speaker-0: Hello everyone and welcome to another episode of Decline Invite, the only invite worth accepting. I'm your host, the extroverted IT leader, Ryan Drumheller, and today's episode is one I think business leaders, marketers, and you know, honestly anyone making decisions from dashboards and reporting really needs to actually hear because data is not always the problem, the interpretation is. My guest today is Philippa Games. A digital analytics expert who has reviewed more than five thousand websites over the last twenty five years and consistently found the same pattern. The numbers often look convincing, but the conclusions businesses draw from them are not always correct. Philippa is the author of Website Wealth and works with organizations around the world helping leaders avoid making competent decisions based on incomplete understanding. So today's conversation is gonna get into a lot of analytics, Cabo. AI generated reporting, decision, drift, and hidden opportunities inside website data and why activity is not always the same thing as business value. So welcome Philippo the Decline Invite. It's a pleasure to have you here today.
speaker-1: Hey, thank you, Ryan. I'm looking forward to this.
speaker-0: Absolutely. So one of the first things we like to go into is some icebreakers. ⁓ and you've reviewed more than five thousand websites over your career, which is just incredible, honestly. And you know, a lot of pattern recognition. You know, what originally pulled you into analytics and digital strategy?
speaker-1: Gosh, that that was actually a long time ago. But, you know, analytics just grabbed me ⁓ when I started to get into this field, because it was so new. You know, we used that to sort of do audience research and market research and you know, so like if you had a magazine or a newsletter, you could tell how many of those got sold. But that didn't tell you how many people actually read. The thing, how many people saw any particular page, how many how people responded to it. Right. Now we've got all this amazing data about what's going on that we can learn so much from to make decisions about not only improving our website, but also growing our business. and the thing that I really love about it is or that really kind of inspired me to do more is is But it yeah, you can geek out on it. I mean, I I I really liked it when you said you're the extroverted IT person, because you're absolutely I mean, yeah, that's like that's like a contradiction in terms, right? most most IPT people are sort of really geeky people that hide behind the screen and and talk techie all the time, right? But the thing that I love about the analytic strategy piece is that yes, you have to be good at sort of an ana analytics mind. But it's also really creative because you've got the other side of it where you go, okay, here's what we think's going on. What the heck are we going to do about it? And that's where analysis doesn't help. You've got to have some imagination and some creativity and some understanding of the market and all of that. So it's a really, I think, lovely combination of of the two. Often a sort of polar opposites, right?
speaker-0: definitely get that comment a lot. ⁓ I I might intentionally stick it out there, but it's pretty evident when you start talking to me. ⁓ but yeah from from a data standpoint, I talked to a marketer recently who who kind of made the same you know conclusion and and direction that you're going, which is, yeah, here's all the data, but what are you gonna do with it? You know, what story are you going to tell with it? And you know, how does that link together? And as somebody who has just launched all kinds of different pieces of content and newsletters and yeah, I'm looking at the things you really just said, which is, yeah, how many people actually opened the thing? That's great they subscribe, but did anybody read it? ⁓ you know, if they're not reading it, that's a problem. But then if they did read it, what things you know pulled them in, you know, what things should I keep going on? It's like the the real story, right? I want to know those pieces, not how many people signed up for it.
speaker-1: Right. Right. Well that's a good start, but right.
speaker-0: Yeah, yeah, I exactly. So, you know, in analyzing all of these kinds of things, what kind of patterns kept showing up for you over and over again?
speaker-1: I think well, I think honestly one of the patterns that that i is my my decision services, my decision confidence services I should say, ⁓ is a fairly new service that I'm that I'm developing. ⁓ and I think it's based on the fact that maybe for a lot of people analytics is kind of boring and the data is kind of boring and they'd really rather go out and just, you know, do the business and do the stuff they really enjoy, which is totally understandable. But what that leads to, I think, is people taking maybe a lot of the surface numbers. You know, the the the p page one of the dashboard that says, you know, this is up and this is down and and just going with that. Right. I I mean honestly, I think it's not to insult anybody, but it's easy to do and it look you know, you get especially now with AI, you get these beautiful looking dashboards. And you go, ⁓ okay, so this must be what's going on. Let's let's either assume everything's great and carry on. Or, ⁓ well, maybe we need to do something, but then, you know, not really thinking through what is actually happening, w what's the big picture, what do we really need to to question here?
speaker-0: Yeah, I I mean, I think as a human, right, we just look at the visual data like like you said and go, ⁓ okay, and don't even like maybe even question what where is the data coming from or you know, is this even right? And we're just kind of running with it. ⁓ you know, at at what point do you do you think that you noticed that maybe businesses were too trusting of that or they're making like like confident decisions based off looking at those types of data points?
speaker-1: I mean, all the time. I mean, you know, as I as I get involved with working with a business, I think that there's a lot of times when when that becomes apparent. And I I I was just thinking the other thing that is really interesting, and maybe it's a human psychology thing, is that people tend to concentrate on the bad news more than the good news. Now, okay, if your sales are down, obviously you need to do something about that. ⁓ but Luckily not so much these days. ⁓ back in the old days when we had universal Google Analytics, for example, that showed the bounce rate, ⁓ people would absolutely obsess about the bounce rate. That's the number of people, right, who leave your site pretty much qu immediately ⁓ and don't do anything. And so say let's say they had a 50% bounce rate, right? They'd go nuts. Like, what am I gonna do? What am I gonna do? Why is this this is terrible? Instead of focusing on the fifty percent people who stay and actually look around and actually do stuff and how can we get them to do more? You know, what what can we do to improve that bit because they actually liked us and they want to engage with us a bit. ⁓ and I I mean I I think this is probably just human I know I I used to do a lot of speaking, right? And you get your speaking evaluation and you know, you could have hopefully an awful lot of people saying you were you were great, this is really helpful, I really learned a lot. And then you have the one person that says, this was completely useless. And and you go, you obsess about that, right? so so really thinking about which which information is it that you really need and that it will be the most useful and you know expedient for you to do something with, as opposed to what is it that just triggers me and I obsess about?
