So What? How to Streamline Your Marketing with Google NotebookLM

So What? Marketing Analytics and Insights Live

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn all about Google’s NotebookLM and how you can use this powerful tool to streamline your marketing efforts. You’ll discover how NotebookLM differs from other generative AI tools and why it’s uniquely suited for marketers. You will explore practical use cases for NotebookLM, from analyzing weeks of meeting transcripts and customer feedback to generating fresh content ideas.  You’ll see real-world examples of how to use NotebookLM effectively to boost your marketing results.

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In this episode you’ll learn:

• How to get started with Google’s NotebookLM
• Use cases for using NotebookLM for marketing
• Best practices for maintaining NotebookLM

Transcript:

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

Katie Robbert: 00:35
Well, hey everyone. Happy Thursday. Welcome to So What? The Marketing Analytics and Insights live show. I’m Katie, joined by Chris and John.

Christopher Penn: 00:46
Oh, yeah, just gonna do that.

Katie Robbert: 00:51
Oh, boy. This week we’re talking about how to streamline your marketing with Google NotebookLM. NotebookLM is one of a thousand tools that probably hit the market over the past 18 months that has some kind of generative AI built in. We want to talk through the basics of NotebookLM—why it’s a tool that you, as a marketer, should be using—and what the heck to do with it. So, Chris, where would you like to start today?

Christopher Penn: 01:21
Let’s start with an introduction. If we head over to Google’s website, this is how they pitch it: NotebookLM is your personalized AI research assistant, powered by Gemini 2.0. We’ll talk about all the different features and things in it—all the flashy stuff. But a couple of things to note: There are two versions. There’s the free individual version, which uses Gemini 2 Flash—Google’s newest model. You can upload PDFs and all sorts of good stuff, create summaries, create audio overviews, and things like that. Then there’s Plus, which just rolled out literally days ago. Plus gives you more documents, more audio overviews, and allows sharing within your Google Workspace. We learned yesterday you can’t share outside your Google Workspace. I could create a notebook, say, for Trust Insights, which we all do, and—

Christopher Penn: 02:13
—then share it with the team so folks have access to it. A couple other things worth noting, with the restrictions they specify: With NotebookLM, you can have up to 100 notebooks. Each notebook can contain up to 50 sources. Each source can be 500,000 words long. So we’re talking about a lot. With Plus, you get 500 notebooks, 300 sources per notebook, and fewer limits. The big thing everyone wants to know about is privacy. In all versions of NotebookLM they’re showcasing here—for Plus, the Google Workspace or the Google Cloud—your stuff is not trained on, is not seen by human reviewers, unlike the free version. That’s an important distinction.

Katie Robbert: 03:03
So let me ask the basic question: How is NotebookLM different from me pulling up a document and using the search function?

Christopher Penn: 03:16
NotebookLM is a very specific kind of AI system. The technical name is Retrieval Augmented Generation, which means you load documents and data into it, and then that special version of Gemini asks questions of the documents you upload to it. What makes it different, special, and better in a lot of cases for certain tasks is that if you load a document to NotebookLM and the answer isn’t in there—isn’t in the data you provided—it will not give you an answer. It will say, “I don’t have that information.” Whereas if you use, say, any other general AI tool, it will say, “Well, here’s what I know about this thing.” So if you have high-stakes material—like law, for example—you might want to do those queries in NotebookLM.

Christopher Penn: 04:09
If you ask it, “Hey, what is the law on this regarding consumer financial regulations?”, it will tell you and cite where in the document it found that. If you ask ChatGPT, it could just make it up.

Katie Robbert: 04:25
Okay. So really, the benefit is that it’s constrained to the information. So while, yes, it’s hosted on the internet, it’s not searching the internet for answers; it’s only searching. But again, the basic question is: Do I need to use NotebookLM, or if I have a document, why can’t I just use Control+F to find the information?

Christopher Penn: 04:55
It depends on the scope and scale. Let’s get started with it so we can answer that specific question. I’m going into NotebookLM. Here’s the basic interface. I’m going to create a new notebook, and we’re going to upload some sources. I’m going to load 39 weeks of conference calls—all the Trust Insights Monday and Friday standups that we have. This is a lot of information. Could you Control+F and find something in here? Sure, you could. It’s going to take a while because you have to open all 39 documents. More importantly, it allows you to see across documents, whereas with Control+F, you have to go document by document to try and find things. Let’s ask something fun and innocuous.

