John (00:07): Hello, and welcome to another episode of the Duct Tape Marketing Podcast. This is John Jantsch. My guest today is Kenneth Wenger. He’s an author, research scholar at Toronto Metropolitan University and CTO of Squint AI Inc. His research interests lie in the intersection of humans and machines, ensuring that we build a future based on the responsible use of technology. We’re going to talk about his book today is The Algorithm Plotting Against Us, a Lay Person’s Guide to the Concepts, math and Pitfalls of ai. So Ken, welcome to the show.

Ken (00:44): Hi, John. Thank you very much. Thank you for having me.

John (00:46): So we are going to talk about the book, but I’m just curious, what does Squint AI do?

Ken (00:51): That’s a great question. So Squint AI is a company that we created to do some research and develop a platform that enables us to do AI in a more responsible way. So I’m sure we’re going to get into this, but I touch upon it in the book in many cases as well, where we talk about ai, ethical use of ai, some of the downfalls of ai. And so what we’re doing with Squint is we’re trying to figure out how do we try to create an environment that enables us to use AI in a way that lets us understand when these algorithms are not performing at their best, when they’re making mistakes and so on.

John (01:35): So the title of your book is The Algorithm Plotting Against This? It’s a bit of a provocative question. I mean, obviously I’m sure there are people out there that are saying no and some are saying, well, absolutely. So why ask the question then?

Ken (01:53): Well, because I actually feel like that’s a question that’s being asked by many different people actually with different meaning. So it’s almost the same as the question of is AI posing an existential threat? It’s a question that means different things to different people. So I wanted to get into that in the book and try to do two things. First, offer people the tools to be able to understand that question for themselves and first figure out where they stand in that debate, and then second, also provide my opinion along the way.

John (02:26): And I probably didn’t ask that question as elegantly as I’d like to. I actually think it’s great that you asked the question because ultimately what we’re trying to do is let people come to their own decisions rather than saying, this is true of ai, or this is not true of ai. Right?

Ken (02:40): That’s right, that’s right. And again, especially because it’s a nuanced problem and it means different things to different people.

John (02:48): So this is a really hard question, but I’m going to ask you where are we really in the continuum of ai? I mean, people who have been on this topic for many years realize it’s been built many things that we use every day and take for granted. Obviously chat, GPT brought on a whole nother spectrum of people that now at least have a talking vocabulary of what it is. But I remember I’ve, I’ve had my own business 30 years. I mean, we didn’t have the web, we didn’t have websites, we didn’t have mobile devices that certainly now play a part, but I remember as each of those came along, people were like, oh, we’re doomed. It’s over. So currently there’s a lot of that type of language surrounding ai, but where do you think we really are in the continuum of the evolution?

Ken (03:37): That’s a great question because I think we are actually very early on. I think we’ve made remarkable progress in a very short period of time, but I think it’s still, we’re at the very early stages. If you think of ai, where we are right now, we were a decade ago, we’ve made some progress, but I think fundamentally at a scientific level, we’ve only started to scratch the surface. I’ll give you some examples. So initially, the first models that were great at really giving us some proof that this new way of posing questions, neural networks, essentially they’re very complex equations. If you use GPUs to run these complex equations, then we can actually solve pretty complex problems. That’s something we realized around 2012 and then after around 2017, so between 2012 and 2017, progress was very linear. New models were created, the new ideas were proposed, but things scaled and progressed very linearly.

(04:39): But after 2017, with the introduction of the model that’s called the Transformer, which is the base architecture behind chat, GPT and all these large language models, we had another kind of realization. That’s when we realized that if you take those models and you scale them up and you scale them up in terms of the size of the model and the size of the dataset that we used to train them, they get exponentially better. That’s when we got to the point where we are today where we realized that just by scaling them, again, we haven’t done anything fundamentally different since 2017. All we’ve done is increase the size of the model, increase the size of the dataset, and they’re getting exponentially better.

John (05:18): So multiplication rather than addition?

Ken (05:22): Well, yes, exactly. Yeah. So the progress has been exponential, not only in linear trajectory, but I again, the fact that we haven’t changed much fundamentally in these models, that’s going to taper off very soon. It’s my expectation. And now where are we on the timeline, which was your original question. I think if you think about what the models are doing today, they’re doing very element. They’re doing very simple statistics. Essentially the idea of these models being called artificial intelligence, I think it’s a bit of a misnomer. I agree, and it leads to some of the questions that people have because there isn’t much deep intelligence going on. It’s just statistical modeling and very simple at that. And then where we are going from here and what I hope the future is, that’s when we start. I think the things are going to change dramatically when we start getting models that are able not just to do simple statistics, but are able to understand the context of what it is they’re trying to achieve and are able to understand the right answer as well as the wrong answer. So for example, they’re able to know when they’re talking about things they know and when they’re kind of skirting around this gray area of things they don’t really know about. Does that make sense? Yeah,

John (06:43): Absolutely. I mean, I totally agree with you on artificial intelligence. I’ve actually been calling it ia. I think it’s more of informed automation is kind of how I look at it, at least in my work. Do you see a day where prompts asking questions, that’s kind of the street use, if you will, of AI for a lot of people. Do you see a day where it starts asking you questions back? Why would you want to know that? Or what are you trying to achieve by asking this question?

Ken (07:10): Yeah, so the simple answer is yes, I definitely do, and I think that’s part of what achieving a higher level intelligence would be like. It’s when they’re not just doing your bidding, it’s not just a tool, but they kind of have their own purpose that they’re trying to achieve. And so that’s when you would see things like questions essentially arise from the system is when they have a goal they want to get at and then they figure out a plan to get to that goal. That’s when you can see emergence of things like questions to you. I don’t think we’re there yet, but I think it’s certainly possible.

John (07:44): But that’s the sci-fi version too, right? I mean where people start saying the movies, it’s like, no, Ken, you don’t get to know that information yet. I’ll decide when you can know that.

Ken (07:56): Well, you’re right. The way you asked the question was more like, is it possible in principle? I think absolutely, yes. Do we want that? I mean, I don’t know. I guess that’s part of it depends on what use case we’re thinking about, but from a first principle’s perspective, yeah, it is certainly possible to get a model to do

John (08:17): That. So I do think there are scores and scores of people. There are only understanding of AI as I go to this place where it has a box and I type in a question and it spits out an answer. Since you have both layperson and math in the title, could you give us the layperson’s version of how it does that?

Ken (08:37): Yeah, absolutely. Well, at least I’ll try. Lemme put it that way. A few moments ago when I mentioned that these models, essentially what they are, they’re very simple statistical models. That itself, that phrase itself a little bit, it’s controversial because at the end of the day, we don’t what kind of intelligence we have. So if you think about our intelligence, we don’t know whether at some level we are also a statistical model. However, what I mean by AI today in large language models like chat, GPT being simple statistical models, what I mean by that is that they’re performing a very simple task. So if you think of G pt, what they’re doing is they are trying essentially to predict the next best word in a sequence. That’s all they’re doing. And the way they’re doing that is that they calculate what are called probability distribution.