speaker-0: Yeah, the psychology piece is ⁓ so fascinating to me, like to the point where I almost thought about going back and getting, you know, a psychology degree and I stopped myself because I have too much education and I I I do kinda understand humans I think better than most and and what triggers them and and what draws them in. and yeah, you're you're absolutely right. Like ⁓ w even in IT, right? We're we're we're constantly worried about fixing the next thing over and over and over again. We never really stopped to celebrate or do any of those things. So we're on to the next like fixing bad situation. that if you get a data point that's like, look how good this is and you know, it looks like there's might be some room to even go further with that. It's not as important as like like what you said. It's like, yeah, but that one's that one's red. Like that that means bad, right? Like our like our eyes and attention and mind dripped over there. So why what do you
speaker-1: I mean i if it's red because things are about to blow up, yes, you need to do. But you know, if it's an outlier yeah, right.
speaker-0: Someway. Right. Yeah. Yeah, I mean i again like training yourself as an outlier and and I was gonna try to save this for as long as we could go, but like as a data, you know, data point ⁓ you know, situation, at some point you become a storyteller, right? You become here's what's happening with the data and these are the actual things to actually be concerned with, think about, grow from whatever the story is to tell from the data, but you have to translate it, right? So d otherwise it's just it's just numbers at the end of the day. And if those individuals don't know how to translate the numbers then it's useless to them. So w w why do you think that, you know, so many organizations kinda make the mistake with the activity over maybe the actual demand or the actual s what the story trying to tell you?
speaker-1: think you know sometimes business leaders maybe I I mean I see a lot of businesses that work with agencies and this is not to knock no agencies, there are a lot of very good agencies, but at the same time there are a lot of agencies that are reporting on the metrics that mean that are important to them, such as, you know, click throughs from ads, right? So So if we have a lot of click throughs from our ads, we're doing our job and you should continue to pay us. And they may not have all of the data that goes through to the end result. I mean, I I had a a a CEO come to me recently, and he had a big contract with an ad agency and exactly the situation that the ad agency was reporting all of this success in terms of of click throughs and and visibility and so forth. and he really felt like he wasn't seeing the end result to his actual business. Right? Now the agency may not be able to diagnose that for him, but the agency is saying, look, we're doing our job and you need to to renew the contract because you know it's going really well. And and his question was, is that true? And then that's exactly what I'm hoping to help people look at. That question, is that true? Is the evidence that you're giving me ⁓ actually reliable in the big c bigger context. because often i i it it isn't or the people giving that evidence, it's true for them, but it actually doesn't add up in the big picture.
speaker-0: Somebody questioning the data, not a bad thing. That that that's a great intelligent thing at the end of the day. I'd I'd love that somebody came to you and said, Hey, yeah, you know, here is here's what I'm thinking. Can you can you paint the picture of what the actual picture should be or what it is and ⁓ you know, how close is this to reality versus not and w where are the opportunities along with that? I it's somebody brilliantly running a business.
speaker-1: Well, and to be clear just to be really clear about this, it's not that the data that the agency is presenting is wrong or inaccurate. It's that it doesn't answer the question that the act the business leader actually has. Right? Or it doesn't answer the complete question, which is, is this is this generating what I want it to generate? and you know, sometimes, for example, people will come or or people will say, Look, I've got, I don't know, X product on my site and It doesn't seem to be doing much, you know, or they'll say, we we serve a number of different industries. For example, like we've got various services for different industries, they're all laid out. Clearly, our most important industry is, according to, you know, according to the engagement levels on the site, is this one. And the question is, is that true? And it may be true. Yeah, mo most visitors go to industry X, but is it actually because that is the industry where this business has the most traction, or is it because something like that's where they've concentrated all their marketing activity recently? Or is it like, for example, you've got the product that nobody's seeing? I mean, I I had a a business owner come to me and they got this flash sale that was really important to them. And nobody was was picking it up. And he couldn't understand why. You know, and and honestly I didn't even need to look at the analytics for that because it was obvious. ⁓ he had one link to this flash sale that was right at the top of the homepage, which meant that as soon as you started scrolling down the page, it disappeared. And this is so typical. Search boxes, this is like you know, search boxes disappear almost immediately on most sites. And that search box is really an important thing. and he kept saying to me, but You know, it's obvious, it's obvious where it is, it's right there, I can see it, you know, because that's another thing that happens. We get so close to our own stuff that it's really difficult to put ourselves in the shoes of anybody else, especially somebody who's not immediately familiar with our site. And so those questions where you say, either this pro this content, this product, this sector, this whatever is either A mo much more interesting or B much less interesting than the average. Again. The numbers that you're seeing are true in terms of of attention, but but are they actually telling you the the truth about the big picture, the market, the demand, et cetera?
speaker-0: I love that as well because at the end of the day, ⁓ data is just data, but what does it actually s you know, what story does it actually tell? ⁓ you know, the search bar is a great example, right? It right. It it is obvious until it's not. until I have like, you know, scrolled or I've checked something out. I forgot, it's not in my face anymore. ⁓ y you know, a situation that you are a little too close to. I've I love other people telling me their perspectives and try to find the common pieces or the themes throughout it because it doesn't matter what mine is. Like if I want the the end goal, I want what majority is is feeling like. So the fact that at least the individuals asking and then, you know, making a case and getting educated, like fantastic at the end of the day. You're right. But we can go a little further and here's the data points to to show. You know, obviously we have less data points because it's not as obvious it seems. ⁓ which is a big dose of reality and I bet you next time that thing's gonna get slapped everywhere. It's ⁓ to make sure we drive some traffic.
speaker-1: Right. And the search bar, you know, is such a great source of market research. And, you know, I have a number of stories where we've looked at the searches that people have put into a site and essentially either solved low sales problems or created new products or services because of that. ⁓ it it just doesn't make sense to to not have that really, you know, maybe floating or but r in a really good, obvious place. ⁓ for the majority of a visit because it it's really good information.
speaker-0: Yeah, I mean that's that's very valid. Like i if you're getting, you know, whatever, X number of hits where it's enough to pay attention to and you don't have that product, or you're like, ⁓ wow, we've done a really bad job of like making it seamless to get there, then you can make quick, easy decisions and and fix some of those. And like you said, probably see some some good data points grow in some other areas at that point. So
speaker-1: Right. Right.
speaker-0: You said that good data can still lead teams in the wrong direction. ⁓ what does that mean in in your words?
