So What? Generative AI for Content Personalization - Trust Insights Marketing Analytics Consulting

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn how to use generative AI for content personalization to improve your marketing. You’ll see how to tailor your content to different audiences, including individuals, and learn the benefits and challenges of content personalization. You’ll get a step-by-step process for using generative AI to personalize your content, and learn tips for avoiding common mistakes. You’ll also get a behind-the-scenes look at how Trust Insights uses these techniques to improve their own marketing.

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So What? Generative AI for Content Personalization

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In this episode you’ll learn:

What is content personalization, and how does generative AI help?
How to use the ICP to calibrate content personalization
Where you should and shouldn’t use generative AI for content personalization

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. Howdy, everyone. Welcome to the So What? livestream, the marketing, analytics, and insights live show. I am Katie, finally joined by both Chris and John all in one place. We got the band back together. They high-fived. It’s an exciting day, just in time for us to go on a three-week hiatus after this.

John Wall 00:55
Wonderful conferences. Get ready for rubber Chicken dinner tour.

Katie Robbert 00:59
Yep, Chris and I are. It’s event season as many marketers know, but that is not what the show is about today. Today, we are talking about generative AI for content personalization. We, as marketers, we’re always trying to make things personalized for our audience. Obviously, that opens up a big Pandora’s box of who is the audience? What do they want? When do they want it? How do they want it? Are we doing it? Do we do that? So, there’s a lot of things to tackle. So, Chris, welcome back. Where would you like to start?

Christopher Penn 01:34
Thank you. It is good to be back. I briefly caught a tiny snippet of last week’s show while I was laying on the floor in LA trying to get home.

Katie Robbert 01:46
So that is a story for another show.

Christopher Penn 01:49
That is the story. Or at the bar at one of the many events coming up. So, if you’re going to be at many events, come say hi to us. We do not bite unless requested. Content personalization is all about taking content and making it personal to someone. Instead of saying “Here’s a generic piece of content, a blog post, a podcast, a white paper, etcetera,” saying this is the idea you want to convey is: this is a piece of content that was made for you, Katie. Or for you John, or for you who’s watching this now. The first place we need to start is, huge surprise, why? Why would you even want to do this? Why is content personalization a good idea? That was not rhetorical.

Katie Robbert 02:33
No, I know. It’s funny. Total side note, my brain is sort of ping-ponging today because I have a lot of things that I’m working on. I, for those of you who don’t know, I don’t always answer questions immediately. I always take a second to think about it. And so, whenever I watch Jeopardy, I’m like, “I know the answer, but you gotta give me 30 more seconds.” And that’s just not how that show works. And unfortunately, we don’t have that format here either. So, let me go back to the point. That was my way of giving myself a little bit of time to get to the answer. See, that was pretty clever. Why do we want to do personalization?

Katie Robbert 03:08
Well, it’s interesting in this day and age, which is one of those phrases that we like to use in this day and age with all of the different ways that people can consume information, there is an expectation of personalization. So, if we as marketers are capturing data about our customers, if we’re asking them to give us information, then the expectation in trade is that, “Well, if I’m giving you information, then you’re going to give me exactly what it is that I need, not some generic piece of content that really doesn’t address any of my pain points.” And so, it’s marketers struggling to meet the expectations of their customers. And the bigger the company, the harder that is because you have such a broad spectrum of types of customers.

Katie Robbert 03:56
And so gone are the days where having the generic Personas are acceptable of, “Well, they are a male who lives in the northeast who’s between the ages of 40 and 50.” That’s not good enough anymore.

Christopher Penn 04:14
This is the reason we have such a problem with personalization and marketing these lovely black mirrors on these devices. We have gotten so used to not only instant gratification but personal service and deliveries, catering to our every need. You want a date, a car, a bottle of wine, whatever, you swipe on something and something happens, and mysteriously, somehow, it shows up at your house or place of residence. And that expectation as consumers bleeds over into everything else in life to say “I expect my marketing to be just what I want right now.” And the reality is, for those of us, particularly John and I, who have been in the Martech space before, it was called Martech—

John Wall 04:53
Yeah.

Christopher Penn 04:54
—those systems are not up to that challenge. I mean John, you’ve been in the Martech space just as long as me.

Christopher Penn 05:11
How long have you been trying to personalization?