Christopher Penn: 05:46
Where has Katie gone on vacation?

Katie Robbert: 05:50
Oh, boy.

Christopher Penn: 05:57
No, she went to Vermont. She went—Chris jokes—you went to Montreal. You actually didn’t. You went to New York. You were planning to go to Austin, Texas. You made a potential trip to Rhode Island. So those are kind of—you know—it went through very quickly, as opposed to us hitting Control+Command+F over and over. Again, we found those things. This is the bare-bones benefit: You put your documents in, and you can figure out what it said, what’s in the stuff. This is a huge time saver, and we know there’s a decent amount of reliability because it’s the documents here. One thing that’s very interesting is that you can turn certain sources on and off.

Christopher Penn: 06:45
You might want to ask information from just this week’s meeting or just certain weeks’ meetings. In this example, you don’t have to ask at all, just by turning them on and off in the left-hand control.

Katie Robbert: 06:59
Interesting. We have a question from Nancy: Since you’re limited to 50 sources, couldn’t you ask it to summarize all of those calls, then add that one summary as a source so you can delete some of those calls and add more?

Christopher Penn: 07:18
You could. You could do that; however, you would lose a lot of the details. I think one of the great advantages of this is being able to say, “Yeah, I want—I have—may have lots of questions, so I might not.” There are cases where you definitely would want the summary, but there are other cases where I was like, “No, I actually need raw materials.” Here’s the most important thing about NotebookLM—the thing that will make or break your success with it: You need to be good at gathering and preprocessing data. To Nancy’s question, if you had 39 PDFs, and you are aware of utilities, there’s a free utility called PDF Unite. It can glue all 39 PDFs into one, and then you just load that one source and you have access to it.

Christopher Penn: 08:05
If you’re good at processing large amounts of text and breaking it up into pieces—we had this experience the other day. We were at a client, and they were asking us, “Hey, can you analyze this one piece of legislation? It’s a million and a half words.” It went past the length limit of what NotebookLM can handle for a single document. So we had to use a free utility to split it into four pieces and load each of the four pieces in so we could ask questions of it.

Katie Robbert: 08:31
So if right now, let’s say we’re using an AI-based transcription service like Fireflies or Otter, or one of the various tools that already gives me a summary, that already gives me action items—what is the benefit? What is the use case of this additional step? I know we’re talking specifically about weekly meetings, but maybe you can extend it to a different use case. I’m curious, and I’m really trying to understand why I need something like this versus the million other tools I’m already using.

Christopher Penn: 09:13
That’s a good question. This is any document source. Let’s start a new notebook so you can see. You can add things straight from your Google Drive. You can add things like pages from websites or YouTube videos. You can put in copy text. You can even drop in audio files. If I wanted to take, say, the last 100 episodes of Marketing Over Coffee, boom, just toss them all in here, let it process them, and then I can ask questions. For example, every interview John’s ever done on Marketing Over Coffee, I could put them in here, and we could ask questions of the entire pantheon of Marketing Over Coffee guests, or even just individual ones. Seth Godin’s been on the show like seven times now.

Christopher Penn: 09:57
I could take just the Seth Godin episodes, put them all in, and say, “Let’s ask virtual Seth Godin questions about what he’s talked about in Marketing Over Coffee.” This is true of any data source you can get your hands on. That’s why data management is so important: If you have the data, you can make incredible use of this. Let me show you an example. Katie writes the cold open to the Trust Insights newsletter and has for years now. At the end of every year, we bundle them all up and produce “Letters from the Corner Office”—a PDF; it’s downloadable; it’s on the Trust Insights website. I put the last three years of this in here, and now I have a virtual knowledge base of everything Katie has said in the newsletter.

Christopher Penn: 10:41
I can go in here and say, “What are the overarching themes that Katie writes about?”, and it will spit out a very nice summary. I can ask it specific questions: “Has Katie ever talked about—in fact, let’s do this—has Katie ever written about SARIMA and compared its usage to other time series forecasting methods like Grey Kite or Prophet?” Now that’s a very specific question.