speaker-1: Well, so again, it it it's what I'm saying is it's not often not that the data is wrong. I mean, ⁓ yes, obviously you want to get somebody to audit when you do setup, you know, is this is it tracking properly, is it tracking all the things we want it to track, you know. Yes, ⁓ you need you need to ensure data accuracy. But beyond that, once you've done that, then then you know, what we're really talking about is what metrics do you really need to investigate and what bigger picture context do you need to have in mind? to to rely on the decisions that you make. You know, I used to teach for a ⁓ well actually I've taught at three different business schools, but I I ⁓ really enjoyed that. And one of the things that I used to do was to give the students some scenarios ⁓ and say to them, okay, what would you do now? And one of my favorite scenarios was, okay, you've just received your e-commerce sales report for the last month. ⁓ Your revenue's down by 30%. Your boss wants to see you in an hour, right? What are you gonna do? Right? And my favorite answer was, well, I think I'm gonna get a cup of coffee and read the newspaper. Because, you know, this month is January, last month was December, we rely an awful lot on seasonal gift sales, and our revenue is always down by thirty percent in January, so there's nothing new here. But of course a lot of the times the default is to say, ⁓ no, panic, right? So and that's where again, things like AI may not it's good maybe it would, but you know, that's where I'm finding that AI falls down because is it able to put things like it it can easily flag, look, your sales are down by thirty percent. But the question is, can it then take things further? And it could be something else. It could be a new competitor has entered the market, everybody's really excited about them. This is what's going on. We need to do something about it, but meanwhile, this is the immediate explanation for the thirty percent drop, right?
speaker-0: Yeah.
speaker-1: Something that you can't tell from the immediate metrics in front of you, which are completely correct, but that you need to go out into the wider picture to think about.
speaker-0: Yeah, I I mean it's a it's a great point. If you just accepted the metrics, then you said all kinds of bad panic, whatever, but if you explore that a little bit further, you know, the metrics is the metrics is the start of going, Okay, mate what what caused this, right? Like that should be the next step. ⁓ I hope. In a lot of business cases to to peel back the onion layers and go, Okay, like you said, is it this factor or that factor or you know, whatever it is to be able to tell the story of how we got here. And if it is something that we can do, then fantastic, let's work on those things. So we can stop that trend. But if it's something that happened to us ⁓ or was seasonal or whatever, yeah, right now I don't think AI is gonna know that until you train that model on your business and give that, you know, that new information. ⁓ so I'd be hard pressed to trust the immediate result of whatever it's giving you. trust that you need to go look into it and figure out what it's saying, but yeah.
speaker-1: And you know, sometimes ⁓ the the the c conclusions that people jump to can be a bit dramatic. I mean, I had a client last year where they now this was ⁓ they're selling an app. It's a a s a a a logistics app, and ⁓ they were running a lot of paid ads and they were concerned that their ad guy, so this is an in house employee, wasn't doing something right because they were seeing a lot of click throughs and again no traction. They were worried that the conversion tracking so i within analytics, you know, you can link up your ads ⁓ with the analytics and and see which ads convert best and do all that kind of stuff so that you can then go back and reinform your ads. But you know they they They were worried that their conversion tracking wasn't working right, ⁓ which it was. They were worried that the ad guy wasn't properly doing his job in terms of, you know, the the running the campaigns and so on. And obviously, therefore, he was potentially in trouble. and again, the metrics were correct, the number of click-throughs compared to the number of what the the ads were for was to sign up for the free trial of this of this app. Right. But what actually happened, was happening, was looking at it more deeply, no traffic source, no way that they were getting traffic to their sign up for the free trial page was converting. None of it. So their newsletter, their search engine, their social whatever else they were doing, there was no conversion going on on that page. And so it's a different question. The question actually became, or what it became obvious immediately was To me looking at it, and again I d I don't know that an AI assistant would yet really pick this up well, was that the sales department at this company was insisting on them including on the form all sorts of questions. this is another issue. I mean the politics, you know, half the time the problems that you encounter are nothing to do with the technology or the data or the way the website works. It's the politics of who's saying we have to do this this way. ⁓ and they were asking all sorts of really in-depth questions about this, you know, prospect. ⁓ and you could clearly see, and especially if you've got a screen tracking thing going on as well, you know, people start filling in the form and then they go, Wait a minute, you don't need to know that right now. I'm not telling you that and and they're gone. Right? Now That became a whole different issue because the sales department initially refused to back down on but we need this information. And you can say to them, yeah, you can get this information when somebody signed up for the free trial, they're in the middle of it, you can say, hey, how's it going? Right. but meanwhile, this poor ad guy was getting blamed for non performance, and it was not his job it's not him at all. So, you know, sometimes making the wrong conclusions from correct data ⁓ has some really serious ramifications.
speaker-0: Yeah, that that is a great story and answer to that question because that was wild that would have been wildly off in the end when when in reality there was a couple of different things, you know, going on. And untangling that and getting that more streamlined probably produces better and, you know, in a way it kind of goes back to logic, because I agree with you. If you're asking me for my life story at the front of this you know, ⁓ of this ⁓ one, I don't have time to type it all in and and two, I just wanted to see how your product worked first. ⁓ before I you know got to that point. And by all means, I'll give it to you if it's an amazing product, right?
speaker-1: Yeah. I mean if you actually sign up to use it, of course you will, right.
speaker-0: Right. Yeah. I mean I I I want it. I'm gonna give you whatever you need. ⁓ but yeah, I know I I I do find the a lot of the human elements still, ⁓ you know, especially with AI and you know, other things, you know, along the way, they kind of are the reasons to ⁓ a lot of the the outcomes that we have. So ⁓ I I find data, you know, fascinating. I think AI definitely helps uncover data. like you said, it c it it can spot things, but if it doesn't know the variables that that you're uncovering, you know, from a human eye at that point, the best use at the moment for from AI it seems is to show you the the data points, show you the flags, and then you uncover the actual situations from them at that point that it's not gonna know.
speaker-1: Right. And since I hate designing reports and dashboards and things, you know, that's great. Love it. Do that all day for me. Fantastic, you know.
speaker-0: Absolutely. Yeah. Some of the some of the biggest time syncs, right? And then ⁓ AI comes along and just makes it easy. In terms of that, like yeah, use these fields over here and this access. Done. Awesome. Perfect.
speaker-1: Right. And and to be honest, I use it. I actually have a I design my own ⁓ Claude prompt, which ⁓ actually goes through websites and it's designed to point out some areas to me. I I th my prompt, you know, is designed on based on my my kind of experience of where things can go wrong. ⁓ and it just flags stuff for me and then it's much quicker than me going through it. So I mean, you know, that's great. But then I get that report and then I start using it and that's where I think I add, hopefully I still add value. because then I can go to a level of thinking that AI can't do yet.
