My guests today are Colin Eagan and Jeffrey MacIntyre. Although they work for different companies, Colin and Jeffrey share a common focus: how information technologies might offer more personalized experiences. They co-authored an article on the subject for A List Apart and Jeffrey gave an excellent presentation based on that material at this year’s IA Conference, which led to this interview.

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Transcript

Jorge: Jeffrey, Colin, welcome to the show.

Jeffrey: Thanks for having us.

Colin: Hey there. Thanks for having us.

Jorge: I’m very excited to have you. Well, Colin, you and I have not met before, but I had the great privilege to meet with Jeffrey at the IA Conference this year where he presented a framework that we’re going to be talking about today. When I saw that presentation, I thought, “I need to get these folks on the show because this is really interesting stuff that our audience will want to learn about.” But before we get into it, would you please introduce yourselves? Why don’t we begin with you, Jeffrey?

About Jeffrey and Colin

Jeffrey: Sure. I often describe myself, flippantly, as a card-carrying personalization optimist. And we’ll get to the “card” part of that title later. I’m a long-time independent consultant with a background in information science, product, and content. My practice, Bucket Studio, is a nano practice. I pursue high-challenge, high-interest projects and work across many sectors. My clients include Amazon, MailChimp, IBM, Consumer Reports, and I do a mixture of product design, information architecture, and experience optimization, otherwise known as test-and-learn experimentation, CRO, that sort of thing. That’s me.

Jorge: And is that practice focused on personalization? Is that a fair take?

Jeffrey: Yes. So, my go-to market is really around simplification. There are many different ways to solve complex products in various domains. Personalization is one expression of your content. It is sometimes a good path to go down, but not exclusively. And I think Colin will vouch for me when I say that we are very experienced in saying no to clients and prospects regarding personalization questions because it’s often an ambition and rarely the successful outcome they think it will be.

Jorge: If they see you as a person wielding the hammer, they’ll come with all sorts of problems and say, “Hey, hammer this.” I would expect that would be the case. But that’s a good segue, Colin, to hear about your background.

Colin: Yeah, thanks for having us. So, I’ve worked in the UX field for close to two decades now, primarily on the consulting and agency side. So Jeff and I have different day jobs, but we actually got to know each other through a think tank called the Consortium of Personalization Professionals, which is an outside working group where practitioners in and around personalization—from the design side, the UX side, the content side, as well as tech—get together to share experiences. So, we got to know each other that way, which has been great. But for my day job, as I said, I’ve been a UX generalist for a long time. I have certainly worked in IA capacity, content strategy capacity, and lately digital strategy. I’ve worked on a lot of big redesigns, including IA for large sites over the years. You guys would have heard of UPS, Lowe’s, and some big associations like AARP and AAA. Lately, a lot in the health payer space. United Healthcare is a big client of ours, and over the years, government agencies and associations.

But one of my passions for probably the past, oh man, close to a decade now, has been around personalization. And in particular, as I said, as designers, how can we approach and structure personalization asks of us, right? When the client says, “Here, go make the thing personalized,” in a way that is both intentional and ethical and improves everyone’s experience. So, Jeff and I have been trying to solve that problem for a while now. We haven’t cracked it yet, but that’s our background.

Jorge: We’re about to launch into talking about personalization, but I think it’s a good reminder to start by saying that this is one approach among many and something that can be used mindfully. And hopefully, we’ll dive into what that means.

Personalization, Customization, and Automation

Jorge: As we dive into it, I guess the first question, the place to start, is what is personalization, right? Because that might mean different things to different people.

Jeffrey: Yeah, it’s a question that we get a fair amount, and it depends on the audience, is what I want to say. I think my broadest answer is it is any change or expression of an experience that is tailored to a specific audience. Now, that’s a very broad and very vague answer. We’ve got answers from Forrester and other sorts of places where they break down components of it, but to me, it’s a spectrum that goes from customization to personalization to automation, and those are just different flavors of content expression. But the underlying definition for me is just the tailoring of the content, information, and data that’s exposed to a user.

Jorge: You mentioned three distinctions there: customization, personalization, and what was the third?

Jeffrey: Automation.

Jorge: Automation. I had heard the first two, and I’ll say this: sometimes I bring that up with students. I’ll say there’s a difference between customization and personalization, and I think that distinction might not be clear in a lot of people’s minds. It might be worth unpacking the differences between those three.