Katie Robbert: 11:13
I know the answer to that exactly. I have written about SARIMA, but not in that specific way.

Christopher Penn: 11:21
It says it doesn’t contain any information about SARIMA, Grey Kite, or Prophet time series; however, the sources do discuss the use of predictive analytics and forecasting more generally. In all of these things, I can tap into the citation, and it will show me exactly what issue of the newsletter I wrote about that. So I basically have a virtual Katie whenever I want.

Katie Robbert: 11:42
I really like this use case because we used this a couple of weeks ago for the newsletter. We were on the podcast and were talking about a lot of different things happening, and I was like, “Oh, we should probably do something about building trust,” and you rightly said, “I think we’ve talked about that a lot.” So we went to this particular notebook and said, “What has Katie written about building trust?”, and it gave me a very long list, plus all the resources. I was able to use that as the foundation for that next week’s newsletter because I’ve talked about it so often. Why reinvent the wheel when I can really just summarize the bullet points and point people to those other resources that are still ours? It’s doing a few things.

Katie Robbert: 12:36
It’s giving them the summary, but also giving them the opportunity to look at more of our content.

Christopher Penn: 12:44
Exactly. Because this is powered by Gemini 2, it has the same facilities as the rest of Gemini 2. When you give it PDFs, it can read and see the actual graphs and images inside. So it’s multimodal. The thing that everybody loves to talk about is the audio overviews—the ability for this thing to create a podcast-like experience. However, they added something new in the most recent revision: the ability to join the conversations like a guest caller. I’m going to play this very short one-minute clip to see how this would work. We’re going to use “Lettuce in the Corner Office.”

NotebookLM Female Voice: 13:23
I think it speaks volumes about her approach to building trust. By being so open about the challenges and the learning process, she’s showing potential clients that she’s human, relatable, and not afraid to be vulnerable.

NotebookLM Male Voice: 13:37
Yeah, that resonates. Oh, yeah.

Christopher Penn: 13:39
Does that matter? Does being relatable matter to clients? Or is it really something intangible that we can’t measure?

NotebookLM Male Voice: 13:50
That’s a fantastic question, and it really gets to the heart of what we’re discussing.

NotebookLM Female Voice: 13:53
It’s a really insightful point, actually.

NotebookLM Male Voice: 13:55
Is it all just a feeling, or does it actually matter?

NotebookLM Female Voice: 13:58
From what we’ve read, I’d say it definitely matters. And it isn’t just some intangible idea.

Christopher Penn: 14:02
Yeah.

NotebookLM Male Voice: 14:02
Like we saw, being open and honest—those things seem to make a difference.

NotebookLM Female Voice: 14:06
Exactly. It’s not just about appearing relatable, but about building genuine connections.

NotebookLM Male Voice: 14:12
And that brings up a grape.

NotebookLM Female Voice: 14:13
Oh, go for it.

Christopher Penn: 14:14
What are the foundations of trust that Katie has written about?

NotebookLM Female Voice: 14:17
That’s a great question, and it’s helpful to take a step back and look at the big picture.

NotebookLM Male Voice: 14:22
Yeah, let’s unpack this. So what are the foundations?

NotebookLM Female Voice: 14:24
Well, from what we’ve seen, Katie Roberts’ approach is built on a few core principles.

NotebookLM Male Voice: 14:29
Okay, let’s hear them first.

NotebookLM Female Voice: 14:30
There’s the idea of transparency and vulnerability, which we’ve already touched on.

NotebookLM Male Voice: 14:34
Yeah, that seems to be a big one for her.

Christopher Penn: 14:37
So that’s an example of the interactive version, where you can be a guest caller to your own content and essentially be—you know—if you were to take the recording, you would have a—you could make an episode of a podcast that isn’t just machines. I like that part the best because when the feature first came out, everybody started cranking out automated podcasts with the same two voices all the time. When you’re part of the discussion, you bring your unique human perspective to it.

Katie Robbert: 15:12
It’s a little creepy, but I can see where this is going to be a really helpful sales tool or marketing tool. I struggle to write really good marketing copy or even speak about myself to promote myself. But if I have all this information, I can create that kind of audio overview of two people, hopefully putting my work in a positive light so I don’t have to do it myself. John, I feel like you can retire from Marketing Over Coffee. You’ve done it long enough that you can just load all these up and let the AI take over and start doing the episodes for you.