speaker-0: Think I think that is the perfect combination of like what I've been trying to say along with this show and and my socials that combined with the reason why you would hire that smart employee, in this case, you know, you as a consultant, you know, coming in to help businesses, that's a perfect combination, right? You've got it pulling everything and going, Hey, I did use AI. Here's what I pulled down. Here's my assessment on top of that from my train. ex professional experience that this is what the story is combined with the AI information. It's a perfect blend and that's gonna produce, in my opinion, the best the best results. You got the best of both worlds there.
speaker-1: Right. Yeah. Yeah.
speaker-0: What kinds of opportunities tend to hide in plain sight typically?
speaker-1: ⁓ you know, well, earlier we talked about, for example, the site search engine. ⁓ and I said that it's a great source of market research, which which it is, and I I've several times worked with clients that have, for example, a a huge amount of content on their site or they've got lots of different products, and you see people doing searches for things which are within their area of expertise, but that they don't currently offer. And so right there, you know, you've got literally, hey, there's a there's a clear demand from your visitors for s for this. You could do this. And ⁓ so I actually ⁓ wrote a book last year, which I should probably mention at some point, called Website Wealth, which is full of these kinds of stories to which is designed to hopefully inspire business owners. It's it's all in English, so you don't have to ⁓ worry about the the technology, it's not a training manual on how to use any particular tool. ⁓ but lots of stories about things, business strategy decisions and actions that we've taken that have really made a difference to the business. And and one of those is is literally doing exactly this, developing new products or services based on what we've seen in visitor patterns. I mean, one one story that I have in my book is is ⁓ I was working with a manufacturer of industrial waxes. And so this is a this is a high level B2B business, right? but they, for example, were doing they were a very well known, they were appearing in a lot of search results and so on, and you could tell that a lot of people were coming to their website because they they were consumers, but they wanted to make their own candles. And so they were looking for beeswax and duck wax and all this kind of stuff. And obviously they were they were not going to buy from this business candle. ⁓ at an industrial level. And so the business had been basically sending them away. But, you know, when we saw the numbers, it was like, wait a minute, do we really want to just send these people away? And this business actually ended up creating a little side e-commerce site that ⁓ sold, you know, consumer level amounts of of the product. ⁓ it really didn't take very much maintenance. It didn't need to do a whole load of marketing because they were already getting this traffic. They could easily on their main B2B website have a button that said, you know, if you're a consumer, click here and off you go. And this little side business just ticked along and did all sorts and and really did quite well. You know, and that was basically a decision that was taken looking at the data, looking at what we could tell from the data, and saying, do we want to capitalize on this? And this is another thing that I that I also work with ⁓ businesses on is, you know, are there ours other opportunities that the data shows us that we're not yet taking advantage of?
speaker-0: mean that's that's a great example of I mean you had enough numbers to go, maybe we should. ⁓ and you know now they're capitalizing and getting, you know, more income at at the end of the day and not sending those, you know, individuals ⁓ like that's that's like the perfect example of of data of what you could do at this point. Like it's an opportunity if you want to take that. You know, here's the numbers, right? And from from your story it sounds like they did very well, which is amazing.
speaker-1: So again it it's looking at the data which which is correct, right? Here are all the people that are not are not staying or are not doing business. I mean another example is is people who come from outside your area. there was a company that made ice cream out in the northeast and their ice cream was very well known, very popular in their area, but they didn't ship they did ship it, but not very far because of you know, the ice cream's going to deteriorate and be useless, right? and again, you just looking at the data of how many people come from outside the area that they ship to, and again, saying them saying, Do we want to do anything with these people? and are you then you just you can either say yes, we we don't care, just let them go, or you could say, are there any other creative ideas, for example, we could have we could be selling gift cards on the site so that they can have a shipping a shipment to somebody, you know, who's in the area that they want to send. So it it's it's it's looking at your data and saying, does this tell us any stories that we could use? And at least then we can make the decision, do we want to use this or do we not? ⁓ but not just disregarding it. And again, that dashboard, if you just use the basic dashboard, it's going to say, you know, here are the number of visitors that are actually useful to us. Here are the number that are not useful to us in the immediate term. And you just you know, that's it. And the question is, do you want to think more about that?
speaker-0: missed opportunities. You you're not digging into, you know, what are they coming for? Like all all the things you just said, really. All of the good ideas that you could have uncovered and and had the decision or the choice to maybe do something about. You've given up the choice by not feeling feeling behind the door, looking behind the door so to speak and figuring out. ⁓ so you've actually, you know, talked a little bit or ⁓ a bit about quieter parts of websites, ⁓ you know, sometimes creating more business value than the areas that leadership tend to focus on. You know, why does that happen? Is that just more of ⁓ they're focused on those dashboards and the reds and the greens, or is it just that ⁓ they're not really speaking a lot so they don't tend to focus on those areas?
speaker-1: I think again it it's you know, this is thinking at a different level. So if the business owners are really not focused on this as as a as something that they're interested in specifically, which I as I as I said is perfectly understandable, ⁓ you know, they're reliant on what the agency or their team or AI tells them. and, you know, that's kind of like marking your own homework, right? If that isn't giving them the in-depth analysis or the in-depth thinking or questioning that they need. I mean, the the team doing the reporting is maybe not going to ask that question. Does the evidence support the decision that we're recommending? They may not be asking that. Or they may not be asking that from a high enough level. ⁓ And so unless you you need to get the senior leadership into the mindset of let's look at this in more depth. Let's not just accept what what people are telling us. But and and and not to say that people are wrong, that their teams are wrong or their agencies wrong, but just let's let's just look at this one more time and and see if there's a bigger picture that we can apply or see if there's another question that we need to ask.
speaker-0: Yeah, I I think that's the the interesting and the dangerous point, right? Like that becomes evident then if you have the individuals who are creative enough to take that information and go, Okay, this, this, and this could possibly be happening and or this is the opportunities I see that we could be, you know, trying or taking from that. And if you don't have that part to go with the data part, then it doesn't really matter anyways. But if you have that to be able to read or hear the interpretations of what's happening, then yeah, that's what really starts separating you from the good and the bad. What or the the better businesses we'll call it.
speaker-1: I think there's a lot of temptation to just assume things are humming along. 'cause, you know, if there's no obvious bad news, ⁓ you you you think that everything's working, there's nothing else you need to do, maybe you've got bigger problems, ⁓ y you know, but and you don't realise that maybe you could do something with your website that would as that would actually address some of those bigger problems. and and you know, it's an area that a lot a lot of business leaders are are less familiar with. ⁓ a a and so it's it it it all kind of compounds.