(09:35): So basically for any word in a prompt or in a corpus of text, they calculate the probability that word belongs in that sequence, and then they choose the next word with the highest probability of being correct there. Now, that is a very simple model in the following sense. If you think about how we communicate, we are having a conversation right now. I think when you ask me a question, I pause and I think about what I’m about to say. So I have a model of the world and I have a purpose in that conversation. I come up with the idea of what I want to respond, and then I use my ability to produce words and to sound them out to communicate that with you. It might be possible that I have a system in my brain that works very similar to a large language model in the sense that as soon as I start saying words, the next word that I’m about to say is one that is most likely to be correct, given the words that I just said. It’s very possible. That’s true. However, what’s different is that at least I already have a plan of what I’m about to say in some latent space. I have already encoded in some form what I want to get across, how I say it, that the ability to produce those words might be very similar to a large language model, but the difference is that a large language model is trying to figure out what it’s going to say as well as coming up with those words at the same time.

(11:08): Does that make sense? So it’s a bit like they’re rambling and sometimes if they talk for too long, they ramble in a nonsense territory because they don’t know what they’re going to say until they say it. So that’s a very fundamental difference. Yeah,

John (11:24): I have certainly seen some output that is pretty interesting along those lines. But as I heard you talk about that, I mean, in a lot of ways that’s what we’re doing is we’re querying a database of what we’ve been taught are the words that we know in addition to the concepts that we’ve studied and are able to articulate. I mean, in some ways we’re querying that to me, prompting or me asking you a question as well, I mean it works similar. Would you say

Ken (11:51): The aspect of prompting question and then answering it, it’s similar, but what is different is the concept that you’re trying to describe. So again, when you ask me a question, I think about it and I come up with, so again, I have a world model that works so far for me to get me through life, and that world model lets me understand different concepts in different ways. And when I’m about to answer your question, I think about it, I formulate a response, and then I figure out a way to communicate that with you. That step is missing from what these language models are doing. They’re getting a prompt, but there is no step in which they are formulating a response with some goal, some purpose. They are essentially getting a text and they’re trying to generate a sequence of words that are being figured out as they’re being produced, right? There’s no ultimate plan. So that’s a very fundamental difference. I

John (12:57): Do want to come to what the future holds, but I want to dwell on a couple things that you dive into in the book. What are the, other than the fear that the media spreads, what are the real and obvious pitfalls of relying on ai?

Ken (13:18): I think the biggest issue and the real motivator for me when I started writing the book is

(13:28): That it is a powerful tool for two reasons. It’s very easy to use, seemingly. You can spend a weekend learning python, you can write a few lines and you can transform, you can analyze, you can parse data that you couldn’t before just by using a library. So you don’t really have to understand what you’re doing and you can get some result that looks useful, but hidden in that process. The fact that you can take data, a large amounts of data, modify it in some way and get a response, get some result without understanding what’s happening in the middle, has huge repercussions for misunderstanding the results that you’re getting. And then if you’re using these tools in the world in a way that can affect other people. For example, let’s say you work in a financial institution and you come up with a model to figure out who you should give some approval for credit for a credit line and who you shouldn’t.

(14:41): Now, right now, banks have their own models, but if you take the AI out of it, traditionally those models are taught through by statisticians, and they may get things wrong once in a while, but at least they have a big picture of what it means to analyze data, biasing the data, what are the repercussions of bias in the data? How do you get rid of all these things or things that a good statisticians should be trained to do? But now, if you remove the statisticians, because anybody can use a model to analyze data and get some prediction, then what happens is you end up denying and approving credit lines for people with repercussions that could be driven by very negative bias in the data. It could affect a certain section of the population negatively. Maybe there’s some people that can’t get a credit line anymore just because they live in a particular neighborhood, or there’s many reasons why this could be a problem.

John (15:37): But wasn’t that a factor previously? I mean, certainly neighborhoods are considered as part of the, even in the analog models, I think.

Ken (15:46): Yeah, absolutely. So like I said, we always had a problem with bias, right, in the data, but traditionally, you would hope, so two things would happen. First, you would hope that whoever comes up with a model, just because it’s a complex problem, they have to have some statistical training, right? And an ethical statistician would have to consider how to deal with the bias in the data. So that’s number one. Number two, the problem that we have right now is that first of all, you don’t need to have that decision. You can just use a model without understanding what’s happening. And then what’s worse is that with these models, we can’t actually understand how the, or it’s very difficult traditionally to understand how the model arrived at a prediction. So if you get denied either a credit line or as I talk about in the book bail, for example, in a court case, it’s very difficult to argue, well, why was I denied this thing? And then if you go through the process of auditing it again with the traditional approach where you have a statistician, you can always ask us, so how did you model this? Why was this person denied this particular case in an audit with a neural network, for example, that becomes a lot more complicated.

John (17:00): So what you’re saying, one of the initial problems is that people are relying on the output, the data. I mean, even I use it in a very simple way. I run a marketing company and we use it a lot of times to give us copy ideas, give us headline ideas for things. So I don’t really feel like there’s any real danger in there other than maybe sounding like everybody else in your copy. But you’re saying that as people start relying on these to make decisions that are supposed to be informed, a lot of times predictions are wrong.

Ken (17:37): And so the answer is yes. Now, there is two reasons for that. And by the way, let me just go back to say that there are use cases where, of course, you have to think about this as a spectrum. There are cases where the repercussions of getting something wrong is worse than other cases. So as you say, if you’re trying to generate some copy and if it’s nonsensical, then you just go ahead and change it. And at the end of the day, you’re probably going to review it anyway. So that is probably a lower cost, the cost of a mistake that will be lower than in the case of using a model in a judicial process, for example. Now, with respect to the fact that this model sometimes make mistakes, the reason for that is that the way these models actually work is that, and the part that can be deceiving is that they tend to work really well for areas in the data that they understand very well.

(18:36): So if you think of a dataset, right? So they’re trained using a dataset or most of the data in that dataset, they’re going to be able to model it really well. And so that’s why you get models that perform, let’s say, 90% accurate on a particular dataset. The problem is that for the 10% where they’re not able to model really well, the mistakes there are remarkable and in a way that a human would not be able to make those mistakes. So what happens in those cases that first of all, when we’re training these models that we get, we say, well, we get 10% error rate in this particular dataset. The one issue is that when you take that into production, you don’t know that the incidence rate of those errors are going to be the same in the real world. You may end up being in a situation where you get those data points that lead to errors at a much higher rate than you did in your dataset, just one problem.

(19:30): The second problem is that if your use case, if your production application, it’s such where a mistake could be costly, like let’s say in a medical use case or in self-driving, when you have to go back and explain why you got something wrong, why the model got something wrong, and it is just so bizarrely different from what a human would get wrong, that’s one of the fundamental reasons why we don’t have these systems being deployed across safety critical domains today. And by the way, that’s one of the fundamental reasons why we created Squint, is to tackle specifically those problems, is to figure out how can we create a set of models or a system that’s able to understand specifically when models are getting things right and when they’re getting things wrong at runtime. Because I really think it’s one of the fundamental reasons why we haven’t advanced as much as we should have at this point. When models work really well, when they’re able to model the data, well then they work great. But for the cases where they can’t model that section of the data, the mistakes are just unbelievable. It’s things that humans would never make, those kinds of things.