John Wall: 15:59
Yeah, just cranking them out. I like how it has the built-in pause to run the query. It gives itself time to get the answer together.

Christopher Penn: 16:15
The other thing about this—and there’s a question Liz was asking—is that there are ways to correct it if it’s inaccurate. In the audio overview section, there’s an area where you can add up to a 500-character-by-500-character prompt—basically two tweets—where you can specify things like how to pronounce names. If you’re familiar with IPA phonetic notation, you can specify how to pronounce Katie’s name, because Katie’s last name isn’t pronounced how it’s spelled. In general, it’s garbage in, garbage out. If you dump garbage in, it’s going to do things that aren’t great and will screw up.

Christopher Penn: 17:08
I personally would never just take the raw output from this and stick it on the internet. I would at least do post-production on it, just to fix the compression. The compression is tuned for what it was trained on—NPR-like sound.

Katie Robbert: 17:28
I was going to ask because it sounded very NPR-like, exactly.

Christopher Penn: 17:34
Now, in terms of getting other data, Nancy asked a question about getting stuff that’s behind logins and firewalls. You can’t get things like that in here. You have to pull it out yourself. There are a bunch of different ways you can do that. You can use scripts like Python scripts to extract the data. You can use things like the operator from OpenAI and have it record what it’s seeing on the screen. You can use Gemini’s real time. There are a lot of different ways to do this. The core principle is that it’s a form of Gemini; it’s locked to the data you give it, and you need to give it data. That’s the biggest challenge, and that’s where people will get value out of it or not get value out of it.

Katie Robbert: 18:18
That’s true of pretty much any system—not just generative AI, but anything in your tech stack. It’s garbage in, garbage out. When we’re thinking about more traditional tech-stack platforms, we’re thinking numeric. Now we have the opportunity to do more of the unstructured, qualitative stuff that we struggled with as marketers without really expensive tools. That’s why market research tends to be so expensive—pulling those trends and insights out of written or verbal feedback. Now I’m like, “Okay, great. I’m going to load all the feedback we’ve ever gotten through our contact form and all these other reviews and YELPs and whatnot into here and then start asking questions like, ‘Where are we sucking wind?'”

Katie Robbert: 19:21
Why? Where are we doing really good? What’s working? What’s not working? That’s a great use case.

Christopher Penn: 19:30
That’s a fantastic use case. Before the show, I went to the Marketing Over Coffee website, went to our form manager, and exported the last two years of people filling out the contact form—all the pitches, good and bad, that people have sent us over the years. As a marketer, you can take call center transcripts, you can take CRM data, you can take anything. In the Workspace version, which is protected and they’re not training on your data, and no humans review your data—you can ask, “What are the most common issues that people call in about? What are the most common pieces of feedback we get? What are the most common company types that pitch Marketing Over Coffee to make unsolicited guest appearances on the podcast?”

Christopher Penn: 20:30
Remember, this is still Gemini, which means you can type in very basic prompts like this. But if you were to use prompt structures like the Trust Insights RACE framework or the PAIR framework or the REPEL framework, you don’t have to do the priming part because you’re obviously loading the data, but you still have things like a role statement—”You’re a world-class”—and outlining what you want it to do. You’ll still benefit from those prompting strategies. Based on the provided sources, the most common types of companies that pitch Marketing Over Coffee are marketing and ad agencies; PR and comms firms; technology and software companies; consulting firms; e-commerce businesses; podcast-related businesses; individual experts and authors; creative content agencies; and other things.

Christopher Penn: 21:11
You can ask things like, understanding that it’s subjective: “What are the least on-target, worst pitches that Marketing Over Coffee—a business and marketing podcast—has received? Things that are totally wrong for the audience.” This is a fun example. Think about maybe you have website feedback from a feedback form on your website and you were to put it all here and say, “What do people dislike about us the most right now?” Let’s see: a foundation repair expert; a cybersecurity consultant; an expert on handwritten letters; a business coach for wedding professionals; an expert on home foundations; an art director; and a podcast publicist.

Katie Robbert: 22:07
That all sounds right, John?

John Wall: 22:09
Yeah, that’s definitely high on the list. Foundation repair is classic. We’re rarely looking for that on the podcast.