John Wall 05:13
Oh, one-to-one marketing, Peppers and Rogers, what was that? It was in, like, 1997. They were like, “This is the future.” And here we are 30 years later and it’s still not being done everywhere. Yeah, there’s a bunch of problems with that. I mean, one is that it doesn’t scale. That’s just what it is. Every person has a bag of unique characteristics. Each person you add to the pile makes it worse. But the important thing, though, is that personalization is an expression of empathy. You’re showing that you know the audience and what they’re doing. And what happens is that always cuts the line when it comes to attracting attention.

John Wall 05:46
If you can prove that you know something specific about that person, like a great example is I have to email this guy today who is, a very popular speaker and he, like to get through his inbox is a challenge. But I know that he has canoed his whole life, and canoeing has nothing to do with his business. So, when I send him a subject line in an email of “canoe”, that’s going to get read no matter what happens today. And so, those kinds of things, you can garner attention, you can cut through the clutter if you can get personalization. But yeah, marketers have been lying about being able to, how to do this for decades and still not delivering.

Christopher Penn 06:21
So what do you mean, “Hello, first name” is not personalization?

John Wall 06:26
Or like, “Hello,” with that weird space in the comma too, because they, “Hey, friend.”

Christopher Penn 06:34
Which brings us to the first point on personalization, which is in order to do personalization, yeah, kind of need the data. You need data that can provide personalization but if you don’t have the data, you can’t personalize it. Now, in, I would say there’s like three tranches of personalization. There’s none at all. It’s like, “Here’s the content. Good luck.” There is sort of aggregate, which is where things like an ideal customer profile comes in. We would say, “Hey, this content is made for CMOs at midsize enterprises. This content is made for people who have supervisory authority over 50 or more direct reports.” And then there is personalization of “This is made for you, Katie Robert, and only for you. No one else will have exactly this kind of thing.”

Christopher Penn 07:23
What John was saying, which was 100% correct up until recently, the ability to do tranche three, literal individual personalization, anything more than “Hi, first name,” it was impossible because we simply did not have the tools to be able to remix content in an effective and intelligent way. I mean Katie, you and I have been working on natural language processing really since 2014 when we were at our previous company.

Katie Robbert 07:51
Yeah, and even that has its limitations because, so here’s what’s interesting. We, as marketers, even though—so Chris, you held up your phone to sort of demonstrate the expectations. We’re still only limited to the amount of information we can get. So, we can scrape a social media platform for conversations, but if our customer base isn’t active on social, then it’s not helpful. If our customer base isn’t someone who’s going to order a lot of things online, it isn’t helpful. We probably have a decent amount of customers who don’t do a lot of their life, whatever it is, online. And the only way to get to that is to talk to those people one to one, but that’s, as John mentioned, it’s not scalable, especially for a small business. It’s really difficult.

Katie Robbert 08:51
And so, we’re firmly stuck in the aggregate, broad strokes kind of, hopefully coming close to addressing the majority of our customers’ general pain points kind of personalization. And I don’t mean just us, I mean, marketers as a whole.

Christopher Penn 09:10
Exactly. So, let’s talk through how you would implement content personalization using generative AI. First at the aggregate level and then at the personal level. And we’ll walk through some live demos of how to do this, and then talk about the upsides and downsides, because there are, it is not without consequences. And I can tell you, one of the consequences immediately is what you talked about on this week’s Trust Insights podcast, Katie, which is about bias in AI and making assumptions about a person based on things like their gender.

Katie Robbert 09:50
It’s just as like a side note, so I recently reengaged my Lyft account. So Lyft, L-Y-F-T for the rideshare. The first thing that came up was, “We made assumptions about your gender based on your name. Help us correct this or not.” And I was like, “That’s a really dangerous game to play.” Now, granted, I happen to have a very American female presenting name, so it wasn’t difficult. But yeah, the bias is real. And I don’t think that a lot of people recognize how deep it could potentially go, but that’s a topic for another time.

Christopher Penn 10:31
That’s for another time. And if you’d like to catch the episode where Katie and I talked about it, go to Trust Insights AI podcast. That was Monday’s show. Alright, so let’s go ahead and switch over. We’re going to start in our good old friend Google Gemini again. As we always say, this applies to any foundation model. So, if you want to use Anthropic Claude, if you want to use ChatGPT, all of it’s totally fine. We’re going to start with our basic first step in the Trust Insights PARE Framework, which you can get at trustinsights.ai/pare. We’re going to ask the model what it knows about content personalization. This step, of course, prepopulates the language model’s working memory with all the information about, in this case, content personalization. So we want to say, what do you know about it?