Jeffrey: Yes. And as I break it down, it’s helpful to just put a tack in the fact we’re talking about designing for these states. So when we talk about customization, the quickest way to familiarize somebody with what that means is to go into any given software product that you use, log in, and go to the system preferences or settings page. There will be options there that are customization functions. I want a newsletter. I don’t want the newsletter. I want this kind of newsletter. Here’s my avatar. This is my given pronoun. All of those things are ways that you have agency as an end user to tailor the presentation of your experience and how others see you in that product setting.

In personalization, inferences are made either by machines or humans with business logic or algorithms that then determine and shape your experience. Those same decisions get made, but not by the end user. The trade-off is supposed to be a mixture of serendipity, speed, and just delight in progressing a user to the outcome that they desire.

And now, with automation, it’s just a slightly different flavor where you’re taking any given series of steps in a user flow and shrinking it down. So let’s say it takes eight screens to do an onboarding, but there’s a guest mode that, in e-commerce, there’s often a guest mode. Instead of registering for a site, you can just quickly bypass all that registration and go straight to the transaction. That’s an example of a foreshortened user flow. Oftentimes there’s an opportunity to fully automate those things and make them pushbuttons, such as the one-click buy that Amazon made famous, but there are others as well. I think it broadens out the definition of what we’re talking about because sometimes people really zoom in on personalization, which entails a lot of other needs of the data.

Jorge: To read it back to you, what I’m hearing there, and this kind of corresponds to my mental model, but I just want to make sure I’m hearing it correctly: Customization is when the user is doing it, like the user is tailoring the experience based on their preferences. Personalization is when the system is somehow reconfiguring itself based on what it thinks it knows about the users. And then, what I’m hearing about automation there is that — now I’m going to go out on a ledge here — as the user goes through the experience, there might be things that you can pick up from their interactions that would help with either of those two things, with either personalization or customization, to expedite the process. Is that fair?

Jeffrey: I think that’s fair. Colin, what do you think?

Colin: Yeah, no, I think that’s the idea, right?

The Personalization Pyramid and Data Inputs

Colin: The way we have described it—and we’ll reference this or get to this—but Jeff and I made a deck of cards for personalization called the Personalization Pyramid, which is a framework for designers to structure personalization. The way that we define it is the practice of improving a user’s experience based on data inputs. So that is what it is, right? It’s about figuring out how to optimize all those data points that are coming in, right? It could be in real-time; it could be over a lifetime, right? And personalizing some type of content.

And in this case, Jorge, we are talking about digital content for the most part, right? Because Jeff and I joke that if you type “personalization” into a web browser, you still get “customize my Christmas card,” right? That’s still like things you’re going to put in the postal mail. But in our case, as designers, we are in the digital realm. But yeah, overall, that’s the premise, and it’s a broad waterfront, of course. But it’s certainly interesting to see how it evolves and how the focus changes as time marches on within that.

Jorge: My sense is that the phrase “data input” is worth unpacking there, right? Because, like, I remember one of the first times that I was exposed to the idea of what you might think of as personalization was reading a business book many years ago. And the thing is, I don’t even remember what book it was, but there was a case study—I think it was about the Four Seasons hotel chain—and the CRM system they used to remember things like what kind of pillows someone likes, so that the next time they stay, they have the right pillows set up for you so that you don’t get an allergic reaction or what have you. And that would be an example of personalization IRL.

You made this distinction—like we’re talking about digital experiences here, which I expect to be different in quality from in-real-life experiences by the nature of how dynamic the digital medium is, right? Like the entire thing can be reconfigured on the fly. With that in mind, I’m wondering how the differences between customization and personalization play out in a digital environment as opposed to these in-real-life personalizations that you get when you engage with a really high-touch service.

Colin: I can take that one, Jeff, and then I’m curious to get your thoughts too. It’s a double-edged sword, right? Because, as you say, Jorge, we do have this power now to personalize in real-time, which can be dynamic. But that means you could have a really great dynamic experience or you could have a really bad one right at the flip of a coin. So, with this sort of power comes responsibility.

To get to your first point about the pillows, which I actually think is a great example, and we still use that reference in talks, you can think of personalization as a spectrum. Jeffrey and I sometimes show this visual: there’s a personalization spectrum. If you think of it like a curve starting at the left at the bottom and then going up on the right. On the far left, you might have basic personalization, what we would call basic—that may be more general or at a segment level, right? Not necessarily individual, but still personalizing based on something we know about you, right? Or a segment that you’re similar to, or a cohort, or that type of thing.