Christopher Penn: 22:18
This is a phenomenal tool for customer care because you can ask specific questions and dig into the data really deeply to understand what’s going on. This is also a phenomenal tool—particularly the protected Workspace version—for client services. If you’re at an agency or providing client service, you can—if you know how to get the data out of your email system—put all your client emails into this thing and say, based on my client emails and the responses, “What things have I not done? What things have I forgotten?” I do this at the end of every month with our top clients, our biggest clients, and I say, “What did Chris forget to do this month?” He said he would in an email.

Christopher Penn: 23:06
I can export from our Slack instance—our company Slack instance—and say, “What things in these client channels?” Because every client gets their own channel. Did I forget about? It allows us to provide safe, detailed, constrained client feedback to know we’re not going to drop the ball on client stuff ever again because we have tools to remind us, “Hey, you forgot that.”

Katie Robbert: 23:32
That goes back to having good data in the first place. It’s great that you can load up your emails, but if your emails aren’t focused and actionable, you’re not going to be able to find out, “Oh, here’s what I said I was going to do.” Supplemental would be the notes and action items and transcripts from those client calls to say. Then the query is good: “What did we forget to do? What did we say we were going to do?” Let’s list out everything we promised this month, and then we can go, “Oh, well, there are five things that don’t ring a bell.”

Christopher Penn: 24:22
I asked this one: “What creators of Trust Insights mention on their weekly calls, excuse me, content marketing ideas?” We talk about all sorts of stuff on our weekly calls. I can go in and say, “What things have we talked about? Like, ‘Oh, it would be great if we did a live stream on this or a thing on that.'” There’s a lot of stuff on the ICPs—using the ICP as a scoring mechanism against individuals in the CRM, an executive GPA. There’s a lot of stuff we’ve talked about. We’d have to go through and say, “Did we actually do that? Should we have done that?” Just from our call transcripts is exactly what you’re saying, Katie: Here’s what we’ve talked about.

Christopher Penn: 25:00
So many companies have transcripts and call recordings—all this data laying around—and it just takes up disk space when you could be using it.

Katie Robbert: 25:13
I can get that list. You’ll share that with me? We did say we were going to do all those things. I can also see—John, you’re our chief statistician slash head of business development—you do a lot of input calls with prospects, and I can see you using this as a tool to further refine the sales process. If you take three or four calls a week, that adds up over time. You have those transcripts, and then you can say, “What are the common pain points that people are talking about?” Then you can bring that to myself and Chris and say, “You guys are only addressing about 50% of these, and that’s why the other 50% are walking away.”

Katie Robbert: 26:05
We don’t have clear examples of how we address those things. That’s a really powerful sales tool.

John Wall: 26:14
Especially as the sales tool gets to be multiple people. It’s one thing when a single person has a view of what’s going on. But if you have five or six reps making calls or fielding demos, something like this could be hugely valuable because people only care about what closes deals. They’re going to miss all the stuff that isn’t on the markers in the close—lost.

Christopher Penn: 26:36
I was working on this earlier today. I’m speaking at the Tourism Industry Association of Alberta next week in Edmonton. It’s a beautiful place. I love going to Canada. One of the things I was asked to address is that Canada has been in something of a productivity crisis for a while. Could I talk more about that and how AI will impact that? I don’t know much about that, so first things first: I go to an AI-enabled search engine like Perplexity, turn on Pro mode, and ask it for actual research on the topic. Then I find the best research, dump it in here, and say, “What is this? What’s causing this?” I have one from TD Bank.

Christopher Penn: 27:17
I have the Tourism Industry Association of Alberta’s own publications from their website. Put it all in, and now I have a good idea of what the root causes are based on the analysis so I can speak to it intelligently. If you’re in marketing and you’re running your speakers, you run a podcast, you have a YouTube channel, you have a newsletter—there’s no excuse for you not to be able to dig into any subject.

Katie Robbert: 27:44
Think about the companies that are trying to grow their business and expand into different verticals. We were talking about this with a client, and they were like, “Well, we’re in this vertical, and here are the other three verticals that we want to master and really become the leader in. But we don’t have enough, and our team is constantly scrambling to try to keep up so that we can bring on those clients.” This is your own internal Wikipedia that you can put together and say, “What happens in the ball-bearing industry? What are the pain points?” That’s such a powerful tool.