Christopher Penn 11:20
What are the good things? What are the bad things? What are the things you think are true, but are actually false? What are the things that are false, but actually true? And so on and so forth. This is our first step to load up the model with context, with knowledge, and for us to also validate it, just as we look at it. Going by—going, is this actually correct? Does the model know what it’s talking about? Or do we have a problem if we don’t? If you don’t do this step and you just dive right into content personalization, you may be working with old knowledge. Now, I’ve done this a couple of times today already and this is reasonably up to date. I’m also going to change the output length because we’re going to be working on some longer-form content.

Christopher Penn 12:05
For those who are unfamiliar, there are two versions of every AI language model. There is the consumer version, which you will see in things like Google Gemini, where you have a friendly web app, chat, GPT, and then there are developer versions. It is my personal preference to use the developer version because I have more control over it. You do not need to.

Katie Robbert 12:29
I will. I don’t use it personally. While you’re pulling that up, Chris, I just want to address, we did get a question. “How do you find are the best way to combat that?” So, in reference to, if your customer base is nothing necessarily online, and so I said speaking to them one-on-one— It’s tricky. If they’re not in— Again, in this day and age, if they’re not, if they don’t have a strong online presence, you do have to find those other ways. So, direct mail, paper surveys, electronic surveys, phone calls, those kinds of things, those all still exist. Those are still valid market research tools and techniques that a lot of firms specialize in, and is still really great for capturing data.

Katie Robbert 13:18
So I would definitely not look past the tried and true methods for gathering data.

Christopher Penn 13:25
Yeah, there’s so much value in, it sounds weird, but like, talking to people, just saying “What’s going on?”

Katie Robbert 13:36
It’s strange.

Christopher Penn 13:37
It is strange. So, my first prompt is, we’re going to do this for the ICP first. So, if you have not caught our previous episodes on building an ICP, you can go get them at Trust Insights AI YouTube. I’ve said, “Let’s do some content personalization first. I’m going to provide you with the ideal customer profile for Trust Insights. This is a text document that Katie and I have put together, which details exactly who we want as a customer. I say, read through the customer profile and respond, ‘Okay,’ if you have no questions.” And, let’s see, the model says, “Okay,” it has no questions. So now we have step one, all the knowledge about content personalization. Step two, we have the ideal customer profile. Our third step is going to take a piece of content and do some personalization.

Christopher Penn 14:23
The content in question is, I took this— I have to start with this LinkedIn post. Now, this is a post that I wrote. It was me responding to someone’s question. It’s reasonably technical. It, for sure, does not address the needs of our ICP because it’s just a question about AI. So, part of personalization is, can you adapt content to actually fit those needs? So I’m going to go ahead and say, “Our next step will be to adapt this piece of content to suit the needs of our ideal customer profile. You should rewrite it to keep the main ideas of the content and the information in it, but ensure that it is useful and helpful to our ICP.” Let’s go ahead and load the content now. And this is our example piece here. And let’s see what it does.

Christopher Penn 15:35
“So how your business can leverage AI tools for advanced, enhanced data strategies. The rise of generative AI tools are changing the game. Benefits for data-driven business, key implications for your business, the future of AI and data,” and so on and so forth. Just at a very quick glance, this has done a decent job of taking what is a fairly technical, not so insightful for the ICP, post and turn it into, “Hey, here’s what this means for you.”

Katie Robbert 16:08
And, we’re not unique in the sense that, we’re drowning in subject matter expertise and content, and ideas, and things, and assets. We have all of those things where we and other companies struggle is to do this exact thing because we can talk about things in a more general sense, and it’s very easy for us to forget, “Oh, we’re not talking about it for our own sake. We’re talking about it for the sake of our customers. What do they need to know?” And so this, I’m already, my wheels are spinning of, “Alright, great. So, we have a whole lot of stuff that we could actually, probably do this for.” But, let’s get through this first example exactly.

Christopher Penn 16:55
Now, this is not good enough. As we’ve talked about in previous livestreams, particularly the livestream we did on automating email marketing, it is not enough to have a piece of—”Hey, I just rewrote something.” That’s a bad idea to say, “Hey, we’re done.” What we want to do now is we want to understand whether or not this is better. So, in that episode of the livestream, we talked about building scoring rubrics. I’m going to say next, “I would like you to score the original piece of content and your rewrite, using the Trust Insights ICP scoring rubric.” And I’m going to paste that in here, which is this gigantic thing that we built. In that episode, you can see the scoring rubric. “Is ICP content resonance? Is it relevant to the ICP? Does it, the content quality, and is it a good format?”

