Noz Urbina: The RAUX Method for Accelerating Content Projects with AI – Episode 3

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Modern content projects begin with research to create lifelike customer personas and build detailed customer journey maps. Whether you’re on a tight budget or have a team of UX researchers at your disposal, AI can help accelerate and improve the development of these personas and journey maps.

Noz Urbina has developed the RAUX (Rapid AI-powered UX) method to help omnichannel content strategists develop realistic personas and craft effective customer journey maps.

We talked about:

• his work at his consultancy, Urbina Consulting, and his learning hub, OmnichannelX
• the RAUX AI method he has developed to accelerate user research, customer journey mapping, content design, and content development and drafting
• his simple equation for doling out information in complex content environments
• how AI can help you aggregate and understand your sources of customer information to help build personas
• how he looks at customer journeys and journey mapping
• how content fits into his customer journey maps, and how AI facilitates the tedious work that precedes and informs how to address key customer needs
• the AI-driven persona-development prompt methodology at the core of the RAUX methodology
• how to prompt AI agents in ways that mitigate the biases that often come with public data sources
• how you can query an AI persona that you have developed with the RAUX prompt methodology to help you fill in the details of a customer journey map
• how LLM’s propensity to hallucination is actually a benefit when you’re trying to conjure human feelings, questions, and queries
• how AI lets us all become programmers without becoming coders
• how AI can help with content creation, especially tasks like brainstorming and drafting
• the importance of thinking about how to use AI at every stage of the content lifecycle

Noz’s bio

Noz Urbina is one of the few industry professionals who has been working in what we now call “multichannel” and “omnichannel” content design and strategy for over two decades. In that time, he has become a globally recognised leader in the field of content and customer experience. He’s well known as a pioneer in customer journey mapping and adaptive content modelling for delivering personalised, contextually-relevant content experiences in any environment. Noz is co-founder and Programme Director of the OmnichannelX Conference and Podcast. He is also co-author of the book “Content Strategy: Connecting the dots between business, brand, and benefits” and lecturer in the Master’s Programme in Content Strategy at the University of Applied Sciences of Graz, Austria.

Noz’s company, Urbina Consulting, works with the world’s largest organisations and most complex content challenges, but his mission is to help all brands be able to have relationships with people, the way that people have with each other. Past clients have included Johnson & Johnson, Eli Lilly, Roche, and Sanofi Pharmaceuticals; Microsoft; Mastercard; Barclays Bank; Abbott Laboratories; RobbieWilliams.com; and hundreds more.

Connect with Noz online

• Urbina Consulting
• noz at urbinaconsulting dot com

Video

Here’s the video version of our conversation:

Podcast intro transcript

This is the Content and AI podcast, episode number 3. These days, designing content experiences starts with detailed customer persona development and extensive customer journey mapping. Whether you’ve got a six-figure budget or you’re doing scrappy do-it-yourself customer discovery, AI can help you accelerate and improve your research process. Noz Urbina has developed a detailed methodology that he calls RAUX (Rapid AI-powered UX) to help you develop realistic personas and craft effective customer journey maps.

Interview transcript

Larry:
Hi, everyone. Welcome to episode number three of the Content and AI podcast. I’m really delighted today to welcome to the show Noz Urbina. Noz is an omnichannel strategist at Urbina Consulting, but I know you do a lot of other stuff there too, Noz. Tell the folks what you’re up to these days.

Noz:
Yeah, absolutely. Yeah, as you said, I’m an omnichannel strategist, which involves a lot. It’s a lot of customer journey mapping, a lot of stakeholder ecosystems, content modeling, metadata modeling, working with people like ontologists and taxonomists and systems architects to really build full workflows that support omnichannel.

Noz:
My job is I’m founder and I lead a lot of the projects at Urbina Consulting. I also have founded an organization called OmnichannelX, which was a conference but has been pivoted to a year-round buffet of learning opportunities. We used to do an annual conference, the usual four-day thing. And we found that we weren’t going to be able to do physical after COVID. It was just the conference market is too difficult. And we only had one physical conference before COVID, so we never really had a chance to establish ourselves as a physical conference.

Noz:
We were going to do it online anyway. Why not make it an all year thing where people can come and they can pick and mix and they can go look at things from the archives and the library? We run the podcast regularly there too. It’s actually doing a little bit too well because sometimes, what I’m talking in business situations, people go, “Oh, yeah, well maybe OmnichannelX can come in.” And I go, “Oh, no, no, no.” Urbina are the consultants. OmnichannelX is our learning hub where we try to advance the industry and provide learning resources for all the good people out there.