John (20:40): Yeah, and obviously that’s certainly going to, that has to be solved before anybody’s going to trust sending a man’s spacecraft guided by AI or something, right? I mean, when human life is at risk, you’ve got to have trust. And so if you can’t trust that decision-making, that’s certainly going to keep people from employing the technology, I suppose,

Ken (21:04): Or using them, for example, to help in, as I was saying, in medical domains, for example, cancer diagnosis. If you want a model to be able to detect certain types of cancer given let’s say biopsy scans, you want to be able to trust the model. Now, any model, it’s going to make mistakes. Nothing is ever perfect, but you want two things to happen. First, you want to be able to minimize the types of mistakes that the model can make, and you need to have some indication that the quality of the prediction of the model isn’t great. You don’t have that. And second, once a mistake happens, you have to be able to defend that the reason the mistake happened is because the quality of the data was such that even a human couldn’t do better. We can’t have models make mistakes that a human doctor would look at and say, well, this is clearly incorrect.

John (21:54): Yeah, yeah, absolutely. Well, Ken, I want to thank you for taking a moment to stop by the Duct Tape Marketing Podcast. You want to tell people where they can connect with you if you’d like, and then obviously where they can pick up a copy of is the Algorithm Plotting Against Us?

Ken (22:09): Absolutely. Thank you very much, first of all, for having me. It was a great conversation. So yeah, you can reach me on LinkedIn and for the copy, for a copy of the book, you can get it both from Amazon as well as from our publisher website. It’s called the working fires.org.

John (22:22): Awesome. Well, again, thanks for stopping by. Great conversation. Hopefully maybe we’ll run into you one of these days out there on the road. Thank you.

John (00:08): Hello and welcome to another episode of the Duct Tape Marketing Podcast. This is John Jantsch and no guest. Today I’m doing another solo show. Some of you may not like these. I get a lot of great feedback when it’s just me. It gives me a chance to kind of break down some of my thinking, my opinions, quite frankly. And today is no different. I’m going to talk about trends for 2024. Now, before I get into any of my prognostication, that’s a big word to use in the morning. Trends shows trend, post trend predictions are stupid. And the reason I say that is because a lot of times by the time you spot a trend, it’s not really a trend anymore. It’s happening or you’re just taking guesses at stuff that you think should happen. There’s so many over the years, there’s so many things that people talked about.

(01:11): This is the next trend, and 10 years later we’re still talking about it. I’m going to mention a couple things today that we’ve been talking about for a long time, and that’s the thing about trends. I think that really make them difficult to identify with any clarity. I think it’s really more a matter of acknowledging something that’s coming maybe and saying, well, gosh, how could that impact me? And then going about your business, it reminds me of the 1926 novel by Ernest Hemmingway. The Sun Also Rises. There’s a character in there and he asked, I think his name’s Bill. Bill says to Mike, so how’d you go bankrupt? And he said, two ways. Gradually and suddenly. And I think that’s the thing about trends is there’s a lot of things that we’ve talked about for years. It’s kind of the gradual it’s coming. And then by the time it gets here, it’s like, wow, that was fast.

(02:08): AI is a perfect example. I’m going to talk about ai. Of course, how could I not? But AI is a perfect example. It’s actually been coming for probably close to 10 years, certainly just in the very guts of things. I mean, if you’ve ever used Google Maps to get somewhere that has used AI forever, Siri has used AI since its inception. Obviously it’s gotten better, the technology’s gotten better, but those things have been baked into things for a long time. And then chat, GPT comes along and all of a sudden it’s the masses and sudden as a trend. So that’s my table setting. Before I get into it, I’m going to talk about five things that I think will impact the agency world, will impact the marketing world for small to mid-size businesses as well. So the first one, as I already mentioned, is ai.

(03:05): It’s certainly moved past trend, it’s here. But there were a lot of things that came along that way. Social media, mobile marketing, even search quite frankly, all came along slowly and then suddenly they were here. And I think AI certainly fits into that category. I think it’s going to be a little different. We talk about some of those other things like search and pay-per-click when it came along, and social media, we talk about those now as channels. And I think that the realization that we all need to understand on it with ai, we won’t be talking about it as some trendy new thing. It’s just going to be baked into everything. It’s going to be how we go about our day. So for example, a lot of people are using it for content writing, which is absolutely a great use. We teach it. We hold bootcamps to teach people how to use it quickly and efficiently.

(03:59): But I also use it to take a spreadsheet and say, tell me what’s in this. To summarize a document and say, give me the high spots to take a video and say, I recorded a video with a client, for example, a testimonial video and I have it. I could go through the transcript of that, but I take the transcript and say, give me three or four great sound bites. And it extracts from the already great content very efficiently. So I think that kind of usage is going to become just commonplace. We won’t even think about it. We’ll go to chat GBT or some other AI platform every single day to accomplish some of the tasks that we accomplish. We’ll write SOPs that will allow people who have maybe no experience in the field that we’re asking them to work in, and they’ll be able to efficiently use some of these tools like any good research assistant might use in aiding somebody writing a book and aiding somebody who is trying to come up with a draft for some content.

(05:04): It’s certainly going to filter in. I mean, right now there are people that play with Dolly and play with the other image creating tools, but that’s going to get better and it’s going to spill into video. There are platforms today, I’m not saying that they’re there or perfect yet, but there are platforms today that you can actually train with some amount of your voice of you actually speaking and you actually on video and they will actually be able to take any transcript or text that script that you feed it and then create very synced up live looking videos. So those advances are going to just keep coming every single day. But I think the real power of many of the AI tools is just the efficiency and the time saving aspects of it will maybe someday get to the point where it can write better than a human being.

(06:04): I don’t know that we’ll ever get there because again, I always tell people that it can create great content, but it can’t create context. It can’t understand the context in which somebody might be consuming that content. And I think that’s always going to be the element that a strategic marketer can certainly add to anything. Alright, let’s move on to number two. Search. I think it’s, again, it’s one of those that has evolved gradually. I mean whatever Google wanted it to be, it became to a large degree, but it’s gotten, I don’t know if it’s gotten better or not. It’s certainly evolved in terms of the results that they show. And from an SEO standpoint, from a marketer’s standpoint, certainly evolved in terms of how you get those results. But I think we’re actually going to see in 2024 some pretty dramatic changes in really the whole paradigm of search and how search is done and how we get results and what results we’re looking for.

(07:09): Things like answer engines are going to and optimizing free answer engines are going to happen. The fight is always going to be with Google because Google wants to show paid ads. I mean, that’s where they make their money. They don’t make any money in search. They make their money because they’re able to show all those ads right along with search in a very contextual way. So are they going to kill the golden goose or is the golden goose going to be taken from them in a lot of ways without them, unless they respond in an entirely new way in which we get results, I think there will be ad free search engine opportunities. I think that there will be ways in which we can just similar to what you do in chat GPT today, that’s not far off from the model I think of search, where you just go and put in, I’m going on a trip to blah blah blah and I want to visit these and I have five days and here’s who’s going to be in my group.