speaker-0: Yeah, no, I I think that's a great example because if it looks like we're hitting our targets, everything's working fine, I'm probably not putting a second effort or a second think into what else could we be doing? What are we missing? Like everything's working great, we're hitting the number. What do you mean? You know, like until you actually put some effort and thought and make sure I at least investigate to ⁓ like validate that that's actually true, then it's just a missed opportunity.
speaker-1: I think that's all that deaf by data actually. Maybe we should talk about that. You know, I I sometimes there's a temptation because you have got AI and you can make a bazillion different dashboards to measure absolutely everything that you can measure. And again, that kind of leads to analysis paralysis, right? I mean it's like, okay, but what are the real questions that we need to be asking right now? so we see all these metrics come in and a lot of them look really good and again, so we assume it everything's everything's right and maybe within that there's something hiding that i it could be really different. ⁓ i you know like you you mentioned earlier you've done a lot of content creation. one thing that I see a lot there is is people looking at initial engagement levels. So you know s number of videos starts. Like if you look at YouTube, I believe that when you see the numbers on the YouTube views, that's people who've started the video. And potentially I can't remember if it's played 30 seconds of it or something like that. ⁓ and then it c counts as a view. What that doesn't tell you is what happens after that. So how many people actually watch the whole thing? And the number I mean, the if you look at those kind of numbers, the the the number of people whose attention falls off after the first thirty seconds, one minute is huge. And if you're investing a tremendous amount in in high level I mean, I've seen this high level B to B video production. And like you've got a ten minute video and ninety percent of your audience only watch the first minute, that's a huge amount of investment that's just wasted. And especially if your key call to action, you know, the thing that you actually want to happen and isn't get doesn't get mentioned until the end. Right. so so looking up the data and saying, okay, we're we're making these decisions and these assumptions ⁓ and again, you know, are we looking at all the correct metrics to to really base base our thinking on?
speaker-0: Yeah, no, I mean that's ⁓ that's all very, very true, very, very valid. we we have to know what's important to our business in particular, right? What are the things that we want to attract? You're right. You can get lost in all the land of data and then y you're not doing any of those things that actually matter to your business because you're worried about everything when maybe you needed four of those things, whatever it was. that you really needed to track, make sure that that was happening. ⁓ so I can I can see it, especially being involved and being in it. And and that's why I've been a huge advocate that sometimes grabbing somebody who is not a part of the organization, has no attachment to it, be able to go just here's what I see right away, ⁓ helps pull you out of the weeds a little bit, so to speak. And then, you know, you can start working on those things.
speaker-1: That's that's great. That's music to my ears 'cause that's exactly what I'm what I'm offering.
speaker-0: That's I mean, th I I see it all the time, especially when we're talking about AI. it's kind of a similar thing. ⁓ you know, AI is in in the data world amazing for that right now. You can get more metrics than you've ever I probably ever asked for or new that you could get. ⁓ but like you said, it's so easy to get lost in it though. what is it that you actually need or want to track or or things you want to watch out for? make that custom, you know, for your business, watch that. And then, you know, maybe keep an eye on a few things over here on the side. I'd I'd be very curious, you know, from your perspective, especially in in in dealing with businesses now with AI being so heavily, the things that you ha you see as challenges when it comes to AI or the things that you recommend for them to pair with. And and maybe it's an example of like an idea I was pitching there. But maybe there's a better version or a better Better way that that you come up with
speaker-1: Well, I I mean, I know that y everybody's talking right now about AI hallucinations and you know, the I think it is a trap that you get something that looks very professional, very well produced, and could be completely wrong. And unless you know how to question it, you don't know if it's wrong. ⁓ I mean that's why I'm saying in my own practice, you know, I I do use AI and I'm not I'm certainly not gonna pretend that I don't. But I take what it tells me and then say, okay, is this can I verify this? You know, this is help this helps me start, this helps me jump in. but is it for real? a and the pieces that it can't do. I I ⁓ I I mean, give you another example. I I was watching screen recordings, so I which I really love because they show you exactly what people do. And there was this one She was a major non profit in ⁓ in New York City and they were having this benefit gala with big names, ⁓ guests and everything. and this was a form that people could fill in to register to s you know, for tick to buy tickets for the gala. And and the screen recording tool was reporting a lot of ⁓ drops. And using a screen then the screen recording tool had an AI assistant and the AI assistant was saying, ⁓ look, hi ⁓ here's the form, and a lot of people are dropping. Right? But it that was where it stopped. It's like, okay, yes, I can see that, thank you. and it was quite subtle. When you watch the screen recording as a human, it was actually quite obvious what was happening, and it was really bizarre. But for some reason They had split their address ⁓ input field over two lines. I don't know why, two separate fields. And both of those fields were required, which made no sense whatsoever. I don't know how this happened. But you could see the person again and again, the person would fill in their address on the first field and continue on, fill in the rest of the form, hit continue, and they got an error. And it and it was just wasn't clear, or it or it didn't continue. ⁓ and Until you saw somebody who came along and and went, ⁓ I see, and they went and split their address and put the second half of the address on this on the second line and boom, that worked. And it was really odd. but the AI couldn't diagnose that. Whereas as soon as you see it with your eyes, you can go, ⁓ look, that's what it is. That's what it is.
speaker-0: Yeah. And it's I think it's a great example of like AI going, Hey, we a problem, which at that point, you know, already already figured out there is a problem, right? But ⁓ in this case it needed the human or or you or who or whoever, you know, that actually's pulled us apart and then it was pretty obvious that maybe we missed QA in in in going through this. and, you know, hey, we're not getting any registrations for something we expect a lot. ⁓ what what what is happening and all of that is a perfect blend of of how you can use all of those aspects to fix the ultimate problem at at the end of the day and and should not blindly trust, you know, AI or or anything else without reviewing the actual data. In this case, luckily you had a screen recording because I mean, yeah, like you said, ⁓ what what lovely behavior at that point to be like 'cause I would have submitted and have been like, what what what the heck's happening? I didn't put my address in. Like why is it time to
speaker-1: You could see people doing that and giving up. Which is also not good for the reputation and the brand of ⁓ you know
speaker-0: Yeah. ⁓ that that is one thing that I I would I would co sign and agree with you on that you gotta be careful with, especially with AI is ⁓ you you can use it. It's a great example of where to use it and maybe don't rely one hundred percent on it because it's like here's a problem, don't know how to fix a problem. Come in with a human set of eyes, you fix the actual issue. You know, great use of both sides, blend, perfect, but that AI tool didn't fix it and now your reputation you know, over and over again of social media all of a sudden it's ablaze with all these bigger names putting on there and then others underneath them kind like, yeah, it's a nightmare, right? We don't want that.