And then all the way at the extreme on the right, as you go all the way up, you have what’s sometimes called individualization. The pillow example you gave is a good one of that, right? That’s very specific to “Jorge wants this type of pillow when he stays with us,” right? That’s an individual premise. You’re not a segment of people who use those pillows. That’s you, that’s your pillow. Sometimes you’ll now hear the term “hyper-personalization” get thrown around a lot, right?

So it’s not enough anymore to say, “How are you personalizing?” It’s to what extent. But to answer your question about what’s the spectrum there, all of those are tools in the toolbox, digitally, that you can use from segmentation all the way up to collecting first-party user data on the other side to really adjust or fine-tune an experience in real time. But Jeff, what am I missing from that?

Jeffrey: Oh, just to complement that, I think one thing that I find myself saying very often is that we do design in multiple systems of record. When we’re designing for personalization or automation in particular, we could be working in a CRM, a CMS, a DAM, a product information management suite, or a marketing automation platform. There are lots of software categories where there are already very robust features and functionality to automate or pull data and then express an experience based on that data and information or inference that you can make.

Now, this is why we often talk about connected experiences. It’s really down to the one customer experience that Jorge has with the Four Seasons at that locale at that time, but it requires the CRM to be speaking to the individual bellhop’s mobile device. Maybe he’s using a kiosk to make sure they can order that pillow a week in advance or whatever the case might be. Now, that’s obviously a little pedantic, but you get what I’m saying: all of these acts of design really require us to be thinking outside any given one system. So it’s not just about CMS-driven content, and it’s not just about DAM assets. It’s really about threading a needle.

Challenges and Ethics in Personalization

Jorge: I’m glad you brought up the systems of record thing because when I hear that the improvement of the customer experience is happening through a mindful use of data, where my mind goes is you probably cannot successfully design an experience that is intentionally at some point along that spectrum if you don’t understand the data that you have to work with and the capabilities of the systems that are going to be delivering the experience. To what degree is familiarity—and I’m talking now like firsthand pragmatic familiarity with the backend and frontend systems—important to doing this kind of design work?

Jeffrey: It’s really crucial, and this is why I talk and practice so much test-and-learn stuff. Experimentation is really valuable, and having even just a basic familiarity with statistical significance and what it takes to set up a hypothesis and then test that. Even if you’re using freemium software or MailChimp or what have you, just making sure that you are gathering data and understanding how automation or personalization is performing is really the stepping stone to figuring out, “Is this working? Is it working at a certain scale? What can we tweak or do to enhance this?” And so measurement is always front and center when we’re talking about personalization.

Colin: Yeah, and I would say, just to add to that, I would say, Jorge, the good news there is, to Jeff’s point, it doesn’t have to be terribly complicated to get started with personalization. Right there, there’s actually been a shift back in the industry, I think, that we’re seeing, which is a good thing, towards first-party data as opposed to the more convoluted ways of trying to deduce what we know about a user from third-party data sources that one might purchase, for example.

So I think there really has been this move back to now, and couple that with, we see every year the rising customer expectation in general that a good digital experience will have some degree of personalization to it. Designed well, it’s typically improving the experience, and they do surveys year after year. There are a couple of good ones, actually, that we can reference for you that I know Jeffrey and I always look at before we give a talk to see what the latest is. Now I think over half of consumers, when you ask them, say they will become repeat buyers after a personalized experience, and that’s up something like 7%, right? Like year over year that continues to go up.

There basically, there’s that rising expectation. But you could have something as simple as a short quiz or intercept survey when you’re getting to know a new user on a site and an experience, right? That done properly and isn’t too intrusive, could really give you some great first-party data that you could then use to customize in a meaningful way. And that’s not terribly tech-intensive. Now there’s the extreme of that, and we can get into the AI piece and the ML piece if you want in a bit, but obviously now there’s a lot that’s being done in terms of AI-driven personalization for orgs to drive the growth in their businesses. And that’s changing every day, isn’t exciting? But that part also gets a little scary.

Jorge: I definitely want to go there. I wanted to say, with regards to the technology, when you were talking about the spectrum going from very basic to highly individualized experiences, it’s easier for me to come up with an example for the kind of basic side of things. The example that immediately leaped to my mind is my own website. I have, I think, two lines of CSS code that flip the site into dark mode if the user has set their operating system to be functioning in dark mode. And I consider that to be like the most basic thing I could do to tailor the experience to the user. I don’t know anything other than this person prefers dark mode on their OS.