(08:11): And it’s going to spit out an itinerary for you as opposed to just giving you what TripAdvisor says of the top 10 spots or to go visit because TripAdvisor is able to dominate the search results. Doesn’t mean they’re any good, but a lot of people rely on them. And so I think that that ability to create custom very detailed search is similar to what I think people experience in chat GPT today chat. GPT is not perfect. It’s not real time. It doesn’t have, it’s terribly inaccurate. Its citations are bad, its data is bad. It sometimes says, well, here’s an answer. I don’t know where that came from. But I think the experience that people are having with that type of search query is certainly going to be what we expect. And I think you’re going to see some sudden changes. We’ve had gradual changes and I think we’re going to see some sudden changes in search number three, this is another funny one, video live streaming.

(09:20): They’ve been around now, well 20 years really live streaming maybe 10 years. People have used them in various ways, certainly promotionally. I mean you look at what’s going on with the micro video snack video in places like TikTok and every other platform that copies them. And so it’s not a matter of saying, oh, video’s here, it’s finally here now we should be using it. I mean, people have obviously been using it effectively for many, many years. The reason I put it on here as a trend is I believe that it is going to become, become the basis for how content is created. And what I mean by that is it will be video first for almost all content. And the reason I say that is because marrying it with some of the AI tools I think gives you the ability to get some amazing efficiencies out of a 10 minute video where you’re explaining something.

(10:23): You can take that transcript and create a 3000 word blog post that is formatted exactly the way the current search engine crawling is looking for. You can take that video and cut it up into 27 TikTok type videos where it’ll take out the ums, which I do frequently give you. So I think that while the trend itself of video is hardly a trend, but I believe we’re going to see an explosion in the creation of video because it is the content first platform for lots of your video creation. There’s no denying the trust factor that comes across in video. There’s no denying that people like to consume video. Look at what happens in YouTube every single day. So I think it’s been around, but I’m leaning into the trend, the idea of a video first in terms of its content. Alright, another one. See I’m going to say this.

(11:30): This doesn’t sound like a broken record. Another one that’s been around for quite some time data privacy and complying with data privacy. It’s the whole reason we have Google Analytics four curse it all you want, but Google got tired of being fined by countries that had passed strict data privacy rules. Facebook is certainly moving towards it. Do you remember the days when you could have all these selects that really allowed you, I remember seeing in the early days of Facebook targeting somebody that was trying to target his wife because it was her birthday and he wanted her to be the only one in the audience that could actually see the ad that he placed. And he was able to get that granular that he was able to accomplish that. So the days of that granular level of targeting are certainly gone. And so we’ve been talking about this one for a long time.

(12:28): I mean GDPR, when was that passed five years ago? And you’re not really hearing people talking about it. You certainly are hearing people give lip service to it. You’re hearing people that are doing some just kind of basic compliance with it with privacy policies and terms of services and things like that that have become kind of standard fair. But I think that the adoption came about punitively, right? It is like if you don’t adopt this, you’re going to get slapped on the hand or worse. And that’s never a really great motivator for most people. What’s happening now certainly is that the ability to get third party data is just online, at least is going away. It’s kind of funny, but again, I came into this world of marketing before we had online and digital and you still can today offline get some pretty incredible amount of data.

(13:32): You can buy list of people that live in a certain geography, make a certain income and have a certain disease, have been diagnosed with a certain disease that you want to target. That’s a terrible example. But that’s the kind of stuff you can get offline. So it’ll be interesting to see if that level of privacy ever comes to the offline world, but it’s certainly here in the digital world. Third party data is just going to get harder and harder to get. So what’s the trend part of that? Obviously building enough trust to get first party data and that’s the game we’ve been at forever, right? Enough trust that somebody will give you their email address and other information, maybe their phone number and their mailing address because they want to buy a product from you. That level of data collection and trust building to get that level of data collection I think is going to become the event.

(14:27): It’s going to become more and more apparent that people that don’t have that are not going to just be able to rely on bombing Facebook ads. Alright, the last one, not a trend at all, except aspects of it are, and this is one of those that has been with us forever. I’m just going to throw it out, it’s customer experience, but it’s one of those that I think the pandemic here, I’m here, I’m in end of 2023, still blaming the pandemic, but it’s one of those that I think really elevated people’s expectation when it comes to customer experience and frankly that’s employee experience, that that’s culture inside of organizations. I think those all go very much hand in hand. And I think we’ve seen a lot of rebellion almost with organizations that don’t really get that. And customer experience means a lot of things. And that’s probably the thing that’s changed the most is what that actually means to people.

(15:28): It used to be solely that somebody answered the phone and that they were nice and that somebody was able to get a resolution to a problem that they had when somebody became a customer. It was a, if not joyful, it was at least a convenient experience. And I think that today there are a lot of companies that aren’t doing that, even matching that level. I mean, try getting an insurance company on the phone. Try getting a rental car agency that you left your prized water bottle in their car. I know that’s a very specific example. Try getting them on the phone, right? It’s not going to happen. So there’s a lot of people that are not doing it. So in a lot of ways what the digital presence has really done and AI bots have really done is they’ve given people one of two paths.

(16:26): They’ve given them the ability to wall off any need for human interaction, right? It’s like, here, talk to our bot. Go through, fill out this form, go through the phone tree to get to the answer that you want. So it’s given people the ability to actually provide no service in a lot of ways, but it’s also given people the ability to provide the level of experience that somebody wants. There are certain instances in which, I’ll use an example of my eye doctor. When I am up for an annual exam, I can go to their website and I can make an appointment. I will get a notification when that appointment is coming, I’ll go to the appointment. And there was no need. It was actually far more efficient for both parties to have that online scheduling. So there was really no need to have somebody answer a phone and say, oh, okay, well we’ll get back to you like five times later.

(17:27): We finally get the appointment schedule. So it offers the ability when used correctly to offer a frictionless, very speedy, very convenient experience. And I think those are elements of an elevated customer experience that people want and expect today. Married then with true trust building value at every possible interaction. And I think that that is clear to me that if we’re not reaching out to our existing customers and making sure that we are meeting their evolving needs, that we are helping them achieve the goals that they want to achieve, we are helping them with the transformation that they want to achieve. That’s our job. Having an AI bot or having an FAQ section on our website, those are nice. Those are things that give people the speed and convenience that they want, but then we need to supplement that I think with what I used to call hugs and handshakes that we can do, even if it’s done online, done via Zoom, done via one-to-one video on Loom.

(18:38): Those types of touches people are expecting. And the beauty of elevating your customer experience is that not everybody’s doing it. So it is a brilliant way to stand out. Alright, that’s it for my wrap up of the 2024 trends. Nothing too trendy in there. It’s more a matter of recognizing that trends happen gradually and then suddenly. Alright, take care. I’d love to hear your feedback. If you’ve got any comments or thoughts on these trends you’ve got, anything you want to add? I’m just John at Duct Tape Marketing and of course we love those reviews and five stars that you give us in the various places that you listen to your, all right, take care. Hopefully we’ll run into you one of these days soon out there on the road.