Larry:
Well, that’s great. It’s always good to have a little too much success, so congrats on that.

Noz:
Thank you.

Larry:
But hey, the reason I wanted to have you on the show today, this is the whole point of this new podcast is about AI and in particular using how folks are using AI in content practice. Now, my first two episodes were background setting and level setting on the whole field of AI. You’re really the first person that I’ve had on, this is how I want the whole rest of the episodes to be like. You’re out there in the world doing cool stuff with AI in your practice today. All that stuff you just mentioned, the customer journey mapping, content muddling, all those things. Tell me how… Well, you have a specific model, I know called RAUX, the Rapid AI-powered UX. Tell me a little bit about how that came to be and what you do with it.

Noz:
All right. Okay. You got to pronounce the cool way, which is RAUX.

Larry:
Okay, now I’m on board. Okay.

Noz:
The RAUX methodology, I couldn’t resist the acronym. It’s not just for UX strictly speaking. Depending on how you define UX. It’s for any type of experience or content design, but it also extends into the early research stages and also the content development and drafting stages.

Noz:
What we found was that in order to be able to do content design, content strategy properly, we really had to do customer journey mapping. Because if you Google customer journey map, you get some very disappointing diagrams which are aligned with a couple boxes superimposed over it. And so I get a newsletter or see an ad and I click a landing page and I download a thing. And it’s just this happy path of contact points and they put some notes on them.

Noz:
That’s really inadequate for content people. If we’re really trying to get into the informational needs and the informational journey and requirements of our people, the level of customer journey mapping that we were getting from your usual UX design process was not what we needed.

Noz:
What we’ve started doing at Urbina Consulting, because we’re an omni-channel consultancy, is we’re trying to put together a methodology which works for everybody. You can just use it for designing product, you can use it for designing digital experiences of any kind. We’ve come up with integration requirements. And for example, we realized that we needed to pull this data from the CRM, so in real time we could show on the website what was happening in the call center. Which is not your usual kind of content requirement, but it comes out through properly analyzing the journey.

Noz:
But it’s a lot of work. What RAUX is about is using AI to accelerate that process. Taking the research you do have or starting wherever you are and using AI at several points in the whole content life cycle from research all the way out to delivery.

Larry:
Nice. And I know I do a little bit of content modeling and journey mapping myself. And typically, especially in journey mapping, that is one of the, we’re all used to these giant grid things. We all work in spreadsheets all the time. But a good journey map is the ultimate, giant deepest spreadsheet you’ve ever worked with. And there’s so many cells to fill and this is what this helps with. Right?

Noz:
Yeah, exactly. What we like to do is have a decent narrative. We want people to have a story that’s told in the first person. We don’t do, the user does this and then they click here and then they do that. Because I’ve literally found this in workshopping. When you speak about the user in the third person, you start to objectify them.

Noz:
That if you really want to drive empathy in the team when they read the narrative, they should be role-playing in their heads. They should be living the story that this person is going through. That’s, the writing of that is a bit of an art. You have to be able to empathize and you have to be able to think in the first person and get yourself out of your own head as the person who might own these assets that are in question or be very close to the problem.

Noz:
That’s a decent amount of work is also the figuring out the questions over time. Our journey mapping methodology at its simplest, you were saying there’s all these cells, all these rows. At its simplest, it’s literally just that. What is the information over time equation here? What kicked me off on my journey and why am I going on this journey at all? I have an objective to answer some sort of question. Whether it’s explicit or implicit. What should I do this afternoon or how can I keep myself entertained? We don’t really articulate that question in our mind, but that’s the thing that makes us pick up Netflix or whatever app to start scrolling.

Noz:
And then in real life situations with the kind of clients we have, then it’s something more particular. Like they’re going to university or they’re signing up for a bank loan or they’re taking on a new medication. You don’t know all the questions that you’re even going to ask along this journey at the beginning. Having all the answers there right at the beginning before you even realize you have those questions is not necessarily the best experience. Properly dosing out information over time, is we found to be an incredibly effective way to help organizations plan how they should structure, manage and componentize their information.

Noz:
Which questions are common across multiple audiences? Which questions are unique to a particular audience? Which questions are unique to a particular channel? Can I trust this chatbot? Or is the other person on the side of this live chat qualified? Those are very channel-specific questions. You don’t have those questions if you’re doing this, if you’re walking into a physical environment or something like that.