Christopher Penn 17:51
“Does it have the brand tone?” And so on and so forth. So, the original content, let’s see, scored 35 out of 100. Totally expected, because it was a LinkedIn post as me foaming in the mouth. The rewrite came in an 80. So, already, we’re in better condition. We’re going to say, “Great. Now, do a refinement of your rewrite to increase the score for the ICP.” Now, it’s going to— We’re going to do another round of revision here. This is taking what was a frankly not appropriate piece of content for ICP, revising it, and now revising it again to lead to fit those criteria. Scoring is so important because just like if you were talking to the intern, you say, “Hey, is this better?” The intern would be like, “I don’t know. I guess it’s better.” If you say, “Here’s a scorecard, score this.”

Christopher Penn 18:50
Now, tell me if it’s better. The intern’s like, “Okay, I can go down the checklist. Yes, this is better. The tone is better, the benefits are better,” and so on and so forth.

Katie Robbert 18:58
Well, and I think it’s important to talk about that context too, because if you said to me, “On a scale of one to five, is it better?” And you said the same thing to John, we’re still missing that context. So again, as Chris mentioned, you can go to our Trust Insights AI YouTube and go to our “So What?” playlist to find out more about building that scoring rubric. It’s not just a simple, “Okay, on a scale of one to five, is it better?” But it’s actually an in-depth analysis with a lot of context of what the scores actually mean. And what I like about that is exactly as you’re saying, Chris. It takes that uncertainty out of it because depending on who you ask, you’re going to get a different answer of, “Is it better?”

Christopher Penn 19:41
Exactly. But the machine can make it better with good prompting and an ICP. That’s thorough. “So here we have refined content, unlocking data-driven growth. Our enterprise leaders can leverage AI tools for enhanced customer experiences and operational efficiency.” So, it’s got a better introduction now. “Specific benefits for enterprises like yours, key actions for enterprise leaders, futures to data-driven growth.” And, of course, the call to action. “Are you ready to explore how AI tools can transform your data strategy? Contact Trust Insights today for a personalized consultation. Go to trustinsights.ai/contact.” Sorry, carried away.

Katie Robbert 20:17
I have a question, though. So, it took it from a score of what, 30 to 80?

Christopher Penn 20:22
From 35 to 80, and then 80, 95,

Katie Robbert 20:26
No, I understand that. My qu—but that’s not my question. My question is, how true to the original post is this now? And, or has it changed so much that the integrity of your original post is lost? And would it have just been better to say, “This is our ICP. Can you write a post about what AI tool leaders should be thinking about?”

Christopher Penn 20:53
So, this is, and this where I have to get out of my own way. This would piss me off because there’s an insufficient amount of technical information about that was in the original post. There’s a lot of things omitted about, like, building fluency directly into a model to understand tool calling. But, our ICP doesn’t care about that. It doesn’t help them. And so, it retains enough of the concepts like the tool belt idea, where a language model can just pick up a tool and use it, but it no longer has things that would make me happy. Now, I am not the ICP. When we look at the details, it says, “AI tools can break down data silos.” Yes, that is true. It did hallucinate the numbers there. “The streamlined operations, AI tools in this context,” these are correct statements.

Christopher Penn 21:52
“API-first approach, prioritizing tool integration.” These are all correct statements. So, the essence of the original post is still there. I would find this deeply unsatisfying. Chances are our ICP would not. So, from a content personalization perspective, this is much more personalized for our ideal customer profile.

Katie Robbert 22:12
John and I talked about this a lot on our livestream last week, is that the trap that we as marketers fall into is with that ego, and that it’s about us, it’s our expertise, it’s our experience, and it’s really— We have to provide that information. But, if we’re writing it for ourselves, whether we realize it or not, we’re doing it wrong. And the thing that I really like about using the ICP as the foundation, is it takes us and our ego out of the conversation as much as humanly possible. And so, obviously, Chris, you would need to be the one to edit this. And I’m sure, knowing you, would try to add some of that technical expertise back in. But, it’s not about—you’re absolutely right, it’s not about you. It’s not about what you want.

Katie Robbert 23:04
Nobody cares what you want, Chris. Let’s just put it out there. Nobody cares about you. We care about our customers.

Christopher Penn 23:10
Well, it’s true. That is, in the context of a business, that is and should be the rational, ground-level truth. If you’re not serving your customers, you’re doing it wrong.