John (00:00): Welcome to another episode of the Duct Tape Marketing Podcast. This is John Jantsch. My guest today is Ash Ali and Hasan Kubba. We’re going to have two guests today. They’re award-winning authors and entrepreneurs. And despite not going to University, Ash became a serial tech founder and the first marketing director of the Unicorn Startup Just Eat. Hassan built a successful startup from his bedroom with nothing more than an online course and a yearning to escape the rat race. They’re now international bestselling authors, coaches and keynote speakers, and we’re going to talk about their latest book, the Unfair Advantage, how You Already Have What It Takes to Succeed. So Ash and Hasan, welcome.

Hasan (00:43): Hello. Thank you. Thanks for having us.

John (00:45): Hi. Awesome. So the book starts out with this premise, and we could probably do the whole show without me asking another question, but here it is. Life is fundamentally unfair. Who wants to take that dollop of hope?

Hasan (00:58): I’ll take it. I’ll take it. Go on. So life is unfair. Yeah, that is the underlying principle behind our book is that life is not fair. And sometimes when you get into self-development like I did, and I still enjoy a bit of self-development, you learn that what you got in life is what you deserved. You built the life that you’re living now. You designed it, your decisions led to the moment you’re in now, and all these kinds of quotes and beliefs and mental models to make you take responsibility for your life, which is a very useful tool, but it’s limited because it’s not actually that accurate. So one of the ways to look at when we talk about this in the book is it’s all about mental models. So there’s one extreme, which is to think that all success is based on hard work and merit.

(01:46): And the other extreme is to think it’s all luck and unearned. And the reality is squarely in the middle, there’s a lot of serendipity in life. There’s a lot of luck of births and genetic lotteries, and there’s a lot of things that just happen because you are in the right place at the right time. But at the same time, you can stack the deck in your favor, you can make the right decisions, you can be consistent in how you think, in how you behave and the decisions you make to lead towards success. So it’s a mixture of both. Life is unfair and ultimately we’re so lucky and we should all be so grateful for everything that we have going for us. And at the same time, we can also exert our own agency on the world. We can also take bear some responsibility. We can also take control of our lives to an extent.

John (02:30): Yeah, it is interesting. I mean, we all know people have had everything handed to them, all the funding, all the backing, all the mentors, all the whatever, and they’ve still found a way to piss it away, haven’t they? So it really is kind of that combination. So let’s maybe start out by defining what an nfa, maybe some examples of what you would call an unfair advantage that people tend to recognize.

Ash (02:54): So an unfair advantage is something that’s unique to you based on your circumstances and also based on your background and who you are as an individual. There’s so many books out there that talk about strengths, but what we do is talk about your strength, but also about yourself as an individual, as a unique person. So we talk about life is unfair and it’s not a level playing field, but sometimes when life is unfair and it’s not a level playing field, some people can grow up with a victim mindset and a victim type of thinking say, I didn’t have this, I didn’t have that. But actually what we say in the book is actually how do you turn that around? How do you make that stuff that you felt was unfair growing up in poverty or growing up in an area that wasn’t great? How can you turn that around and make it part of your authentic story and use it to an advantage? So an example for me would be I grew up with little money and when I start companies now, and I know a lot of listeners are listening here who run small businesses when you don’t have a huge amount of money for marketing budgets, for example, I’m the perfect person to come in and work with you because I know how to be resourceful because I had no money. And so my mindset is always based around being resourceful. That’s just an example of something that you could use

John (03:59): Straight. But again, the flip side of that, I guess we all know people who had everything and should have made it. We all probably know at least somebody, or at least you’ve read their story of somebody that never should have. Like you said, they didn’t have the education, they didn’t have the backing, they didn’t have the money. They didn’t really, seemingly didn’t seem that smart. They’ve made themselves successful the way we define that. So I guess to Hassan’s original point, it’s kind of somewhere in the middle. It

Ash (04:32): Is somewhere in the middle. It’s interesting because I’ve got a daughter now who’s growing up in privilege, and I look at her and I look at my life and think, okay, does she have the fire in the belly? And what can we do to help her have the same mentality of working hard and trying to achieve things in life? And one of the things I found was that interestingly is that constraint does kind of foster creativity. And if you just give everything to your children, for example, straight away, then they’re not going to feel grateful for it straight away unless they’ve worked for it. So sometimes having constraints does make you more resourceful and more creative. And that’s as an example of something. We live in an abundant world now where everything is available quickly. You can order your takeaway quickly, you can order your cab quickly, and they’re growing up in a different environment compared to us where we had to wait for something, but we had to have some patience around something. So it’s understanding what constraint is and how to manage that, I suppose was,

John (05:27): Yeah, of course, it’s so cliche now, but I like to tell even 30 year olds about a dialup internet and things of that nature. Can you imagine that now it would take 10 minutes and we had to take turns. Who could use it? Only one person could be on at a time and pretty crazy. So I think would you classify or would you characterize this book as a business book or a self-help book?

Hasan (05:50): Yeah, good question. It really is in the middle because what we’ve done with our book is we’ve the origin of the book, let’s get into the origin. We did this book because we were getting pitched by loads of startups for funding, and it was just like Shark Tank essentially that’d come in and pitch us. And we thought, what is the difference that makes a difference here? When we confer between ourselves, we’re like, what is it with some people that we’re like, even if we did believe in them, they’re not going to close out their funding ground. Nobody else is going to believe in them and they’re going to really struggle here. And what is that difference? And we start thinking about this and really diving into it, and we decided to write this down, this idea of the unfair advantage, it’s essentially a sustainable competitive advantage for a big business.

(06:32): It’s the type of thing Warren Buffet talks about in value investing. You want a business that has the economic moats, the defensibility that it’s going to sustain. And it’s the same thing for individuals because at that early stage of a business, when you don’t yet have a product, even sometimes when you don’t yet have customers, you don’t yet have traction and sales, how are you going to judge it? Well, you’re going to judge it by the team, by the co-founders. And when you’re judging it by the co-founders, that’s when you have to try and decide, okay, what have they got going for themselves? What do they have that’s going to allow them to push through? Do they have a track record? Do they have something that gives you the idea that they’ll be able to get into this? Do they have the unfair advantages? And essentially that was the idea behind the book, and that’s what made us think about how we can help people to gain that kind of self-awareness to know what kind of business to go for, to know what kind of strategy to go for. Should you raise funding? Should you bootstrap? Who should you partner with? These are the kind of decisions we wanted to help people with at that early stage. So we’re just bringing it back to the individual. So that’s why he’s in between a business book and a self-development. It’s about the early stages of a startup to

John (07:39): Workshop. So I think there are some unfair advantages that are pretty obvious that people could identify. But if I’m out there listening, what are just some of the places that you go looking? I know you have a framework you call the Miles framework, so we can kind of go letter by letter for the acronym, but what are some of the places maybe that are less obvious that you’ve said, Hey, these are unfair advantages that people don’t even realize they have? So

Ash (08:03): The Miles framework is, it stands for money, intelligence, location, and luck, education and expertise and status. And it sits on top of mindset. And we talked earlier about why it’s important for people to understand the unfair advantage in the context of business because business is all about people. And most investors invest in small startups and early stage startups because of the people, not because of the idea itself. It’s the founders themselves. And so if you can identify your unfair advantages and then amplify those in your pitch, in your message to hiring people to your or getting customers, it’ll help you get your early traction, which is what starts a business. So coming back to the Miles framework, it’s about understanding within each one of those miles frameworks in each one of those letters, what you have that’s going for you. And one of the big ones is insight.