Noz:
What we use AI for is taking whatever research we have and being able to synthesize it together in an efficient way. From the beginning, right at the beginning, if you are able to do interviews, then you can use things like Otter or Zoom’s. I think Zoom is introduced a new captioning feature, transcribing feature. Lots of tools allow you to transcribe interviews in a research context.

Noz:
And every time you call somewhere now it says, “Your call is being recorded for quality and control purposes,” blah, blah, blah. Those interviews can also be transcribed. Support calls, sales calls, or chatbot logs, anytime you have rich sources of unstructured content coming in, AI can help you transcribe them, structure them, do some analysis on them to help you build your personas understanding.

Noz:
But wherever your starting point, but the idea is that we want to talk to our customers as much as we can, but that’s always finite. We always have a finite amount of time, finite amount of money to talk to customers. If you can take all that research or take whatever research you do have and spin that up into an AI who will role play as your customer as long as you want, for as many workshops as you want for as many interviewees and interviews as you want, that’s an incredible asset. When we discovered we could do that, it’s really supercharged our ability to generate these maps and generate this context, which is then the basis for everything else.

Larry:
Yeah. And can you… I’m going to ask a complicated question, both tease out and stitch back together the relationship between the personas and the journeys? Because often in these journey maps you’ll have one or two or more often more than one persona that you’re serving. And that just again magnifies that number of cells issue that you’re filling problem. But maybe talk a little bit of both about how AI can help flesh out those personas and how it can identify and articulate the needs at different points on the journey.

Noz:
Well, I think we should start with the various perspectives people are in first. When I go into a project, there’s a huge spectrum of situations. On one end, we’ve got triple digit research budgets, we’ve got multiple UX researchers. We’ve booked interviews with multiple client representatives or audience representatives, staff.

Noz:
And we’re going to talk to 5, 10, 20 people all from different roles to understand this whole thing. And then we’re going to supplement that with online surveys where we’re going to get quantitative data for 500 or 1,000 people to validate the findings in those interviews.

Noz:
That’s one extreme. The other extreme is nobody cares about you. You have to get this thing launched and out there. You have zero budget for research and no one wants you to speak to anybody. What we said we needed is we needed a methodology that helped us go from wherever we are to where we need to be as fast as possible.

Noz:
We’ve had situations where we have next to no research and we have one paragraph describing a persona. And then others where we have quite rich research, but we have no journeys. The relationship between the two is that the journey is the story of a persona getting their task accomplished. You can start or stop whenever you want. You can make these as narrow or broad as you want.

Noz:
A journey is not logging in. That’s more of a tech use case, because that’s not really an objective. Although theoretically you can map it out using the same process. Getting something done, something meaningful accomplished, that’s where we bracket the journeys. You can do them without a persona. You can do them simply with a role. As a, I’m a doctor and I want to do this. Or you can have multiple personas.

Noz:
For example, if we had a situation with a bank, and interestingly enough lost card, like replacing your bank card is one of their most common journeys. And it’s one of the top three things people go to their bank website to do is replace their card. Otherwise, they go straight into the app, straight into online banking. But actually looking at the website, one of the top three things was how do I replace my card?

Noz:
And if you screw it up, people get very upset. That’s the kind of thing where you don’t want to just say “cardholder” because you want to say, okay, “cardholder who is digitally literate.” “Cardholder who the website is not in their first language.” “Cardholder who has lost their card while traveling and they have their kids with them in Greece.” Depending on how important the journey is to your business, you may do it with multiple personas and do multiple persona variants.

Noz:
The other link between the two is that your journey is also where you figure out where you’re going to do meaningful personalization. What we’re seeing out in the market is brands spending literally millions on personalization capable tech stacks, but then don’t know how, why, or when to actually personalize. They have all these new capabilities, but if you buy a Ferrari and just try to take it on the track, chances are you’re going to flip it. Or you’re going to be too scared to get in it in the first place.

Noz:
That’s what we see. They see these Ferraris parked in the garage because people are going, “I don’t know how to drive that thing. That’s way too much power.” And so we teach a method to say, “Okay. Well, the way that you do personalization is figuring out when there’s an actual need. And you can only do that by running through the journey. What are people needing? Where can we add additional value by doing something differently?”