Christopher Penn: 28:30
Here’s another example: This is the new DeepSeq R1 research paper. You can download it from their website. They talk about all of what they did. One of the techniques they explained is GRPO. They say GRPO is one of the key techniques that makes DeepSeq so powerful. Explain how GRPO works in terms of puppies. With all forms of Gemini and all forms of generative AI, you can ask these tools to explain things in a context you understand. Group relative policy optimization as a method of saving training costs of reinforcement learning. The goal is for the puppies to learn to sit correctly when you give a command.

Christopher Penn: 29:26
The old policy is the not-yet-perfect way the puppies understand your command. When you say “sit,” each puppy will attempt the trick with varying degrees of success. Generate a group of outputs. You give the command, and each puppy attempts to sit. Each puppy’s attempt is an output. Some might sit perfectly, some might sit partially, and some just don’t sit at all. You observe how well each puppy sat and give the puppy a reward, and so on and so forth. We can see just from this explanation how GRPO works without having to look at the very detailed calculus here. These tools are phenomenal for training, professional development, and study.

Christopher Penn: 30:07
Katie, if I told you to write about GRPO for next week’s newsletter, this is how you would get at it.

Katie Robbert: 30:13
100%. You’re going on a web browser and searching what GRPO is—not super helpful because, number one, you’re inundated with whoever has the best SEO, not necessarily the most accurate information; and number two, you still have to read through it; and number three, you’re just plagiarizing.

Christopher Penn: 30:39
That is true. However, there are times when you absolutely do want to plagiarize—and that time is when you’re plagiarizing from yourself. One of the most useful things you can do is to take something like all the newsletters you’ve ever written and dump it in and say, “Let’s outline a book. I want to write a new book. I’ve been writing a newsletter. I’ve got a YouTube channel. I’ve got all this stuff. I know I’ve already covered everything I want in the new book.” You would load up all that data in however many forms and formats and places that you’ve put your stuff.

Christopher Penn: 31:18
Maybe you drop in the MP3s—all the episodes of In-Ear Insights—and say, “Now with this data, let’s outline the book and then let’s start writing the raw materials piece by piece.” I will say Gemini Flash 2, in particular, is kind of a crappy writer. It will spit out factually correct, very bland text because you can’t do as much extensive property as you can with regular Gemini. But you could take the outline—the chapters, maybe one at a time—and put them through something like Anthropic’s Claude and say, “Now rewrite this chapter, keeping all the factual information true, but applying Katie Roberts’ writing style.”

Katie Robbert: 32:01
That’s on my list of things to do. I’ve been asked—and you’ve definitely encouraged me—to write the 5P book. I don’t know where to start. I write about it every week. I have so much content about it—why not plagiarize myself and repurpose it? That’s probably the whole “work smarter, not harder” philosophy we’re talking about with NotebookLM.

Christopher Penn: 32:32
Exactly. In terms of streamlining your marketing, it’s all about using the tool—using its summarization and citation—so you can find and process really good things. There was a question in the chat about privacy for NotebookLM Plus. Your data, which runs in Google Workspace, is completely private for the free version. It’s not as we typically expect from all things. We want to say how you could get data sources for NotebookLM to process. If you want to do some research, use the AI search tool of your choice. You can use Perplexity; you can use ChatGPT search; you can use DeepSeq search. Anything where you’re searching the public web. Let’s say we want to learn about marketing attribution.

Christopher Penn: 33:22
I want to find peer-reviewed academic research from credentialed journals about marketing attribution. Focus your results on any publication or research published after January 1, 2020. Prefer publications that have DOI numbers. People do this wrong with AI search—they don’t give it enough parameters. They don’t give it enough things to focus on. Give it specifics: “I only want things with DOI numbers,” which only academic publications are issued. You get the answers on Perplexity. I don’t care because what I want is the actual sources themselves.

Christopher Penn: 34:10
I can go from source to source and download papers, put them into NotebookLM, and now I can ask questions of this corpus of knowledge about what I need to know about marketing attribution. Let’s take a quick look and see if we can download the free PDF. There was a problem requesting a thing. Let’s see. This is open access from 2020. Download PDF. The customer average. This isn’t a PDF. Let’s see, download full text PDF. Download focus.