(08:54): For example, when you’re starting a company, if you have insight into something that nobody else has and you are starting a business around, that’s a very powerful unfair advantage. And there’s so many case studies in our book around that about specific insights around that. Another one is being in the right place at the right time, the location and luck. Can you find the right co-founder? Can you find the right customers who are close to you potentially who can become customers straight away? Status is another one, your network. And here, when you are starting a business, if you know how to raise money quickly and you have a network, that’s an unfair advantage. And if you need to go out to the market to raise money from ground zero and have nobody, no network, it’s much harder to do, much harder to do. And we know that’s how investment generally works. So there’s lots of little examples in different places for different types of projects or businesses. It depends where you want to apply the framework itself, whether it’s a project, whether it’s your career, whether it’s a business itself.

John (09:48): I want to come back to insight in a minute and have you share some examples to help clarify that one. But let’s talk about luck. Some people are purely lucky. I mean, they run into luck, right place, right time, like you said. I would say a lot of entrepreneurs have come to the realization that they make their own luck, and that’s almost something that’s earned as opposed to something that’s an unfair advantage. How would you respond to that notion?

Hasan (10:12): I totally believe in making your own luck as well. So we talk about luck and we talk about the fact that it’s overlooked and luck exists. Hey, luck does exist, talent does exist. All these books has become trendy to say there’s no such thing as talent. Just work super hard and get the 10,000 hours in. And that’s enough. These things exist. Tiger Woods could swing, a golf could swing a club before he could walk. These are the kinds of things that is pure talent. Oprah Winfrey was giving speeches to whole congregations at church when she was three years old. So these things exist, but making your luck also definitely exists. We talk in the book about how you can actually increase your luck. There have been psychologists who’ve studied the phenomenon of people who think of themselves as lucky, less of people who don’t, and how the fact that they think of themselves as lucky just makes them more proactive, makes them more observant to opportunities that come up. And it’s been literally proven in studies. So it’s quite interesting that you can make your own luck. We say, put yourself out there more, increase your surface area to luck, and maybe more lucky things will happen. So it’s essentially rolling the dice. Just keep rolling it. No one’s counting how many you’re throwing the D, how many times you’re throwing the dice. If you keep rolling, you’re more likely to roll the double six.

John (11:25): Yeah, I actually, I started my blog in 2003 that I talk about being in the right place at the right time. That was luck to spot that technology. But also it led to my first book four years later. But that point, I had also written a thousand blog posts. So I always talk about, really that was a lucky decision on my part to go that route. But then I do think you can also then turn that luck into something that is very fruitful.

Ash (11:52): Yeah, absolutely.

John (11:54): So what’s your unfair advantages? And I’ll let you both answer that one. Go on. For example, as you mentioned, you didn’t go to college, so we can

Ash (12:03): Take, okay, I’ll

John (12:03): Stop the college degree from Oxford off the table, right?

Ash (12:07): Yeah. That can be an unfair advantage if you know how to use it. Some people don’t know how to use that as well. We see people coming to us and like, oh yeah, I went to Oxford in Cambridge or wherever, and it’s just par. It’s normal for them. But actually that could be an unfair advantage if you know how to use it properly. An unfair advantage, there’s several different things with strength. They can be double-edged swords as we call them. So having something and not having something, and we talk about constraint earlier on. I’ll go through it from my perspective, which is the double-edged sword version of it, and has someone go through it from his perspective. So from my perspective, I had no money growing up. So now when I’m building startups, I’m really shrewd and very lean and I can build things very quickly and I’m very resourceful.

(12:46): And actually what it does has done to me is made me more creative. So one of my high skills is creativity, intelligence, and insight. I have lots of insights with businesses because I’m doing things all the time. I’m always taking action. So I’m seeing opportunities and getting insights and different things and intelligence. There’s different types of intelligence. A lot of people said to me, Ash, you’re really cool. You’re the glue amongst your friends. So I’m good at bringing people together and doing things together, which is cool. And I don’t like to be the smartest person in the room. I’d rather not be the most intelligent person in the room, but I can learn from other people quickly. So I suppose that’s the eyesight location and luck. I was born in Birmingham, which is the second biggest city in the uk, an automotive retail industry kind of community.

(13:24): And the tech industry was booming in London. So I moved to London at the age of 19. If I didn’t move, I wouldn’t have had the same opportunities, wouldn’t have been able to join companies like Juste and do the IPO and luck, the IPO, how many companies IPO, far and few between once again, and there’s a luck factor behind that and the right timing of that. And then seeing how that would work out. Education, excuse. I didn’t work university, so I didn’t feel entitled. So that’s why I kind of did everything and anything. And I built my expertise up in digital market and the time when everyone wanted to know how to do SEO and online marketing, I was there. And then status a few, and your Rolodex of contacts, I didn’t know many people, but now I know lots of people. So if I need to do anything now, for example, I can open my black book of contacts, LinkedIn network connections and make things happen because of my status of having connections that I’ve built up over time. So that’s become an unfair advantage for me.

John (14:14): It’s interesting, as you said, the degree from a prestigious school used to really mean a lot. It feels like particularly in the entrepreneurial space, it’s more about what were you doing for your summer job than what degree you got or your side hustle or whatever. It seems to actually hold more weight than college. And I think a lot of it’s because people realize college is great for making connections. What they teach in a lot of a marketing course in college will have very little application to what it’s like to market in the real world. And so that education, the actual learning classroom education is probably not that valuable.

Ash (14:52): I mean, if you want to learn,

John (14:53): So Hasan, how

Ash (14:54): Then the fastest way to learn is reading blogs like yours, John. And if you want to learn about marketing, you can learn a lot more from reading blogs and marketing books can get old very quickly. What happened some time ago, timing-wise might not work now. So it’s keeping fresh and up to date with knowledge. I think that’s really important. And we talk about this in a book about, there’s three aspects of university, but I’ll let Hasan talk about the Miles’s favorite from his side and what his advantages are.

Hasan (15:20): So for me, so it is easier to simplify it to what is your unfair advantage. But the reality is we all have a set of unfair advantages and a unique set of them. And that’s why Ash goes through so many. For Ash, I would definitely say his creativity is just one of the top things about him. And the fact that he just gives things a go, he just goes for it. So for me, I would say that it’s my ability to learn really fast. So I think I have that kind of the intelligence where I pick things up fast and then I’m able to communicate them. So one thing that really helped me to get my initial clients and start to develop and get referrals is the ability to build rapport and build trust very quickly. So I think that’s partly just from my ability to absorb information and knowledge in a space that’s so new and something, one of the main things I was doing was SEO.