Larry:
Yeah, as you’re saying that, I’m all of a sudden picturing a continuum from canned off-the-shelf persona development to the sub-personas that you talked about to full on personalization. It’s almost, I hadn’t thought about it that way before, but it’s like… And that’s something that I think part of the reason I haven’t thought about it before because it would be so technically difficult to do anything with it now. I’m wondering if AI puts us closer to just understanding where we are on that kind of continuum. Does that make sense?

Noz:
Well, I think it can do, if you do sentiment analysis and you are looking at a large amount of incoming data, that’ll give you a a sense where you are on that spectrum in terms of the need. Otherwise, you have to theorize. You have to say, “Well, let’s just role play this a few times with the personas that we do know exist and see what comes out.”

Noz:
The journey map has a lot of stuff in it, like the narrative and the problems and what channels are being used. And a sentiment emotional barometer saying, “This is going well right now, or am I upset?” And it’s got the questions, the various tasks being done. All of that really is not what we’re trying to get out of the journey map. That’s what we have to have in order to get to the good part, which is designing stuff.

Noz:
At the bottom of our journey maps, which is I think one of the things that makes them better than a lot of the methodology we see, is where you get into the real brainstorming and business stuff. We brainstorm our ideas and opportunities. What are we going to do for this person at this stage? What’s at a high aspirational level? What could we do? And then, okay, well, if we have no money and no time, what’s the minimum we should do? And you can brainstorm a whole range in there.

Noz:
What content would be required at this particular stage? And also, what data are we going to be getting and what data are we going to be producing? Do we know that this person came from a personalized URL? Did they come from a particular campaign? Did they come, were they forwarded by a partner? What are the data that we’re going to leverage to do personalization? And if they do the thing we want them to do, if they click or they do the calls to action which you’ve identified for that stage, how are we going to measure that? How do we capture data and how do we contextualize the data so we can drive insights with it?

Noz:
That bit is not done in most journey maps. I think most people run out of steam doing the top bit where they’re just telling the story. If AI helps us tell the story much faster and figure out what the potential questions might be much faster, that lets us concentrate on the, “Okay, so now what?” part. That’s where I see the real add value, is accelerating the humans to the parts where the humans need to take decisions.

Larry:
Yeah. That gets back to you showed me a demo, a deck that you used to walk people through the RAUX methodology. And I’m wondering, I don’t know, we can’t… It’s weird to do it in a podcast and recording, but I wonder if you can walk through that. Because you just articulated the situation that sets up the benefits of this that, okay, you need the data, but you’re never going to always have all the data you need. Simulated data and other benefits that you can get. Can you walk through maybe from the persona, fleshing out a persona, how you’d prompt an AI to do that?

Noz:
Okay. Let’s get into the nitty gritty here. what we’re talking about is teaching the AI who it is and how you want it to respond. That’s the core of the UX part of it. I have some other stuff about teaching it then how to brainstorm content and plan personalization. But let’s talk about the journeys and personas a bit.

Noz:
The first thing you do is you tell it that from now on, you are not ChatGPT, you are Jane. Jane is 18, she’s from this background. She has these goals, whatever you do know about your persona. And then, you instruct the AI from that point onwards to always answer at role play as you would do if you were doing this internally, from that point on, role play as that persona. And it won’t immediately understand the tone of voice, the perspective, the background.

Noz:
We load as much input in as we can. The first prompt is from now on, you’re going to act as this persona. This is the background. And you give them the background. All answers should be as that persona would respond, unless I specifically ask you something separate from that. And the AIs are smart enough today, they can tell the difference to a question that’s directed to them versus the persona, or enough of the time.

Noz:
That’s how you kick them off. Then you explain you’re going to receive more background information and research about your persona. We prompt them. You keep accepting research until I tell you, “Research complete.” I’d have to look up the whole manual to get the exact prompt wording. But the concept is that. You feed it everything you do know, all their motivations, all their goals, all… what brands they like, everything you know about this persona.

Noz:
And here I know that there’s a common concern about bias. People are really worried about, are we really just basically ending up with a privileged or elite view of the world? I was talking with Sana Remekie of Conscia. She’s spoken at OmnichannelX a couple times. And she pointed out that her husband is from the islands area, so I think he’s Jamaican. And she asked it to write a poem in Jamaican patois. And all of their family, their hairs on their arms stood up because it was exciting and terrifying to see a machine that could speak Jamaican patois and write a poem in it. That was funny.