Christopher Penn: 34:53
Okay. Even with a few of these samples, I can go to those samples and say, “Let me put—start putting in—start a new notebook here. I can put my attribution papers in and get a sense of what is current in the world of marketing attribution.”

Katie Robbert: 35:16
In terms of process, is it literally just as simple as start a new notebook, drop in your files, and then you can start querying?

Christopher Penn: 35:28
Yep, that’s it.

Katie Robbert: 35:31
Sounds too good to be true.

Christopher Penn: 35:34
It’s pretty good.

Katie Robbert: 35:38
In my experience, generative AI hasn’t been that straightforward. There’s always some kind of gotcha, or a step that the average user is missing. I know you can do more advanced prompts within NotebookLM, but in terms of getting the data in and starting to use it, this is one of the more straightforward platforms I’ve seen so far.

Christopher Penn: 36:09
Exactly. I just asked out of these papers, “Where does marketing mix modeling fit into attribution?” It says it can be used to optimize marketing budgets but isn’t as effective as attribution modeling. People are asking questions like, “Should we be using this?” It’s really meant for paying money to reach people; it doesn’t do a very good job with organic. But no marketing modeling software does that. Attribution modeling is better. How do you know the difference? Find your papers, load them in, ask questions.

Katie Robbert: 36:48
One of the questions we’re answering on this week’s podcast was, “How do I keep up to date with everything?” You can get that episode at TrustInsights.ai/TI-podcast. The answer is you don’t need to stay up to date on everything, but you need to focus on the things you need to stay up to date on. If the thing you’re responsible for is attribution, then you can be adding in sources or updating sources on attribution as new material is released and doing these queries like, “What’s new? What do I need to know? Break it down for me.” This is a good way to do that because the information exists. It’s a matter of how you’re going to build your process to understand it.

Christopher Penn: 37:42
Nancy points out, if you’re copying and pasting people’s quotes or posts of sources, you want to keep track of the links. You can do that. One of the cool things you can do is—this is Gemini-based—I could say, “Produce a list of links to articles I’ve written about podcasting, format it in APA citation format.” Not just create the list, but I can put it in the specific APA citation format. If you were to download, for example, our LinkedIn Attribution Guide or our 2025 Trust Insights Guide, all the citations at the back are in APA format because I asked the thing to do it, and it just spits it out. It’s a huge time saver because I don’t have to do this.

Katie Robbert: 38:34
That’s a big deal. John, what kind of use cases do you think you would use this for in your daily workflow?

John Wall: 38:48
It’s great. For any fixed amount of—if you’ve got a whole corpus of a ton of stuff and you need to go back and prioritize—I’ve got a project client that’s working on a go-to-market strategy. They want to know everything that goes into that. They have 20 business plans from other folks in the incubator. To load those all in there and say, “Okay, show me the five common programs that these guys have done,” is really easy. You can only go so far with Control F; you can find specific mentions of things.

John Wall: 39:26
But if you’ve got something more ephemeral like concepts or opinions, this is a lot better way to comb through tons of data without having to actually read the stuff.

Christopher Penn: 39:37
If you have a large cohort of buyers—maybe you have 90, 100, 150 LinkedIn profiles—you could put them all in here and say, “Give me a hybrid buyer persona based on everybody that we know.” You could dump all your sales call notes from HubSpot and put it in here and maybe even separate them out. You would have one file called “Closed Won” and one file called “Closed Lost,” and you have all your notes for each of the prospective clients in each bucket and say, “Okay, I’ve got Closed Won and Closed Lost in my document store here. What’s the difference? What did we do that was different in the Closed Won bucket that wasn’t in the Closed Lost bucket?”

Christopher Penn: 40:27
Because the tool can handle huge amounts of data, it can identify those trends.

Katie Robbert: 40:33
As we’re starting to wrap up the conversation on the different use cases of NotebookLM, this is different from other generative AI tools where you would open up ChatGPT or Gemini or Claude and start writing something net new. This is a system where you would want to already have that data and load it in. You wouldn’t open this up and say, “Today, I’m going to write a blog post,” but you don’t have any background information on it. You could load up three years’ worth of your writing and say—well, I guess that’s the question: Is this the kind of system where, like, “Letters from the Corner Office”—that’s three years’ worth of writing—

Katie Robbert: 41:21
Could the prompt be, “Write a blog post from this?” Is that how this particular kind of generative AI works? Or is it purely query and research?