(16:08): I was doing branding and website stuff, but SEO and getting people to the top of Google was huge. And so the fact that I was able to explain it to local businesses, build connections with them, build trust, I think that massively helped me. So that was huge for me. And then you can go further back and just say, listen, I was born in Baghdad, Iraq, and I came to the UK and London when I was three years old with my family to escape the war and all of that. So my unfair advantage is we moved to the UK when I was a baby, and I grew up here in London. If you imagine if I’d come when I was 20 years old and I’d have the thickest accent, and I’d have so much difficulty in terms of just how I come across the status side of it in terms of building rapport, building trust. So this is so lucky. So you can kind of go into the genetic cluttery of it all. You can go into where you grew up and what kind of schools you went to. You can go into your ability to skill stack and build your skills and expertise and learn things quickly. So I think that learning side is kind of the massive piece for me.

John (17:06): So I suspect as you’ve both gone out there and maybe given talks on this or done webinars on this, that ultimately somebody comes to you and says, look, this is great, but I don’t have any unfair advantages. What do you say to that person that feels, especially since mindset really sits on top of this, what do you say to that person that has that mindset?

Hasan (17:28): So I would say that essentially this idea, and as has touched on this idea of double-edged swords, what you think is a disadvantage, you can turn into an advantage, and I’ll give you an easy one. So we have a few examples in the book of people who had a kind of classic disadvantage. So a classic disadvantage is a woman entrepreneur. So woman founder. The example is Sarah Blakely, founder of Spanx. Now, if you think about it, what was her unfair advantage? Okay, well, it was tough. She had no idea about how to raise funding. Nobody would believe in her. She had no connections in that space, et cetera. But what did she have? She had an amazing insight into a problem based on her status as a woman, which is that this idea of shapewear and Spanx turned out to be Spanx. She would cut off the feet off tights.

(18:15): A man wouldn’t have come up with that, wouldn’t have had that insight. The same with Tristan Walker, who’s another example in the book. He grew up in the projects in, I think he was the Bronx maybe, if I’m remembering correctly. Queens actually Queens in New York and really poor. His dad was murdered when he was young. But hey, he was smart. He got scholarships, he got into good schools. He spent a long time thinking about what his big idea is. In the end, his insight was that black men need a different shaving system than other people do because they have more ingrown hairs. And so he developed this single blade shaving system. He used different wrappers who also from his location, so the wrapper Nas grew up also in Queens, and then he promoted his brand, and then eventually he was acquired by Procter and Gamble for $30 million.

(19:00): So it’s like what seems like a disadvantage you can use to your advantage if you grew up poor. Then you have an insight into how poor people live, what needs they have, what mass market products you might be able to create, let’s say. Or if you grew up as whatever, you grew up from another country or you’re learning languages or there’s all these different aspects to everything. So it’s all about your mindset. If you have a growth mindset, and we talk about in the book the growth, the reality growth mindset because we want to root it in some reality, then you can grow and you can turn what seems like a disadvantage into an advantage. And listen, if you’re listening to this podcast, if you’re able to read this book, if you probably have a lot to be grateful for, so you just need to do a sort of an audit. And gratitude is one of the underlying themes of our book.

John (19:46): And it’s interesting too because as we grow up, a lot of the things that drive our parents or teachers crazy ultimately come out as an advantage. We were told they were a negative. For example, my parents used to always joke about how curious I was and always getting into things because I had to teach her same thing. I was told for a long time that that was a problem that has served me extremely well in my professional life. And I think that’s sometimes we just have to overcome what society has told us is a negative, don’t we?

Ash (20:15): Yeah, absolutely. When people focus on your weaknesses more than your strengths, that’s when you start to misunderstand really what your unfair advantage is, because we’ve all got strengths. And the idea of the premise for the book is to double down on your strengths rather than focus too much on your weaknesses and then plug those gaps where you can appropriately and understand that we work in teams and people is about business, is about people. So it’s not just about you as an individual.

John (20:41): So Ash Hasan, tell people where they can find more of you, more of the work you’re doing, and obviously grab a copy of the unfair advantage.

Hasan (20:49): Yeah, we’re all over social media. So I’m @startuphassan. Hasan is spelled with one s and Ash, is it @AshAliuk. Ash, for most of your socials you can find us and our website is unfairadvantage.com

John (21:02): Awesome. And the book is, will be available in, I don’t believe there’s an audio version, is there?

Hasan (21:07): There is,

John (21:08): Yeah, there is. Okay, so an audio and then in ebook format as well as hardcover and available depend upon when you’re listening to this available everywhere that you buy books.

Hasan (21:19): Yeah, it’s available now at time of recording. It’ll be released tomorrow, so it’ll be available by time

John (21:24): From that. And I should have mentioned this, but the book has been awarded. I don’t have it written here. Tell me the best business book in the UK in 2021 or something. You could do it better than I just did. Tell me what the award was.

Hasan (21:36): So we were surprised and happy to learn that we’d won our category of the startup category of the business book awards. And then it was like 12 different categories, and then it turned out we’d won the whole thing as well over all the categories. So we’d won the business book of the year 2021. It was actually, it’s based in the uk, but it’s an international award as well. The only country that the book hasn’t come out yet until now is in the US and Canada in North America. So yeah, it’s done really well. It’s really popular on Goodreads, it’s on YouTube a lot. Viral videos on YouTube summarizing it. So if you want to check it out a bit further, you can see some summaries on YouTube. You read all the reviews it’s doing, it’s thankfully, it’s spreading by word of mouth. People are loving it.

John (22:21): Awesome. Well, thanks so much for stopping by the Duct Tape Marketing Podcast, and hopefully we’ll run into you both, somewhere out there on the road.

John (00:03): That’s right. It’s just me. I got a topic I want to talk about. This is a topic that maybe I haven’t talked so directly about, but in a lot of ways it’s been a big part of my work. It’s something that I’ve talked about for years, but I want to hit it head on today. And that’s if you are a service provider. I want to talk about the idea of productizing your service. So what do I mean by that? What I mean is creating service offerings that you can describe, explain price deliver almost as though they were a product I have for years delivered something we call Strategy First, and it is essentially our approach to developing strategy. So it is a service that we offer to business owners and we have dialed that in so thoroughly. Frankly, it was actually the genesis of Duct Tape Marketing of me creating the approach to Duct Tape Marketing was that I was a little frustrated going out and essentially delivering my marketing services in not a one-off fashion, but in a custom fashion almost whatever somebody needed.

(01:15): That’s what we designed a proposal around. That’s what we delivered. That’s how I priced it. And a lot of agencies do that. You can build a nice business doing that. But I was frustrated because I just felt like I was working more and making less as I grew that. So I created this productized approach. I called a marketing system, and we all the way down to exactly what happens when a client signs on to all the way through the way they’re onboarded. Everything we deliver in the final is really packaged up in a way that I can call it a product. That’s why we gave it a brand name. So that’s the idea behind a productized service. Now, I used an example of marketing, frankly. Accounting can have productized services, legal services can be productized, any kind of consulting can and in my view should be productized.

(02:07): There are some, I think some glaring benefits to why you might want to do this. The first one I started talking about is scalability. It is very difficult to scale an offering that is made up every time that is new for every customer. That is, and here’s my favorite word, bespoke. It’s very difficult to scale that because in a lot of ways it takes the enormous experience and let’s call it brainpower of the person who can be that nimble and deliver every single time. Now, there’s probably a place out there in the world for completely customized versions of service delivery, and in fact, we do it in the right circumstances. But for the most part, if you want to scale a service business, you have to create something that is very easy to message, that is very easy to explain. And frankly, when it comes to scale is easy to actually delegate and hire people and to train people how to deliver that package.