speaker-1: Right, right. And you know, it's funny 'cause I realize that a lot a a lot of this interview, you know, I've been talking about working with with more senior leadership ⁓ on the big question, which is is is the evidence that you're looking at supporting your decision. ⁓ and we just kind of went into the weeds there. But I guess it's all part and parcel of the whole thing. ⁓ I mean I want to start at the big l at the big decis at the big questions and then sometimes you have to sort of dive in a bit and and see what's 'Cause sometimes it i it is, it's something really small that is easily fixed, but that's throwing everything right.
speaker-0: Absolutely. And and I'm glad that you brought that up because it takes me into almost my last question here, which is what questions should leaders be asking before they make the major decisions from analytics?
speaker-1: Well, you know, that's really about understanding your own business and your own goals and and prioritizing what is it we want to do right now. So often I I come in when ⁓ there is a big decision in progress. So it's it's are we going to make a a big investment in in a new marketing campaign? Are we going to make a big investment in redesigning our our website? Right. is Is ⁓ a decision to develop a new product or service supported? Is it true that, you know, of all these different sectors on our website or different audiences that we serve, this one is the most profitable to us? Those kinds of things. ⁓ and and sometimes it it it is more of an ongoing, you know, can you tell us what's happening and what's going on and where we need to focus? But the underlying but the question that that I can't answer is, well, what do you want to be happening? You know, are you clear about the most important things that you're looking at to do? And that's where you start. Knowing your own questions, because that's really not a website question, that's a business strategy question. And we can we can figure out the right data to answer the question, but you you need as business leader, you need to know the questions.
speaker-0: Yeah, ⁓ absolutely. ⁓ I mean more of a if you know the direction, I can help show you the data points to support or don't support that direction and you know, help uncover I guess the rest of the pieces to the puzzle is kind of ⁓ how my mind is is seeing that or hearing that conversation right now. and I'm I'm sure those get into fun conversations as well when you get into those those kind of level of ⁓ you know, discussion. ⁓ but Coming up towards the end here, ⁓ I wanna ask ⁓ one and ⁓ one question in particular for for you with with all of your experience, all of the clients you've worked with and where you're still headed, you know, what does success look like for you today or going forward?
speaker-1: You mean success personally for me? I mean when when do I feel good is is obviously when I've worked with a client, we've figured stuff out. The client has has hopefully maybe learned something that they that they didn't know or didn't realise that's really advantageous to them. I mean it's you know and and it's been fun. I mean obviously it's more fun working with fun clients.
speaker-0: Personally.
speaker-1: You know, when we've done something creative, that's that's always something that really inspires me as well. You know, we've come up with this idea to do something different or or you know, we've solved a problem that we couldn't work out for ages and then all of a sudden you go, ⁓ that's it you know, that that's I mean, you're a data guy, you probably have the same list, right?
speaker-0: I I love ⁓ I think that's the one thing about AI that if I if I have to answer a question ⁓ and I can only pick one thing, it's the fact that we can be more creative than we've ever maybe thought we could be. obviously there's some, you know, dangers with that, but I'm experimenting. I call it's I understand I'm experimenting too, I think is the big key. I'm experimenting with a lot of different things and trying to see where that creativity can go. I I am building something right now ⁓ that does have ⁓ a lot of these points, a lot of dashboards, a lot of reporting in it. So I find this very valuable to me personally, but I'm sure this is gonna be very valuable to ⁓ every business. Every business has data and dashboards and they need this kind of info to drive decisions and and be able to hopefully stay afloat, grow, be better, be a leader in their industry, all all of these points. So ⁓ so yes, I'm very much ⁓ you know, a a part of looking at it. I wouldn't say I'm a I'm a true data analytic. I have appreciation for what data can absolutely tell you and and what you should lean towards versus my opinion. The data is the proof, not my opinion at the end of the day.
speaker-1: Right. Absolutely. Yep.
speaker-0: So with that, ⁓ you know, that's taking us to the conclusion here, ⁓ where can people connect with you, ⁓ find out more about you, buy your book, ⁓ and anything else you have going on?
speaker-1: So as we're speaking, my website is under redevelopment, but hopefully by the time this comes out, maybe that will be done. So that's websites that win, websites that win dot com. ⁓ you can buy the book at all good booksellers online and and offline as well. ⁓ I mostly like connecting with people on LinkedIn and ⁓ I do offer a decision confidence call. So that's a a free 20 minute call just to if, you know, you're thinking through something or you just want to kick something around. ⁓ obviously it's not a a full engagement advice thing, but it it's designed to just say, hey, is there a question here that, you know, you've basically got a handle on and you're looking at the right data off the top of my head? Or is it something that we probably sh you know might be worth dig digging into more deeply? ⁓ so if you'd like to have an initial kick around conversation with me. please ⁓ go ahead and book that a decision confidence call.
speaker-0: Wonderful. And we'll be sure to include everything ⁓ you know, in the links and make sure that the people know where to go. And I ⁓ love ⁓ checking out my guest books. So I will be taking a look at that book as well. ⁓ and ⁓ see what else I can learn further from Justice Convo. ⁓ so Philip, ⁓ it's been wonderful ⁓ having you here. I appreciate you taking in the time to educate myself and the audience. And ⁓ I mean, it's just been a great conversation. Hop hopefully this has been rewarding for you as well.
speaker-1: Thank you, enjoyed it a lot. Thank you.
speaker-0: Appreciate it. And and with that the meeting has been adjourned and ⁓ we will see you all next time, everyone. Take care.
speaker-1: Hey, thank you, Ryan. I'm looking forward to this.
speaker-0: Absolutely. So one of the first things we like to go into is some icebreakers. ⁓ and you've reviewed more than five thousand websites over your career, which is just incredible, honestly. And you know, a lot of pattern recognition. You know, what originally pulled you into analytics and digital strategy?