Katie Robbert 23:19
And unfortunately, that is not how a lot of people operate. Again, whether they realize it or not, they’re like, “But, the business is built around me. I’m the idea person. I’m the thought leader.” Nobody cares.

Christopher Penn 23:31
Yeah, nobody cares.

Katie Robbert 23:32
Nobody cares.

John Wall 23:33
Well, it’s that mismatch between—because we’ve all seen this as marketers, when you’re with a senior executive and they want the writing to be right. They want it to be what they want it to be. And, what we’re pointing out here is the reality that, if you find the copy annoying and a little bit below you, that’s actually where you want to be. You should not be loving the copy because, if you love the copy, you’re missing the audience.

Christopher Penn 23:54
Exactly. So, we have essentially built a methodology here for revising the ideal customer profile— A revising piece of content against the ideal customer profile. Now, how do you operationalize this? Because, this is a fun exploration but what we’ve just done is relatively inefficient. You would not want to do this same exact process, which took us 14 minutes, over and over again. Now, granted, it’s faster than perhaps rewriting itself, but it’s not efficient. It is not. It is not a clean, good thing to do. So, what we would want to do is we would want to convert this into system instructions. System instructions is a way to tell a model, “I want you to come up with a recipe,” really, for performing the same exact set of tasks, over and over again. So, I’m going to start off with the starter.

Christopher Penn 24:51
I’m going to convert this entire conversation into a prompt to be used with large language models like Google Gemini. Format the prompt as system instructions. The purpose of the prompt is to instruct the LLM to take a piece of content from a user, along with a user-provided ideal customer profile and then follow these steps autonomously. The autonomously part is important. We want the machine to do this for us. We don’t want to have to be doing this over, and over again. “Write a new draft of the content that is personalized toward the ICP. Apply the ICP scoring rubric against the content, both the original and the new draft. Select a winning piece of content based on the score from the rubric, refine the winner to increase the score the refinement, and then present the top-scoring piece of content.”

Christopher Penn 26:17
So, those are the five steps that we want this thing to do. We want to incorporate everything we’ve talked about so far about what mistakes to avoid with expert practitioners. So, we are turning this into system instructions. Now, there’s a good chance that we’ll have to maybe do some tailoring on this to make it a little more fine. But, what we’ve got here is so far looking pretty good. We have our system instructions. We have our details. There’s our ICP scorecard. Now, obviously, you will want to substitute your own. You shouldn’t use ours because ours is tailored for our ICP.

Katie Robbert 27:02
Well, I mean I would say if you are looking for your own ICP, you can go ahead, Trust Insights AI contact, talk with our very own Chief Statistician, John Wall, and he can set you up with our ICP building services, because that is something that we do for other people besides just ourselves. And, it’s funny Chris, as you were doing this, as you were putting together the system instructions, we’re like, “Wow, this is like my Christmas right now. This is all I want, is, sops, process, system instructions, repeatability.” It makes me giddy. I am so happy with this right now.

Christopher Penn 27:42
We’re not done yet, but first, a question came in from Richard asking, “Are we asking you to do too much at once?” Ordinarily, I would say yes. However, Gemini, in particular, is very good at instruction following. And you will note in the way that we’re prompting this, we are writing code. We’re not writing it in C or Java or Python, but it’s code. This is code. This is giving steps in a structured format through the language model to execute, just as you would write a Python script and execute in a Python development environment. As long as the model returns results that you actually want, then no, you’re not asking it to do much.

Christopher Penn 28:26
Where you will run into trouble is, when you execute the instruction, the system instructions, and it doesn’t work and it’s like, “Oh, well, I guess I’m asking it to do too much now.” There used to be a really complicated, hard set-up process for making this into something actual. As of this past week, that’s gone. We’re going to go over to Google’s Gemini, the consumer edition, and you’ll notice, on the lower left-hand side, there’s the Gem Manager. Gems are Google’s version of GPTs. So, if you are a used OpenAI’s GPTs and built a custom GPT like KDGPT we talked about in the past, go into Gem Manager, you can see KD Gem exists here. Sounds better than KDGPT, sounds more fancy.

Christopher Penn 29:19
And I’m going to call this the Trust Insights Content Personalization ICP Gem. Those system instructions that Gemini just manufactured for us, copy, paste.

John Wall 29:36
I was not expecting the magic Wanda boop.

Katie Robbert 29:41
Exactly.

Christopher Penn 29:42
And now I have a gem that will perform the exact same task. So, say “Here is the content in the ICP.” I’m going to click into our uploads, I’m going to choose our ICP and our content. Whoops. You do that from the chooser itself. Example content and ICP, and it will start to execute the system instructions that we gave.