(03:13): And that’s much harder to do if essentially everybody’s making it up every single time they go out and work with a new client. So to me, that scalability is probably the leading benefit of doing this. I already mentioned this a little bit, but the sales process gets so much simpler Instead of, okay, what do you need? Okay, we’ll put together a proposal. Okay, we’ll refine their proposal. We’ll make it fit to the budget that you have. When you’re able to walk in and say, here’s what I’m going to do, here’s what you’re going to do. Here are the results we hope to get from this approach. And by the way, here’s what it costs. Shortens the sales cycle, which to me is a great thing. You get a yes or a no, but it also makes it very easy for you to explain exactly what somebody’s getting.

(04:01): I mean, that’s one of the most valuable things you can have in a sailing situation is something that’s very simple to explain. Somebody can get it. They can see on one sheet of paper, here’s what we’re going to do. It also will lead to much higher profit margins. And one of the reasons for that is that when you’re constantly having to figure out how to serve a client, have to write proposals, how to create whatever the deliverable calls for based on the scope of their proposal, there’s a lot of learning that goes into that. And if you can create a repeatable process, you will get better at delivering value because you’ve done it before. You will get faster at delivering value because you’ve done it many times now. And consequently, those two things alone will lead to much higher profitability. But the final piece that I think a lot of people underestimate is when you’ve got something that you can actually show somebody, here’s a proven process to get you results, it is very simple to explain to them exactly what they’re going to get.

(05:15): You can also charge a premium. So you’ve got really that profitability working two ways for you. You can generally charge more for a name branded service offering that you can now deliver very affordably or much quicker, or you can delegate to work to people that are at a much less experienced than you. It just leads to a much more profitable, and here’s the final reason to do it. And frankly, if all those other reasons weren’t enough, the final benefit or reason for doing this is that it’s a better customer experience. I know that everybody, I mean, everybody we talk to, it’s like, no, I want want something that’s tailored just to my needs. Well, on the surface, that sounds really great, but you rather have something that I’ve actually worked on for years and refined and evolved and seeing what works and seeing what doesn’t work.

(06:12): I can deliver. You can deliver much greater value by having a proven process. Now it takes time to prove that, to refine it. I mean, we’ve been doing this for 25 years and it’s evolved every single year for us as well. So to me, the product itself, because we have focused on here’s what you get, here’s what it costs, has gotten much, much better. Now, are there challenges in productizing? Some of what you’ll run up against is just what I mentioned. People want a custom approach. They don’t want cookie cutter. They feel like if it’s not created just for them that there’s something less. And so it really becomes important for you to not only create that productized approach, but be able to communicate very effectively the value, what’s in it for them. I think when people start to realize that by creating a repeatable system, you give people not only the option of getting a better end service, but you also get far better at delivering it.

(07:18): There’s just so much more value in it for them. So I think that the messaging really has to be about that is a lot of times people focus on, well, we can deliver this better, or we just get, we’ve got this down so that we have a very fulfillment engine that is very productized. It’s very systemized. But the key of course, is helping that buyer, that customer, that client understand why that is so much more valuable to them. So how do you get started doing something like this? Because one of the challenges I think a lot of people have in productizing, if you will, is that they’re serving such a diverse market. It’s very hard to actually create one or two or three things, packages, products, if you will, for all of the services that they might be able to offer. So it does help if you can narrow your focus first off.

(08:10): So I’m not necessarily saying a niche, maybe it is for you, but at the very least, who are the top 20% of your customers? What do they need today? What are the problems that you’re solving for them? Could you actually create or think in terms of creating a package just for them? You’ve got to standardize the offering. It’s not enough to just say, oh, this is this and it costs this. You’ve got to work on even the promotional materials need to standardize. Here’s what you get. Here are the benefits of this. Here’s why this approach works. So just even creating marketing materials for it, you have to standardize, but then you have to start writing SOPs. You have to actually map out, whether you call it a fulfillment engine or whatever you call it, you have to map out when this happens, then this happens.

(09:00): So at the global level, you have to at least have the little boxes and arrows that point to that. But then each one of those boxes, particularly critical steps, you need to maybe create an entire training process or SOP around. And I know that this can sound like, well, it is time consuming and it sounds like it because it’s, but ultimately, if you spend several weeks even creating this repeatable system that then can serve you for years and that you can delegate and you can scale your business and you can be more profitable, probably the best couple of weeks that you’ve ever spent in terms of working on your business after you productize it, then it’s field testing. I mean, you could sit in a lab all day long and create what you think is the most brilliant approach to delivering your services in a productized manner.

(09:51): Just go out there and start doing it. Start telling people, maybe give ’em option A, B, and C, but this is what I’m going to do. This is what you’re going to do. Here are the results we hope to get, and here’s what it costs. Do you want it or not? And start fulfilling it. You’re not going to refine this thing until you’ve done it dozens of times. And that just takes practice. It takes experimenting, it takes trial and error. It takes listening to the feedback that you get from your customers. It takes really evaluating, are you getting them the result? Are you getting them a better result than they could have gotten somewhere else? So how do you market this productized service? Well, if you think about it, it is the same as marketing a product. In a lot of ways. People have to understand what it is.

(10:35): They have to understand what’s in it for them. It has to address a problem that they’re trying to solve. I mean, those are all things that really any good marketing does. But I think it’s probably important for you if you’re going to productize. It’s very important for you to, I think it’s very helpful if you give things a name, give it a brand, create collateral around it that shows somebody exactly what they are going to get, and then focus a great deal of your marketing. It’s the same for any professional services on trust building, on explaining not just the components, not just selling the components. In fact, in some ways, the productized approach doesn’t really even become an issue until somebody starts saying, how will this work for me? And then you’re able to give them the very specific way it could work for them.

(11:21): But all of your marketing education, even though you have a productized approach, is going to be around educating on the problems that this productized approach solves. I always make the joke that I sell a marketing strategy. Nobody ever wakes up and says, I’m going to go buy some marketing strategy today. But they do wake up and wonder why they’re competing on price. They do wake up and wonder why they can’t fill their pipeline. They do wake up and wonder why their competitors are always ranking ahead of them in search engines. And to a large degree, those are all problems that an effective marketing strategy can solve. So even though we’re productizing the service offerings as a way to scale, as a way to be more profitable, as a way to more easily help people understand the value they’re getting, we still have to build trust.

(12:11): We still have to create a customer journey that turns us into the trusted advisor and the productized approach happens just to be a delivery mechanism for how we get them, the results and how we communicate the results, and frankly, how we differentiate. So many people are in the marketing world, I’m sure in your whatever service world that you are in, so many people are just selling the idea of the week, are selling, the tactic of the week are going to clients and saying, what do you need? Sure, we can do that. And so this proven process driven way to deliver value in a way that’s very easy to understand is also a great different in a world of service offerings. Alright, that’s it for today. Always love to hear your comments and feedback. And if you’re on one of those services like iTunes or Spotify, make sure that you give us a review, a glowing review, of course. But we hope you like the show and hopefully we’ll run into you one of these days out there on the road.