Noz:
There is a very wide range of humanity in these things. You just have to properly tell them, “This is who I want you to represent.” And the more you can tell it, the better. You can tell ChatGPT, say, “According to Wikipedia, what’s the answer to this question?” And it will tell you an answer based on one website. Even if there’s lots of bias or lots of inequality baked into the training data, if you just tell it, “This is who you’re representing, this is how I want you to respond, this is how I want you to talk,” it’s very good at learning how to do that.

Larry:
Now I want to have you back for a whole episode on anti-bias hacking with AI. No, but that’s a great point because… And this gets into that, it’s like the, “Now do the poem in the voice of Daffy Duck.” It’s like AI is your agents are really good at this kind of thing. Once you’ve got them, then I can see how you get to this really well articulated persona. And I assume there are similar things you can do further down the journey as well, like helping… Okay, you are this persona, now you get to this point in your journey and tell me what you’re trying to accomplish. Is that the next step of this?

Noz:
Yeah, exactly. What we do is we are even more structured. We have our journey mapping methodology, which is ours. We teach the AIs to respond in the structure of the journey map. What you have to do is figure out, okay, who are my personas? What do their processes look like? How do they go about this? Again, the more you know, the better. If you don’t know, then you can ask the AI.

Noz:
If you have a good sense of what the high level process is, for example, applying for a loan or checking for the side effects of a medicine. If you know the high level processes today, then you take that structure and then you feed it step by step to the AI saying, “Each time I’m going to give you a new stage, tell me these things: what your problems are, what your goals are, what questions you might have in your mind right now, what tasks you’re trying to specifically accomplish. Are there any transactions you might need to do from a business perspective? What are your concerns?”

Noz:
Whatever your journey map structure is, you make the AI respond under those headings. That way it tells you, it basically fills out your journey map one column at a time. And because you’re doing it with one AI, the story tends to make sense because you can tell it, “Okay. Now we’ve shown up at the shop and then we’ve gone up to the aisles and tried to find something. And then we couldn’t find it so we went up to a store representative.” It’s following the narrative as it fills out the journey map for you. That’s very powerful.

Larry:
Interesting. And as you say that, I’m thinking that it seems like that whole methodology is a way to prevent the AI agents from hallucinating, which they’re so notoriously prone for. Is that working out in practice or is that an accurate assumption that I’m making there?

Noz:
Well, hallucination in this case is not such a big deal because you’re not asking it for hard factual data. You’re asking it for feelings, questions, queries. And so that’s what it’s very good at. It’s very bad at saying in what year did this person do blah, blah, blah, because it’s got this massive dataset, which is just generalized into the sense of human experience.

Noz:
But if you’re asking it for the sense of human experience, that’s exactly what it’s great at. It can tell you the questions. If you asked it to include particular data points from thus and thus database, if you didn’t have access to those, that might be a challenge for it. But it’s very good at saying, “If I was 16 and I was going to school and I have problems with sleep, these are the questions that might be in my head.”

Noz:
And if you don’t like the first list, you just ask it to elaborate, ask it for more, ask it three more times. The good thing about having an infinitely patient persona is if it comes out with something unrealistic, you can go deeper. Oh, actually I forgot to mention during the training phase, we have our little diagram. We have educate, ideate, validate. But step two is also validate. We, you educate, you validate, you ideate, you validate.

Noz:
When you first load up the persona and you start to ask some questions, we do validation questions. We ask it, “How are you doing today?” And see if it comes back with a persona-appropriate reply. And then we ask it, “What challenges are you worried about today?” And again, we check it and we’ll see things like in the initial, if you haven’t trained it enough, it will use words or vocabulary that doesn’t make sense for that persona. It will be too enthusiastic, for example.

Noz:
AIs have a bias towards positivity. We actually had to train a persona to be a skeptical teenager. We would say, “No, you don’t want to click on every ad that you see. You don’t want to download every app that you’re offered.” Because it was just like, “Yay, I’m going to get all the things.” And so we had to train it to tone down its enthusiasm and to be more cautious, skeptical, et cetera. And then once you’ve corrected it, it will maintain that.

Larry:
That’s right. I love that putting the human in the loop creates this more genuine and human persona. Hey, Noz, we’re coming close to time, but there’s one other thing I wanted to ask you about before we get into the conclusion. We were talking before we went on the air about, you said this brilliant quote that AI turns all of us into programmers without being coders. I want you to elaborate on that a little bit because I guess this is an important lesson for folks.