speaker-1: Gosh, that that was actually a long time ago. But, you know, analytics just grabbed me ⁓ when I started to get into this field, because it was so new. You know, we used that to sort of do audience research and market research and you know, so like if you had a magazine or a newsletter, you could tell how many of those got sold. But that didn't tell you how many people actually read. The thing, how many people saw any particular page, how many how people responded to it. Right. Now we've got all this amazing data about what's going on that we can learn so much from to make decisions about not only improving our website, but also growing our business. and the thing that I really love about it is or that really kind of inspired me to do more is is But it yeah, you can geek out on it. I mean, I I I really liked it when you said you're the extroverted IT person, because you're absolutely I mean, yeah, that's like that's like a contradiction in terms, right? most most IPT people are sort of really geeky people that hide behind the screen and and talk techie all the time, right? But the thing that I love about the analytic strategy piece is that yes, you have to be good at sort of an ana analytics mind. But it's also really creative because you've got the other side of it where you go, okay, here's what we think's going on. What the heck are we going to do about it? And that's where analysis doesn't help. You've got to have some imagination and some creativity and some understanding of the market and all of that. So it's a really, I think, lovely combination of of the two. Often a sort of polar opposites, right?
speaker-0: definitely get that comment a lot. ⁓ I I might intentionally stick it out there, but it's pretty evident when you start talking to me. ⁓ but yeah from from a data standpoint, I talked to a marketer recently who who kind of made the same you know conclusion and and direction that you're going, which is, yeah, here's all the data, but what are you gonna do with it? You know, what story are you going to tell with it? And you know, how does that link together? And as somebody who has just launched all kinds of different pieces of content and newsletters and yeah, I'm looking at the things you really just said, which is, yeah, how many people actually opened the thing? That's great they subscribe, but did anybody read it? ⁓ you know, if they're not reading it, that's a problem. But then if they did read it, what things you know pulled them in, you know, what things should I keep going on? It's like the the real story, right? I want to know those pieces, not how many people signed up for it.
speaker-1: Right. Right. Well that's a good start, but right.
speaker-0: Yeah, yeah, I exactly. So, you know, in analyzing all of these kinds of things, what kind of patterns kept showing up for you over and over again?
speaker-1: I think well, I think honestly one of the patterns that that i is my my decision services, my decision confidence services I should say, ⁓ is a fairly new service that I'm that I'm developing. ⁓ and I think it's based on the fact that maybe for a lot of people analytics is kind of boring and the data is kind of boring and they'd really rather go out and just, you know, do the business and do the stuff they really enjoy, which is totally understandable. But what that leads to, I think, is people taking maybe a lot of the surface numbers. You know, the the the p page one of the dashboard that says, you know, this is up and this is down and and just going with that. Right. I I mean honestly, I think it's not to insult anybody, but it's easy to do and it look you know, you get especially now with AI, you get these beautiful looking dashboards. And you go, ⁓ okay, so this must be what's going on. Let's let's either assume everything's great and carry on. Or, ⁓ well, maybe we need to do something, but then, you know, not really thinking through what is actually happening, w what's the big picture, what do we really need to to question here?
speaker-0: Yeah, I I mean, I think as a human, right, we just look at the visual data like like you said and go, ⁓ okay, and don't even like maybe even question what where is the data coming from or you know, is this even right? And we're just kind of running with it. ⁓ you know, at at what point do you do you think that you noticed that maybe businesses were too trusting of that or they're making like like confident decisions based off looking at those types of data points?
speaker-1: I mean, all the time. I mean, you know, as I as I get involved with working with a business, I think that there's a lot of times when when that becomes apparent. And I I I was just thinking the other thing that is really interesting, and maybe it's a human psychology thing, is that people tend to concentrate on the bad news more than the good news. Now, okay, if your sales are down, obviously you need to do something about that. ⁓ but Luckily not so much these days. ⁓ back in the old days when we had universal Google Analytics, for example, that showed the bounce rate, ⁓ people would absolutely obsess about the bounce rate. That's the number of people, right, who leave your site pretty much qu immediately ⁓ and don't do anything. And so say let's say they had a 50% bounce rate, right? They'd go nuts. Like, what am I gonna do? What am I gonna do? Why is this this is terrible? Instead of focusing on the fifty percent people who stay and actually look around and actually do stuff and how can we get them to do more? You know, what what can we do to improve that bit because they actually liked us and they want to engage with us a bit. ⁓ and I I mean I I think this is probably just human I know I I used to do a lot of speaking, right? And you get your speaking evaluation and you know, you could have hopefully an awful lot of people saying you were you were great, this is really helpful, I really learned a lot. And then you have the one person that says, this was completely useless. And and you go, you obsess about that, right? so so really thinking about which which information is it that you really need and that it will be the most useful and you know expedient for you to do something with, as opposed to what is it that just triggers me and I obsess about?
speaker-0: Yeah, the psychology piece is ⁓ so fascinating to me, like to the point where I almost thought about going back and getting, you know, a psychology degree and I stopped myself because I have too much education and I I I do kinda understand humans I think better than most and and what triggers them and and what draws them in. and yeah, you're you're absolutely right. Like ⁓ w even in IT, right? We're we're we're constantly worried about fixing the next thing over and over and over again. We never really stopped to celebrate or do any of those things. So we're on to the next like fixing bad situation. that if you get a data point that's like, look how good this is and you know, it looks like there's might be some room to even go further with that. It's not as important as like like what you said. It's like, yeah, but that one's that one's red. Like that that means bad, right? Like our like our eyes and attention and mind dripped over there. So why what do you
speaker-1: I mean i if it's red because things are about to blow up, yes, you need to do. But you know, if it's an outlier yeah, right.
speaker-0: Someway. Right. Yeah. Yeah, I mean i again like training yourself as an outlier and and I was gonna try to save this for as long as we could go, but like as a data, you know, data point ⁓ you know, situation, at some point you become a storyteller, right? You become here's what's happening with the data and these are the actual things to actually be concerned with, think about, grow from whatever the story is to tell from the data, but you have to translate it, right? So d otherwise it's just it's just numbers at the end of the day. And if those individuals don't know how to translate the numbers then it's useless to them. So w w why do you think that, you know, so many organizations kinda make the mistake with the activity over maybe the actual demand or the actual s what the story trying to tell you?
speaker-1: think you know sometimes business leaders maybe I I mean I see a lot of businesses that work with agencies and this is not to knock no agencies, there are a lot of very good agencies, but at the same time there are a lot of agencies that are reporting on the metrics that mean that are important to them, such as, you know, click throughs from ads, right? So So if we have a lot of click throughs from our ads, we're doing our job and you should continue to pay us. And they may not have all of the data that goes through to the end result. I mean, I I had a a a CEO come to me recently, and he had a big contract with an ad agency and exactly the situation that the ad agency was reporting all of this success in terms of of click throughs and and visibility and so forth. and he really felt like he wasn't seeing the end result to his actual business. Right? Now the agency may not be able to diagnose that for him, but the agency is saying, look, we're doing our job and you need to to renew the contract because you know it's going really well. And and his question was, is that true? And then that's exactly what I'm hoping to help people look at. That question, is that true? Is the evidence that you're giving me ⁓ actually reliable in the big c bigger context. because often i i it it isn't or the people giving that evidence, it's true for them, but it actually doesn't add up in the big picture.