Katie Robbert 30:13
Now, is a Gem similar to a custom GPT where— So, one of the things you can do with a custom GPT is give it some starter questions. And so, it’ll say, “What do you want to do today?” Or, whatever you decide, could you do the same thing here, where the first thing it asks you when you pull up the Gem is, “Can I have your ICP and example content?” And sort of, in some ways, you’re kind of bypassing, the, “Oh, where’s the instructions? What am I supposed to be doing?”

Christopher Penn 30:47
You could, you would do that in the system instructions itself. So, you would say, step, instead of content personalization, step one, “Ask the user, ‘Hey, what do you want to work on today?'”

Katie Robbert 30:57
But, in this particular instance, we know. So, it would say, “Ask the user for their ICP and their content.”

Christopher Penn 31:05
Yes, you could absolutely do that. The thing that you want to do is be specific. What questions? Again, essentially, it’s programming. So, you’d want to have those exact instructions upfront, which is, I mean, that’s—

Katie Robbert 31:24
A really handy thing. And that’s sort of the—this to me is the automated version of an SOP, a standard operating procedure, because that’s something that internally we talk about a lot is, “Do we have a set of instructions so that this is a repeatable task so that you can get the same result?” And, or somebody who isn’t, you can pick up the task and run with it. And I’m already—again, sort of earlier where my wheels are spinning—now, they’re spinning even faster, of, “We can automate so many of our SOPs to make sure that we’re getting prompted for the prerequisites, and then the system instructions picks it up from there.” That, to me, is the epitome of efficiency.

Christopher Penn 32:14
So, now this Gem using Gemini has done exactly as instructed. It has done the original. It has done the revision, has done the refinement, and it has spit out this revised piece of content and explained how it did its work. And, if I wanted to—so, these can’t be shared yet. I imagine that is on the way—but, I could put the system instructions into the Trust Insights Google Drive and then any Trust Insights employee who’s using the Gemini workspace can just literally make the copy, make the Gem, copy, paste the system instructors, and boom, they’ve got their version. When sharing becomes available, which I expect any day now, then you won’t have to worry about things like version control. But, this would be level two of personalization. Level one is none whatsoever. Level two is personalizing content against an ICP.

Christopher Penn 33:09
Let’s talk about personalizing number three at level three. Personalizing for a specific person. Great. “Next, we want to personalize a piece of content for a specific individual. I’m going to provide the individual’s information now. You can use whatever data you’ve got. It could be from your CRM, it could be from LinkedIn, it could be from Facebook, it could be from the hellhole formerly known as Twitter. Whatever information you’ve got, you can put in here. Respond, ‘Okay,’ if you have no questions.” And this time, we’re going to preload the LinkedIn profile of the Trust Insights CEO.

Katie Robbert 34:01
Oh, hey, that’s me. People might be wondering, “Well, what’s the use case for this?” Think about, having to put together reports or presentations for someone in upper management that maybe you don’t know that well. Or, let’s say, you’re doing a new business pitch, but you know who the person is that you’re pitching to. Personalizing for that specific person is so important because that’s your audience. It’s an N of one. And, to really— As John, you were giving the example earlier of, you have to reach out to someone but you happen to know that he’s into canoeing, so you can use that information to personalize to him specifically.

Katie Robbert 34:48
But, if you then also sent that same email to someone, like, Chris, for example, who maybe isn’t into canoeing, he’d be like, “Why is he mentioning canoeing? That doesn’t apply to me.”

Christopher Penn 34:59
Exactly. So, the more information you have about the person, the better. So, you can see here, this is the—I told it to infer the needs, pain points, goals, and motivations of Katie Robert as an ideal customer profile of a single person. So, this is now, Katie, this is literally and says, “Your goals: grow Trust Insights through a leading data and analytics company, drive innovation, empower business, promote diversity and inclusion, make a positive impact on business and society, intellectual curiosity, building a successful and sustainable business.” I mean, none of these things are incorrect.

Katie Robbert 35:33
No, they’re not.

Christopher Penn 35:35
Great. “Personalize this article specifically for Katie to address her needs, pain points, goals, and motivations.” And now, we’re going to start with the exact same example content, my LinkedIn post. So, this is what John was talking about at the top of the show. This is true personalization for the N of one. So, Katie, “As go ahead, no, as CEO of a rapidly growing data analysis company, you’re undoubtedly aware of the transformative power of generative AI. But are you fully leveraging its capabilities to scale Trust Insights, attract top talent, and stay ahead of the curve?”

