms sort of like the direction of where we want to take our services, our ICPs, all of those things. Like this is, you know, priceless data to me. It is. Now, one of the things that I think is interesting, and this is not a surprise given the marketing space that we're in, is that even though the group is and has been called Analytics for Marketers for years and years and years now, uh, we had very little actual analytics discussion because of the 800-pound gorilla in the room that is that is AI.

Um, and to your point, you know, obviously we want to serve the needs of our community where they are, uh, meet people where they are, is what we always say. And where they were this year was around our all things artificial intelligence. Well, I would also like to caveat that, Chris. With I do ask non-AI questions, but someone in this room always takes over the thread with something AI. So there's a good reason why all of our conversations go that direction.

So I could say, hey, so what's your favorite color? And someone in this virtual room would give me some sort of AI response, which would derail the thread. Mm-hmm. She's on to me. I'm on to you, John.

Oh dear. Um, so let's see what else we got here. A reminder, a very nice call to action that you should be joining the uh the trust Insights community. Please do join us there. So, this was this is how we did it.

So we took the data, put it into Notebook LM, and then created the the outcome, created the output. The versions we have, we obviously have the slide deck. We have an infographic version, which is again very similar. And you can also do the video explainers, right? So you have the video explainers that have uh all the the you know top 10 community topics, exact same thing, same prompts that we used, but we it's just generated across all the different modalities.

So if you wanted to do your own uh 2025 year-end summary, this notebook LM is the tool that I would recommend that you start with. You can absolutely use Chat GPT or or other tools or Gemini's plain image generation models to roll your own if you have the data. The hard part really is how do you get the data out of the system? And if you have no data, but you wanted to make something anyway, um, you could go and get credible third-party data that you're allowed to use. So an example would be uh Reddit as an API.

Um, you could, if you want to understand a topic, grab a subreddit's topic conversations for the last year, um, and summarize that using the exact same uh setup to see. And in fact, that's something that we do every quarter for ourselves and for our clients. We look at what are the top marketing topical discussion threads and AI discussion threads on Reddit to see where the community, the broader community is where where people who are not us uh where their heads are at. I've actually used um one of those notebooks and found it very helpful to understand people's opinions of the CEO or the C-suite in terms of where they're at with AI adoption and it was nothing in the conversations surprised me. There's a lot of you know people feeling like people in the C suite are so detached from what's going on in the day to day but it was nice to have the data to support that.

Now you know coming back to analytics for marketers I can see a lot of other ways that you could be using this data. So if you run a community where you know you want to promote engagement or you're looking for your ambassadors you could probably ask questions of your notebook to say like who are the you know top 25 most engaged um community members or you know excluding the Friday questions of the day you know which obviously skews the data because those tend to be more popular or what are the non-AI topics that were sort of bubbling to the top so that you could sort of look at it a lot of different ways um depending on what you're trying to what your outcome is so for us we can use that infographic as a social post to remind people hey this is a great place to come and have conversations here are the kinds of things we talk about and then also I guarantee our community members are going to be thrilled to see the kinds of things that they've been talking about all year. Thanksgiving side this um yeah, you absolutely mind this for all sorts of stuff. You could take, you know, the top places people want to have a conference. I remember that discussion happened at one point.

Um whose general fast food is better if you just want to start fights repeatedly uh between Kelsey and everyone else. But we've also asked questions like who are the people in your space that you would recommend that you follow, like who are the influencers, who are the thought leaders, like all of that data when you look at a community, it feels hard to pull out. But when you put it in a format like this, it's you know, here's the top influential voices, or here's who our community thinks are influential voices, and that's really useful for other people. And uh Katie, since you were asking and asking and asking. I can't see what that says.

It says the person I was most connected to this year is on this call with me. John dummy. So it shows you like the emoji that you use the the most or the reaction, uh, how many new connections you have, and then it also shows you the person that you connected with the most. And unsurprisingly, Chris and I connected with each other the most on LinkedIn, which comes as a shock to everybody. Exactly.