On the other side of the spectrum, I think that many of us have had the experience of, I think the way that you talk about it in the A List Apart article is, being implored to buy additional toilet seats. And you use this word in the article, overfitting. I’m wondering if, first of all, if you wouldn’t mind unpacking this concept of overfitting. The reason I’m bringing it up in this context is I’m wondering if there’s a point at that end of the spectrum where it’s like you’ve gone a bridge too far and you’ve suddenly spoiled the thing. Like maybe there’s a sweet spot where like that 7% is driven by, “Hey, these folks really understand me.” Whereas it’s no more toilet seats. More toilet seats! Now you’ve turned me off, right? As a consumer. Can you speak to this overfitting idea?

Jeffrey: Yeah, it’s really easy to do. The tooling for doing this kind of work is so far ahead of the design practice, and this is fundamentally one of the reasons we created the deck of cards five years ago, is to try and bring it back to some basic grammar around this is the kind of math you’re doing. Because overfitting happens when you are making too narrow an inference about a user.

So you bought a toilet seat in the last 30 days. Therefore, you like toilet seats. Not true. Therefore, you’re a contractor and you buy toilet seats monthly. Do you have any data to back that up? No. But is it easy to fire off that retargeting rule? Yes. And so there’s different ways that organizations can ship their org chart today.

It’s not just having a poor navigation. You can have poor campaign logic and poor practices that really drag your CX down, drag brand affinity down, and really sink your boat before it’s sailed.

Jorge: I can see that. Like the only data point that you have is, this person likes toilet seats, so it’s like they’ve become a repeat toilet seat buyer. And what I’m taking away from this is that, again, knowing the data, having this intimate relationship with what data is available to you, is a key part of this. So with that in mind, I would also expect that what’s been happening over the last, what, 36 months or so with AI must be significantly shifting what we can do with the data.

Jorge: What’s the state of this given, what’s happening with Gen AI?

Colin: So I was actually looking up, this morning, some of the latest research on this because I know we were referencing this in our latest talk. There is a great state of personalization report that Twilio puts out every year. Perhaps we can link to it in the show notes, Jorge. It would be very useful for folks who want to learn more about this.

They were looking into, first of all, how many companies are using some type of AI-driven personalization in their business, and they defined it broadly. But the number is huge. It’s like 90%. Now, many are trying to use this in some capacity for personalization. Just to give you some specific examples, some of the biggest areas were user behavior prediction and process automation—automating a user’s process, using it to actually segment the user to begin with. How do we put Colin in a particular segment? And then personalizing a user journey is another broad category there.

Those are all very general areas, but one example would be training a recommendation engine. To piggyback on the toilet seat thing, right? We’re all very familiar with Amazon and their “if you bought this, you would like this” recommendation system. They have a very complex proprietary algorithm for that. But now, with the advent of the Google Cloud Platform AI and machine learning products, you can actually access some of these to do some of that logic on your own homegrown system. A recommendation engine is now a use case where AI, using one of those platforms, can help you fine-tune recommendations for your particular service or product offering based on how your users are purchasing and flowing through your system. That’s a big one that comes to mind. But Jeff, what other ones are you seeing?

Jeffrey: I’m really glad you brought up recommenders. Recommenders, like notifications, were some of the earliest experiences I had in designing for personalization when designing products and experiences for early video streaming. Then, I went to places like Consumer Reports, whose fundamental mission is helping consumers make product purchase decisions or understand product value.

There’s so much value for designers to find their way into some of these other domains. I went to the Recsys conference many years ago and I was really excited by what I saw. There were a lot of engineers who care deeply about the ethics of recommenders, and many of them were saying, what we need is better metadata and better master data. That was a wonderful breath of fresh air to hear because that is exactly the kind of work I was endeavoring to do.

I think that no matter what your setting is and no matter what scale you’re doing business at, chances are you have a corpus of content that could use some love and care and can be better stewarded. You can make smarter inferences or take greater precautions with your notification scheme. All of that stuff can be improved. And the beautiful thing is it’s all measured. It all has some proximity to revenue.