Katie Robbert 36:25
No. I’m guessing that’s what they want me to say, because otherwise, there would be no content.

Christopher Penn 36:34
Exactly. “The evolution of AI tools, what tool handling means for Trust Insights? Imagine having an AI model that can seamlessly integrate with your existing CRM systems to personalize customer outreach, analyze marketing campaign performance data, or automatically generate reports summarizing key business insights.” And then, of course, you can see similar things. But, this is tailored specifically to you and your needs. So, this is not just a broad, generic ICP. This is what you at Trust Insights, how you at Trust Insights might find this information useful.

Katie Robbert 37:11
I can see this being a— John, get your pencil out, I got some to-dos for you— I can see this being a really powerful sales tool. If you are targeting a specific company or a person, you want to get their attention, why not create something tailored exactly for them? I mean, I donI don’t know of a better way to get someone’s attention than to give them things that they actually need.

Christopher Penn 37:40
Yep. Now, suppose you have 100, 200, 301,000 customers, high-value customers, and we have—our mailing list is what? If even 1% of those people are high-value prospects, that’s 2,800 people that we might want to personalize content for. That’s not going to go well for John if he has to copy and paste this 2,800 times.

Katie Robbert 38:09
You better get your pencil sharpener out, John.

John Wall 38:11
It’s a lot of Ctrl+C, Ctrl+V here we go.

Christopher Penn 38:14
Yeah, we don’t want to do that. Here’s where it gets interesting. In the developer version of all the major platforms, there’s usually a “get code” button and you can take the code, and it contains the session so far and the instructions for how to execute, in Python code. You would then work with your IT team, developer team, to integrate your data store—like, your CRM, for example—and this set of prompts to mass generate individual one-to-one personalized content. And then, again, with your developers, work that to, push that back into the CRM so that it’s automatically sent out by emails. That’s so— Today, with today’s language models, you can do true, one-to-one personalization where it’s mixed exactly for that person. With two caveats.

Christopher Penn 39:07
Number one, you are sending AI output directly to people, without a human reviewing it first, which may or may not be okay with you. That’s up— That’s your personal policy. Number two, it is very expensive. When you execute this code loop of having it build these things, you’re probably going to rack up a bill for, say, 1,000 contacts, about $10. “Yeah, it’s like, okay, whatever, $10, that’s nothing. We spend more on cream cheese for a meeting than that.” Yeah, but now, do it every day to the same thousand people or do it to 10,000 people or a million people, and suddenly that $10 bill gets real big, real fast. So, those are the two major caveats with content personalization. If you use generative AI, it can be risky, and it can get very pricey, very quickly.

Katie Robbert 40:08
Money aside, because obviously, we can’t afford something like that. We’re a small business. So, John, you have to put on your carpal tunnel supports and get to copying and pasting. I personally would not be comfortable with sending it directly from AI because, even in the example that you gave, Chris, of the content you wrote and then it rewrote to be more aligned with the ICP, you are pointing out where it was hallucinating information. And I think that’s something that we tend to overlook. It’s like, “Oh, but it got me 90% of the way there. What’s a little hallucination amongst friends?” Well, you are working on the information that you have about the individual, and AI is sort of filling in the blanks of the rest.

Katie Robbert 41:00
And, my goodness, what happens if it fills in the wrong blank and you say something incredibly, I don’t know, insulting, racist, misogynist—

Christopher Penn 41:14
Should go shoe shopping.

Katie Robbert 41:15
Right, but. Exactly. And, on a previous episode, that is the example that it gave. It was, it took my name, inferred my gender, and then assumed some stereotypes about me, which were completely incorrect. Now again, harmless, “harmless stereotypes” about shoe shopping— That’s why I put it, sort of, in air quotes because it’s not actually harmless because it shouldn’t be doing that at all. But, that’s still going to happen. So that’s my little mini-soapbox about, you still need human intervention. As much as you want to automate this, do not 100% give full autonomy to the AI to say, “Oh, yeah. I don’t need to review it, just go ahead and send it.” If you would not do the same with a person, don’t do that with your systems.

Christopher Penn 42:04
I would say if you do automate it, there is a way—and maybe this is a topic we can cover on another episode someday—of integrating a sensitivity reader into the process. An automated sensitivity reader, which will say— “Hey, you’re going to look for text that is implicitly or explicitly racist, misogynist, bigoted,” whatever, and programmatically return it to the model and say, “Try again until it passes a sensitivity test rubric.” However, that effectively doubles your processing costs, possibly more than doubles because now you, instead, have one language model doing work, you have two. Because they are going to act in an adversarial way, and they may go bounce back and forth. You will get safer output at double the cost.