Um so in addition to one of the things that is kind of a hidden trick with doing this kind of summaries if you can, if you have the technical capability. Plain text is great. And I would say plain text is what you should provide. But if you can, Notebook LM, we lest we forget, is a Google product and is powered by Gemini. In fact, it is powered by the brand new Gemini 3 Flash now, which came out yesterday.

Um, which means that it has all of Gemini's strengths and weaknesses. One of which is that it is very good at reading, uh, it's called JavaScript Object Notation or JSON. What we provided in here was not plain text. What we provided was JavaScript files. So in, for example, the the work life balance pets channel, you can see this is what is in the source files themselves.

It is this format of file, JSON. What that means is a tool like Notebook LM can read this data with a good deal more accuracy because it's like a spreadsheet, but in a version that it is capable of reading. We cover this by the way in our prompt engineering course and our generative AI use cases course. Um, but JSON is easy for Gemini to read and understand, and it does a better job of doing basic operations like tabulating or saying, hey, here's who said this, because it's structured data. And so if you're gonna do these kinds of wrap-up things for yourself, do remember if you can provide it in that format because it will it will do great with it.

So could you export data and then bring it into like a Google Colab and say, can you transform this into a JSON file for me? Yes, and you can even do that in just like regular Gemini, as long as the files doesn't exceed the size of what it can work with. You can it will actually speak that language well. So, John, um, to give you more stuff to do, because that's what that's one of my favorite parts of the live stream, is adding to your to-do list is you know, when are we gonna see the, you know, top marketing over coffee conversations of 2025 or the topics that you covered, because that's something that I think would be uh useful. I would imagine that much like with our analytics for marketers uh community, the conversation tends to skew towards AI.

Um, do you feel that that's true of the interviews that you've done for marketing over coffee this year? No, but the thing with the marketing over coffee stuff is always that because I've more than a number of times we've done tried to do quarterly reviews or annually reviews, and the problem we have is more than half the content, like close to 70% of the content is so topical that it's stale within you know three months or less. So there's just the the amount of reuse is astonishingly small. I think at one point I did like three years of material, and less than 20% of it was even relevant anymore. You know, so much of it was expired and just you know, complaining about my space and stuff like that.

So there was nowhere to get it. Um but yeah, I don't know. That that's a good question. Yeah, the marketing over coffee wrap up, it would, you know, pretty much just be lips and stats of like, okay, here's the interviews that did well, here's the shows, the episodes that hit. But even that is really weird too, because I don't know, there's just so many things that happen where once in a while a show or an episode will get picked up in some random channel and it'll get a ton of action.

And it really even has nothing to do with the content. It's just something that managed to get picked up and be promoted, you know, and and then we see lift across the board. But yeah, coming up with like a this was the best five things of the year is definitely a challenge. There's not a lot of data as far as listing patterns and things like that. So yeah, yeah.

Oh, well, and yeah, and here's the most important one is I did one year decide to do like an outtakes and funny clips episode, you know, just like take everything. It took so damn long that I was just like, I'm never doing this ever again. Like this is completely if I want to, you know, make this 500 times the work it needs to be. I guess that's something, you know, if I had all kinds of hours over the holiday, load up 50 episodes and just have it pick the five funniest clips. But again, it's the same deal.

Like I would be relying on AI's opinion of what's funny. You know, I don't have people clicking the the laugh button if I was like Instagram live or something like that. So it's I don't know. It's a a rough problem to crack. But I think that goes back to the point that Chris started with you can make you know your wrap up of anything provided that the data exists and the data exists that is good quality and you know doesn't have holes in it or is missing anything or has the correct context.

I mean that's really the core of anything is does the data even exist. Yep. So how would you go and get that? Because here's a funny thing notebook LM supports MP3s. Like you can actually you don't need to transcribe that you can actually go and get MP3s files.

So how would you go and do that? Yeah again you know it's it's not about the MP3 as much as it is the audience feedback you know comments made or you know like there's no way we can track people laughing in their car as they're driving around, you know, like that's the that's the real take. And then the other one is yeah, none of the sponsors want any of their data out there. Like that's another chunk of relevant data. Is like how many leads did you get, how many clicks did you get, all that stuff.