Noz:
Yeah. Well, that’s my go-to quote, and I was very happy to hear that a few months after I started using it. The CEO of Nvidia, who is one of the manufacturers of the AI hardware that’s powering this whole revolution, uses it in almost verbatim. The way I say it is “AI enables us all to be programmers without being coders. Those who can best understand the problem, its context, its constraints, the intended results, and are able to express that logically and clearly will win the future.”

Noz:
The idea is that we used to go get a translator to explain to the computer what we wanted. That was what a coder was, a programmer. Now we can write the program because we understand what we want. We can figure out what we want. And what we need is clear, logical expressions of that. Can you write clearly? Do you have good rhetoric? Can you explain and parameterize what you want?

Noz:
This is the format I want it in, this is the structure I want it in, this is the tone I want it in. This is the kind of things that you take into account when constructing your answer. That allows us to make the computers do stuff. We’re getting to a point where ChatGPT can write entire applications. You give it the specifications of what you want and the instructions, and it will design it, code it, and spit it out. And you can compile it and make a game.

Noz:
There’s a wonderful little video about that, six minutes long, where 25 ChatGPTs form a little software company, and one of them is the CEO, and some of them are programmers, others are testers. The testers test the coders code to prevent – vecause in that case, if it hallucinates, then the code won’t run. But if a tester tests the coder’s code and sends it back to the coder for improvements, then the code gets debugged. This little software company, you can just say, “I want a chess game, or I want an et cetera game.” Or come up with some games which you think would be popular. And this little AI software company will generate actual working code.

Noz:
It’s up to us to be able to express what we need clearly. I always analogize AI to the 10,000 interns. An AI is like having 10,000 interns at your disposal. You can get an enormous amount done, but you can’t leave them unsupervised. You can’t give them vague instructions, you can’t expect them to intuit things from their experience because they have none. It’s the same thing with AI. You’ve got to coach and guide them.

Noz:
The last little thing in my quote, which I left out, which was doing all that vigilant for their bias, your own bias and the AI’s bias. And that’s really for me, how I frame AI, make it a little less scary.

Larry:
That’s perfect. I think that’s a great framing. Especially when you think about that bundle of capabilities you just mentioned, it aligns pretty well with a lot of content people’s capabilities. I hope…

Noz:
Yes.

Larry:
Yeah. Well, hey, Noz, I can’t believe we’re coming up on time. These always go way too fast. But hey, before we wrap, is there anything last that you want to make sure we mention or before we close?

Noz:
Yeah. Well, we didn’t get into two things, which I can cover very quickly. One is the actual creation of content. Is that once you’ve got the AI to help you with the journey, you get to your brainstorming. AIs are fantastic brainstorming partners. Suggest the table of contents for an article that would address this need that we’ve identified once you’ve validated the need, for example.

Noz:
And then you can iteratively go back and forth it, draft some content, you edit it, you improve it, send it back, ask for improvements. They’re really great co-writers, AIs. And the other thing is, again, about the framing, there’s a lot of attention on replacing the delivery. Can we replace websites with AIs? Can we replace writers with AIs?

Noz:
AI is going to be all over the content lifecycle. The RAUX methodology, as I just said, is focused on the beginning, requirements, development, research, understanding, drafting. So instead of thinking how can we deliver through AI, think about how we can do every stage of getting there with AI. And also overlaying that on our tech stack.

Noz:
There’s all these different touch points in an omnichannel experience. Where could AI help each of those as opposed to just always focusing on that, okay, people are going to go into a chatbot and then they’ll get an answer. That’s a drastically over-limited way of thinking about the benefits of this technology.

Larry:
Yeah. And when you set out that list of to-dos, it’s a lot easier when you have 10,000 interns at your disposal.

Noz:
Yeah.

Larry:
Yeah. Okay. Well, thanks so much, Noz. Thanks for helping me kick off this part of the podcast.

Noz:
My pleasure. And people should get in touch because we’re open beta testing this methodology, so we’re sharing it maybe even more than we will in the future for free. If people want the handbook and people want to experiment with it and ask me for some feedback, then I’m happy to do that. Well, LinkedIn is not a good way anymore. Emailing me is a good way.

Larry:
Okay, great. And we’ll include that in the show notes as well, so folks can find you.

Noz:
Fantastic.

Larry:
Thanks, Noz.

Noz:
My pleasure. See you next time.