Katie Robbert 42:50
Right. Now, we had another question from Richard, which, you could probably even, not so much a question but a statement. You could probably even make it sound like it was coming from a marketing rep rather than a form letter, too. One of the things that I know we didn’t cover in this episode, but we have in past episodes, is we very quickly showed “Katie Gem,” “Katie GPT,” all based on my personal writing. And so, we have actually internally created a process where content is rewritten using my tone, my techniques. I still personally, as a human, edit it because I know what I wouldn’t say. But, it gets it farther along and it very closely resembles the kind of writing that I would put out, the kind of, the way that I would communicate.

Katie Robbert 43:39
So, that is another layer into that content personalization is having it from a specific person on your team, but you have to build those system instructions as well.

Christopher Penn 43:52
Exactly. But to your point, you could include instructions in the Gem saying, “You must also upload the user’s writing style.” So now, we have instructions to say, “Here’s the writing style. Here’s the ICP. Here’s the content. Here’s the scoring rubric,” and so on and so forth. You can see that you’re building an ecosystem in which the language model is the processor, but it’s not the originator of the content, it is just the engine to transform it.

Katie Robbert 44:19
It actually would make a lot of sense to revise those system instructions with your brand guidelines, things you do and don’t say. I mean, that sort of— Again, we didn’t cover that in this episode. And so, if you don’t have a specific person on your team that you want it to sound like, you could, at the very least say, “These are our brand guidelines. This is our tone. This is our mission, vision, values,” that gets you farther along down the line to make sure you’re speaking in the way that would be well-represented by your brand.

Christopher Penn 44:51
Absolutely. In fact, we have a client that we work for, where I write content for them. But then, I use their brand guidelines and their writing style guides and their brand safety guide, and I say, “Now you have to clean up my writing so that it conforms strictly to these brand safety guidelines and stuff.” And so, what comes out— You know, in our correspondence with the client, they’ve been like, “This is great. This is exactly what we’re looking for.” Like, yeah, I know, because I told the bot to take my original writing, human-led writing, but make it conform to the requirements that it has to be brand safe. So, that’s content personalization at three different levels. Number one, none at all.

Christopher Penn 45:28
Number two, based on a segment or a Persona, using generative AI, and that’s something that you can scale with things like Gems or GPTs. And then, number three is personalizing to the individual using an agentic approach with code. The more personal you get, the more technically complex the system is going to be, or the more likely it is you’re probably going to end up buying an extremely expensive service from a vendor to implement it. But, I would be very cautious about that because, again, AI content is going to, presumably, important customers. If you’re taking all the time to personalize, you might want to make sure it’s correct.

Katie Robbert 46:11
I would add, sort of, my final thought, and something we didn’t touch upon at all, is personally identifiable information. We are very much skirting, if not getting into that arena of—as we’re trying to personalize, for an N of one, you have to have a decent amount of identifiable information. And so, definitely, if you’re building your own, make sure you understand what PII actually includes. If you’re buying software from a vendor, make sure they are clear on data privacy regulations and including that in the software. And if they make you set it up in your own way—again, you can’t get around the data privacy aspect of it—the more personalized you want it to be, the more you need to be careful about data privacy.

Christopher Penn 47:04
Exactly. And you can catch more about this on this coming week’s Trust Insights podcast episode, “Responsible AI Part.” Katie and I actually do talk about responsible AI and data privacy. So John, any final thoughts?

John Wall 47:18
I have to give a shout-out to Richard there. He just called out Microsoft Clippy, which, any mention of that is, we are far beyond the days of Clippy mail merges, which was the most painful thing I’ve ever been through in my life. It’s funny, I was just thinking about that. I’ve taken for granted that, when I do a merge, it doesn’t crash my computer. I don’t have to restart every time I’m doing some kind of—you don’t—

Katie Robbert 47:37
Have to reset all your macros.

John Wall 47:39
Yeah. Flush the cache and optimize my hard drive so it doesn’t light on fire.

Christopher Penn 47:46
Get off my lawn. Alright, we will see you all next time. Thanks for watching today. Be sure to subscribe to our show, wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at trustinsights.ai/tipodcast, and a 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 Trust Insights AI, analytics for marketers. See you next time!

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