Christopher Penn: 41:31
You can do that. You can have it generate new stuff from stuff you’ve already given. You can’t open up a brand-new notebook and say, “Hey, today, this.” And then you follow the REPEL framework. You would use a purely generative tool that does gap analysis. This doesn’t do gap analysis unless you provide the gaps. In Gemini or Claude, you can say, “Here’s a blog post Chris wrote. What did he forget to include in the post?” The model’s long-term latent knowledge would go, “Oh, well, Chris wrote a post about attribution modeling but didn’t include uplift modeling because—his bad. Unless you had other content in here, it wouldn’t be able to do that because that knowledge wasn’t provided.”

Christopher Penn: 42:10
So that’s the advantage and disadvantage of a locked-rag system like this, where it can only answer from the data that you give it.

Katie Robbert: 42:19
Could you—for example, if we were to take a look at Marketing Over Coffee—could you load up past episodes of Marketing Over Coffee and a keyword list and say, “What hasn’t Marketing Over Coffee tackled?” Could you approach a gap analysis that way? Is this the kind of system you would use for that?

Christopher Penn: 42:42
Theoretically, yes. Theoretically you could do that as long as you provide—as long as you have the actual data for it.

Katie Robbert: 42:48
And that’s what I’m saying—if you had the data.

Christopher Penn: 42:52
Let’s give it a try. Oops, forgot to add my sources. Let’s add a source. Let’s add this keyword list from the Google keyword list provided in the sources. “What keywords from the list has Christopher not written blog content about on christopherpenn.com?” Referencing the output CSV, TXT. If you’re trying to get very specific answers, let it know what file it should be looking at because it does know the source names. Let’s see if this works because I know this works in Gemini. Nope, it can’t do it.

Katie Robbert: 43:50
Okay.

Christopher Penn: 43:51
You’d have to do it.

Katie Robbert: 43:52
That’s a good point: We’re talking about it like a research tool, research separate from analysis. Analysis is better suited for something like Google Gemini or Claude or OpenAI. This is more of the summarization of what’s happened versus trying to connect those dots.

Christopher Penn: 44:17
Right. And one of the advantages using a general tool like that would also be the semantics. You could say what concepts aren’t covered, whereas this tool would have a very hard time doing that.

Katie Robbert: 44:32
Gotcha.

Christopher Penn: 44:36
So those are the major use cases and how to get started with this tool. It’s a very powerful tool, and more importantly, it’s very easy to use, which is very nice.

Katie Robbert: 44:48
I know what I’m going to be doing the rest of the afternoon—unsolicited pitches to marketingovercoffee.com.

John Wall: 44:57
Coming up with unsolicited pitches.

Katie Robbert: 45:02
I’m going to take that notebook you just built of Marketing Over Coffee, take the worst, and repitch them and see what happens. I’ll see if maybe one of these days I can get a guest spot.

John Wall: 45:11
Shine them up to make it through the gauntlet.

Christopher Penn: 45:15
Oh, gosh, you folks. I would encourage people to try the free version with the understanding that the free version does use your data. Use the paid versions if you don’t want it using your data. If you’re a Google Workspace user, chances are it’s already been rolled out to you. Here’s how you tell if you’re using the paid version or not: If you use the paid version, there’s an analytics button in the top right-hand corner. If you’re not using the paid version, that button isn’t there.

Katie Robbert: 45:50
Makes sense.

Christopher Penn: 45:53
Any final parting thoughts?

Katie Robbert: 45:56
Like anything else, just try it out. Do your user stories—which we didn’t cover—but do your user stories. Do the 5Ps, and then just start playing around. You’re likely sitting on a lot of data that you’re like, “Oh, I don’t even know where to start with this.” Put it into a system like NotebookLM and say, “Summarize this for me. What is this? What do I even have?”

Christopher Penn: 46:20
That was it for this week’s show. We’ll see you folks next time. Thanks for watching today. 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/TI-podcast and our weekly email newsletter at TrustInsights.ai/newsletter. Got questions about what you saw in today’s episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai/analyticsformarketers. See you next time.

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.