To go back to overfitting, just very quickly, you can see the improvement that design has made in personalization by studying e-commerce. E-commerce is one of the sectors that has the most mature practices in designing for personalization. There was a time, and I’m gonna really date myself here, where you could be buying—and this is a classic overfitting problem—a women’s swimsuit and then get retargeted for women’s swimsuits. But if you are buying it as a gift, that’s a false inference unless you buy gifts all the time.

In the early days of personalization, they would terraform their homepage based on that premise. They still do it today, but now they do it much more subtly, often around gender, based on what they know about you. Now, of course, they know more, but they don’t fundamentally know if your job to be done is buying a gift for someone else, taking care of a relative, or other tasks. So I see that improvement happening. In other sectors, we have less mature practices and more mistakes as a result. These are the “perso-fails” that we talk about, where things slip up for users.

Jorge: This is something that I’m living with. We have a HomePod at home, which is tied to my Apple account, but primarily my kids are the ones asking to play music through it. As a result, Apple’s recommendations of music that I might like are not exactly the music that I listen to. There’s an assumption that this kind of shared device is tied to one account. I know that they have made some improvements in that regard; we just haven’t adopted them as a household, but it’s an issue that I can see.

A lot of what we’re talking about here is about personalizing what you might call the content of the experience—when we’re talking about suggesting products you might buy based on your past behavior or what music you might listen to. I see that as the content I’m seeing in the system. I’m wondering about experiences where the structure of the system is what is personalized. The reason why I’m asking this is that I heard someone describe a possible future for the devices that we interact with in a world with AI, in which the AI basically redesigns the user interface of your devices for you based on your needs.

My immediate reaction was, oh my gosh, how am I going to help troubleshoot my relatives’ devices when they go on the fritz? Because I won’t be able to look up on YouTube how to get to that setting because it will be an all-new interface. Is there a sweet spot or is this even a thing, this distinction between the structure of the experience versus the content you’re experiencing within that structure?

Colin: So you’re describing a very real thing. And actually, it’s funny, Jorge, the example you give is now a very common scenario in customer service situations. What you just described happens all the time, even with just a website or an authenticated user portal where the user’s logged in. To use some IA terminology, let’s say the block structure of the page or the modules of the page have been reorganized, or there is different content to your point that’s being served. If a user has a problem and they’re describing their screen, the customer service agent may have no idea what they’re looking at. That’s an issue.

In a lot of the more forward-thinking personalization management software and systems out there now, there’s an impersonation ability, where I could… now, you can’t do this with your relative, unfortunately; you would have to get them to actually show you the screen. But they can impersonate the user to some extent to see what their experience is. To your point, that’s very much the real world we live in now.

Another cool thing I’ll reference, Jorge, which you would be interested in, is we came up with, as part of this years ago, something we called a personalization zoning model. When you’re thinking about the IA or the UX of, let’s say, a desktop webpage layout or the same thing for mobile, or even a mobile app, how do you zone or assign different personalization blocks or zones to different types of personalized content? We have a content model for this with different types of personalized content.

For example, an alert. You might have an alert bar with personalized information. You might also have a zone that’s a banner trying to promote a particular service or product that’s personalized for you, or an article that enriches your overall experience.

So to your question, we were trying to, from a designer’s point of view, have a framework to approach those very complex design situations formulaically. If you think about it, just the infinite possibilities wrap up very quickly. Different personalized modules with different personalized inputs coming together to create a cohesive experience, but if you don’t manage it properly and think through it from a formulaic standpoint, it can get out of control rapidly. That’s where my mind goes with that particular example.

Jeffrey: I’m a huge admirer of Colin’s work on the zone targeting method. It’s something that I’ve written and spoken about many times. For me, the takeaway is habituating a user to a more trustworthy experience that is going to be more consistent. Different UIs have different rules, but it’s important to get familiar with what are the emerging norms in these different UIs.

I had a conversation with a client about what they were planning to send in an SMS in their first communications with their customers. I noted to them, SMS has norms. Typically, this is going to be something more transactional or tactical. You’re providing some value. You are not just blitzing a customer with SMS because you’re entering an intimate or parasocial space with a user.

Colin highlights that we need to get familiar with what are the emerging norms in these different UIs. To your point, Jorge, they’re all going to get decomposed, recreated, and morphed. We know this. That’s why it’s good to start thinking about information retrieval and the different modes that users are in. This takes us back to Marcia Bates and berry-picking for different ways to get your content. Those are intrinsic and enduring. If we can design for that, we will serve our users, customers, and clients much better.