And of course, nobody is gonna share any of that stuff. Um but if you were just to sort of like at a high level, say, you know, these were the top five topics discussed on marketing over coffee, you have you do have the data. And I, you know, I hear what you're saying about the topics sort of are uh place and time, but I know when you and Chris record marketing over coffee, and by the way, for anyone who's interested, uh you can where is it? You can subscribe at marketing over coffee.com. Um podcast hosted by these two fellows here, has been going on for what feels like forever.

Uh you're entering into what your 19th year? Yes, yes, three hundred and eight years. We actually redate but it's a lot there it's a long-running podcast for a really good reason. Um, and so if you were to pull like here are the top five topics, you know, that Chris and I talked about, for example, it's probably something to do with equipment, headphones, whatever the newest, like you know, earbuds are probably something to do with you know, AI in the news, and then you know some books is that would be my guess. Yeah, that's it.

That would be interesting. That would be worth doing is just to grab the shows and just say, hey, what's you know, what are the percentages of topics? Like, what was the breakdown this year? Um, yeah, and I'd be interested in other demos on the authors and stuff like that too. Like, how many women guests were there, how many male, you know, how many books, how many vendors, that kind of stuff.

That yeah, that's all interesting. So let's talk through this because I think it'll be interesting. So, one of the things that is unique about marketing over coffee is that John has a really sensible file naming system. The name of the show every week is exactly the same except for the show number, right? So you could say, you know, if this week's episode, if you look at it, is um 898, and the previous week's episode number is MOC 897.

So if I were to go to Google's Gemini, I could say something like, Hey, create a list of URLs that decrement by episode number. Start here from 898, decrement by one, go to 897 and show me the last 52 of these. So it went back and creates that list. Now, if we remember, terminal is one of those things that if you're good at it, it you can make your life a lot easier. There is a terminal-based utility called Wget.

Wget is basically a browserless browser that can go and fetch a file. So if I give it 50 some odd episodes of marketing over coffee to download to my hard drive, which I did, um I have all of these episodes. Like we said, notebook LM supports MP3s. So I just drag all those MP3s in here. And now we can say, what were the top five topics that Christopher Penn and John Wall talked about in 2025, which is the corpus that I have provided.

List the topics by frequency in descending order. And let's see what we talked about this year. So in the time I'm going some kind of audio is number one. So in the time that you and Katie were talking about this, we were able to figure out the file. I knew the file structure because John is extremely organized, which we we love, um, and be able to execute this.

I did not have this pre-baked. So we have standard of AI, large language models, and AI strategy, frequent discussion to get across nearly all episodes, gear, hardware, and tech consumer technology. There you go, Katie. Yeah. Nerd tech.

Exactly. Events, conferences, and travel, economic, geopolitical, and market. Boy, they gotta get an earful in the Christmas episode. Uh and social media platforms and algorithm changes. So those are the top five topics that you and I, John, discussed.

Which I think that's a really helpful, you know, poll of information because that's now something that John can use to promote the show. Like, hey, if you're interested in these things, here's what we talk about. Here's you know what Chris and John talk about on a weekly basis that can become an infographic. Exactly. So I can actually cue that up there as an infographic.

Now, what John's saying, what are the top five topics that John Wall and special guests talked about in 2025, which is in the corpus I provided? Exclude shows in which Christopher Penn appears, only use shows that John Wall had a guest interview on, list topics by frequency in descending order. Because again, Gemini, it we can as long as we prompt it smartly, it is it's able to digest the stuff down. Let's see what comes out. I for the gendered stuff, that's something that I'd want to experiment with offline to see because every model has biases built in, and that gets into some very tricky territory.

You in terms of whether or not how many shows did women appear on, how many shows did men appear on, et cetera, based on people's names? Because we don't explosive say, oh, and my women guest today is. Right. Well, I mean, and you know, this is something that we were talking about internally the other day. There are some ungendered names for I don't know if that's the correct term, but like, you know, uh names that someone of any gender could use.

So it's very hard to say it's this or this. Yes, like the author, DC Fontana. Sure. It's Pat. Yes.

Uh top five uh shows uh exclude shows Christopher Penn, generative AI implementation strategy, marketing strategy, branding and consumer behavior, social media management platforms, operational technology, software development, social and cultural integrity. I mean, that's also a good promo for you know, if you want to pitch your uh person to speak on marketing over coffee, here's the things that are covered. Yeah, prepared to have a point of view. It definitely reflects our like nothing is too nerdy or too tactical for this show, you know. Like we're where this is all deep dive stuff.

People outside of marketing are just like, what is this? Except for that doofus this morning that sent us the pitch, like, hey, you're as a fellow spa owner. I'm like, what? Oh, I didn't get that one. That's another fellow spa.

Fellow spawn. Um let's take a look at the infographic and see what came up. Ah, there you go. Top five topics. I didn't even have the coffee up in the middle.

It's actually in terms of infographics, John, that's not bad. I hope to see that on social. No, I love number four too, because that was like one of the things that we laughed at. Remember all the way back when we did we had AI generate images of gear, and it would just like make these bizarre things with buyers sticking out of it. Like it didn't, you didn't even know what the hell it was.

But that's like that's clearly a drone and an Apple watch and a mic and headphones like that. So we we've nailed it. Yep. Uh if you wanted to, uh, in a different notebook, you can with Notebook LM provide uh lists of things like individual YouTube videos or a pile of YouTube videos. So you could give it like all the episodes, say, of this live stream for the year and see more of the topics we covered on this year's live stream.

So there's no shortage of ways that you can as long as you know how to get the data, which again is the hardest part. If you can get the data, these tools can make it super easy to do these year-end wrap ups. And I would encourage people to give it a try. One of the, and this was in last week's inbox insights newsletter. Or is it last week?

It was a week before. Um, do this whenever you are up for a review, right? So take your inbox, take your your Slack, your Asana, your Jira, your whatever, export your data. And when it comes time for your performance review, you say, hey, here's what I did in the last year. Give me a raise.

Yeah, yeah. Prove your worth. That's a wonderful. That's just a huge time saver for any annual review process. Exactly.

And if you're a manager, maybe consider doing that across your team. Because one of the things I know as a terrible manager, one of the things I suffer most is recency bias. Remembering the last few things that somebody did and not the whole record from the entire year. Because you normally don't go back and go, oh, well, April, you screwed that up. So unless it was really bad.

Um, and um, but it if you are a manager can help you overcome your own recency bias to say, oh yeah, this is what this person did for the whole year, not just the last two weeks, which is all I can remember. And I think that that's a good way to you know round out the episode is you know, think about the data you have, think about the points that you want to make, and you know, use the tools that you have. So make your own 2025 wrap up, bring stuff into Notebook LM, see what it comes up with, and if nothing else, join the conversation in our free Slack group, trust inside AI slash analytics for marketers, and you too can weigh in on the best cut of French fry and or the best Thanksgiving side dish and other things more to come in 2026. Oh, yes, yes. And next in fact, tomorrow I think I had petitioned to have to have people try to use generative AI to make their own holiday song.

So yeah, you did. I didn't approve that idea. No. But don't worry, it's in there. Don't don't spoil it.

All right, I didn't know completely. That is gonna do it for this episode and this year of this year of the so what live stream. So to everyone who has watched whether this is your first episode or whether this is your 52nd, uh, thank you so much for being here with us every single week. Uh, we are on Thursdays at 1 p.m. Eastern time on most weeks, except for the last two, because we're we're we're taking off.

Um, and we hope that you participate in the Slack group, this live stream's community, uh, the email newsletter that comes out on Wednesdays. Uh, if you have questions for our podcast, which we record on Mondays and air on Wednesdays, we're more than happy to make stuff just for you to answer your questions. But most of all, thank you for being a part of our community. Take care, and we'll talk to you all in 2026. 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 slash TI podcast at our weekly email newsletter at TrustInsights.ai slash newsletter. Got questions about what you saw in today's episode? Join our free analytics for marketers Slack group at TrustInsights.ai slash analytics for marketers. See you next time.