Jorge: I’m going to reflect it back to you because I think there’s a through line here: we have amazing new capabilities, right? Even going back to the early days of folks who bought this also like this kind of personalization, which I consider to be the early days of this stuff. Even those are amazing new capabilities. Designing skillfully for these systems, and by skillfully, I mean in ways that serve user needs and business needs—you want people to shop more and do all these things—requires grokking the capabilities of the systems you’re working with really deeply. These feel to me like things that cannot be understood in the abstract. You have to get down with the data, you have to get down with the systems of record, and you have to get down with what the part of the system that reconfigures the front end can do. It feels to me like that’s a through line here. Did I miss anything in that articulation?

Jeffrey: I think that’s right, but I also think there’s a legal dimension here for designers. One of the benefits of designing for personalization or even the AI summer we’re living through is we have this wave of really progressive legislation all over the Western world. You could pick any given law; all the laws are moving in the same general direction: there must be data transparency, there must be data destructibility—all of which point to one important imperative. You are going to be responsible in new and untold ways as a designer for the decisions you make. You’re going to have to design accordingly. There will probably need to be a default state that things can degrade down to, and these inferences can cause problems. There’s really an ethical dimension here that’s important, but it’s heartening to see the direction things are moving in.

Colin: To add to that, I was just looking through some of the latest reporting on this. I saw that the same survey was also saying that this is perhaps one of the first years, and I thought this was fascinating—the consumers they surveyed felt that personalization had become less targeted for them over the past 12 months.

The reason or the hypothesis there is to Jeff’s point. There’s been a lot of crackdown, particularly with Apple and IDFA. If your listeners are familiar, it’s basically an ID for advertisers, similar to a cookie. It’s been a couple of years since they began to phase it out, and they are predicting now that we’re beginning to see the long tail effects of that. Data-wise, I think the sort of crackdown is real. To Jeff’s point, those legalities will continue to expand and improve.

On the other side, I would also say that they survey this every year. This sounds terribly complicated, but in terms of the actual content channels that folks use at the end of the day for personalization, the number one is still email. Every year, it’s email. Your personalization, hello Colin spelled wrong with five L’s, that personalization is being done in email. Email remains the main channel that businesses are personalizing on going forward. It’s more complex in a sense, but we return and continue with the tried and true.

Jeffrey: There’s a quote attributed to Theodore Geisel, AKA, Dr. Seuss, that goes, “Sometimes the questions are complicated and the answers are simple.” And I think that’s what personalization, when it’s done well, is driving at. But just to broaden this out, these cards that we’ve created are a research exercise in public, and we want people to get involved and be play testing them. We’re doing that actively with a number of people, but if you’re interested, please go to http://progressivepersonalization.com. There’s no ‘s’ on that because I’ve got a redirect that does go to an HTTPS, so don’t hate. Really, this is research right now. We’re trying to figure out what’s going to be helpful to the most people in the most settings. Please, follow up with us as you will.

Jorge: That’s great. I was actually going to ask where folks could learn more about the cards because we haven’t talked a lot about them. I see this as a tool to help teams who are working on these kinds of challenges, and I’ll include the link to that in the show notes.

Closing

Jorge: This feels like a good place to wrap up the conversation. Besides the site for the cards, where can folks follow up with y’all?

Jeffrey: If you’re interested in my practice and the work that I’ve done, it’s bucket.studio. And if you wanna learn more—I’m sure Colin will say something—but if you wanna learn more about my admiration for Colin’s contributions to the literature on personalization design, there is a new book out called Content Operations from Start to Scale by Virginia Tech Publishing, edited by Carlos Evia. I will put that in the show notes as well. There’s a chapter on personalization that I was asked to write that features Colin’s contributions heavily.

Colin: And if you wanna find me, my day job, I work for a consultancy called Phaedon; wearephaedon.com. We’re a Minneapolis-based agency, although I work remotely in Washington, DC. But you can certainly reach out there, and I’d be very happy if anyone wants to find me on LinkedIn just to chat. It’s an open invite to connect or, if you want to, just chat or pick our brains about personalization. And then the final one I would say, the group that Jeff and I are in is personalizationprofessionals.org.

Jorge: Fantastic. This has been such a treat. Thank you both for being here and for sharing with us.

Jeffrey: Absolutely. Thank you so much for having us.

Colin: This has been great. It’s a lot of fun. Thanks for the invite, Jorge.