speaker-0: Somebody questioning the data, not a bad thing. That that that's a great intelligent thing at the end of the day. I'd I'd love that somebody came to you and said, Hey, yeah, you know, here is here's what I'm thinking. Can you can you paint the picture of what the actual picture should be or what it is and ⁓ you know, how close is this to reality versus not and w where are the opportunities along with that? I it's somebody brilliantly running a business.
speaker-1: Well, and to be clear just to be really clear about this, it's not that the data that the agency is presenting is wrong or inaccurate. It's that it doesn't answer the question that the act the business leader actually has. Right? Or it doesn't answer the complete question, which is, is this is this generating what I want it to generate? and you know, sometimes, for example, people will come or or people will say, Look, I've got, I don't know, X product on my site and It doesn't seem to be doing much, you know, or they'll say, we we serve a number of different industries. For example, like we've got various services for different industries, they're all laid out. Clearly, our most important industry is, according to, you know, according to the engagement levels on the site, is this one. And the question is, is that true? And it may be true. Yeah, mo most visitors go to industry X, but is it actually because that is the industry where this business has the most traction, or is it because something like that's where they've concentrated all their marketing activity recently? Or is it like, for example, you've got the product that nobody's seeing? I mean, I I had a a business owner come to me and they got this flash sale that was really important to them. And nobody was was picking it up. And he couldn't understand why. You know, and and honestly I didn't even need to look at the analytics for that because it was obvious. ⁓ he had one link to this flash sale that was right at the top of the homepage, which meant that as soon as you started scrolling down the page, it disappeared. And this is so typical. Search boxes, this is like you know, search boxes disappear almost immediately on most sites. And that search box is really an important thing. and he kept saying to me, but You know, it's obvious, it's obvious where it is, it's right there, I can see it, you know, because that's another thing that happens. We get so close to our own stuff that it's really difficult to put ourselves in the shoes of anybody else, especially somebody who's not immediately familiar with our site. And so those questions where you say, either this pro this content, this product, this sector, this whatever is either A mo much more interesting or B much less interesting than the average. Again. The numbers that you're seeing are true in terms of of attention, but but are they actually telling you the the truth about the big picture, the market, the demand, et cetera?
speaker-0: I love that as well because at the end of the day, ⁓ data is just data, but what does it actually s you know, what story does it actually tell? ⁓ you know, the search bar is a great example, right? It right. It it is obvious until it's not. until I have like, you know, scrolled or I've checked something out. I forgot, it's not in my face anymore. ⁓ y you know, a situation that you are a little too close to. I've I love other people telling me their perspectives and try to find the common pieces or the themes throughout it because it doesn't matter what mine is. Like if I want the the end goal, I want what majority is is feeling like. So the fact that at least the individuals asking and then, you know, making a case and getting educated, like fantastic at the end of the day. You're right. But we can go a little further and here's the data points to to show. You know, obviously we have less data points because it's not as obvious it seems. ⁓ which is a big dose of reality and I bet you next time that thing's gonna get slapped everywhere. It's ⁓ to make sure we drive some traffic.
speaker-1: Right. And the search bar, you know, is such a great source of market research. And, you know, I have a number of stories where we've looked at the searches that people have put into a site and essentially either solved low sales problems or created new products or services because of that. ⁓ it it just doesn't make sense to to not have that really, you know, maybe floating or but r in a really good, obvious place. ⁓ for the majority of a visit because it it's really good information.
speaker-0: Yeah, I mean that's that's very valid. Like i if you're getting, you know, whatever, X number of hits where it's enough to pay attention to and you don't have that product, or you're like, ⁓ wow, we've done a really bad job of like making it seamless to get there, then you can make quick, easy decisions and and fix some of those. And like you said, probably see some some good data points grow in some other areas at that point. So
speaker-1: Right. Right.
speaker-0: You said that good data can still lead teams in the wrong direction. ⁓ what does that mean in in your words?
speaker-1: Well, so again, it it it's what I'm saying is it's not often not that the data is wrong. I mean, ⁓ yes, obviously you want to get somebody to audit when you do setup, you know, is this is it tracking properly, is it tracking all the things we want it to track, you know. Yes, ⁓ you need you need to ensure data accuracy. But beyond that, once you've done that, then then you know, what we're really talking about is what metrics do you really need to investigate and what bigger picture context do you need to have in mind? to to rely on the decisions that you make. You know, I used to teach for a ⁓ well actually I've taught at three different business schools, but I I ⁓ really enjoyed that. And one of the things that I used to do was to give the students some scenarios ⁓ and say to them, okay, what would you do now? And one of my favorite scenarios was, okay, you've just received your e-commerce sales report for the last month. ⁓ Your revenue's down by 30%. Your boss wants to see you in an hour, right? What are you gonna do? Right? And my favorite answer was, well, I think I'm gonna get a cup of coffee and read the newspaper. Because, you know, this month is January, last month was December, we rely an awful lot on seasonal gift sales, and our revenue is always down by thirty percent in January, so there's nothing new here. But of course a lot of the times the default is to say, ⁓ no, panic, right? So and that's where again, things like AI may not it's good maybe it would, but you know, that's where I'm finding that AI falls down because is it able to put things like it it can easily flag, look, your sales are down by thirty percent. But the question is, can it then take things further? And it could be something else. It could be a new competitor has entered the market, everybody's really excited about them. This is what's going on. We need to do something about it, but meanwhile, this is the immediate explanation for the thirty percent drop, right?
speaker-0: Yeah.
speaker-1: Something that you can't tell from the immediate metrics in front of you, which are completely correct, but that you need to go out into the wider picture to think about.
speaker-0: Yeah, I I mean it's a it's a great point. If you just accepted the metrics, then you said all kinds of bad panic, whatever, but if you explore that a little bit further, you know, the metrics is the metrics is the start of going, Okay, mate what what caused this, right? Like that should be the next step. ⁓ I hope. In a lot of business cases to to peel back the onion layers and go, Okay, like you said, is it this factor or that factor or you know, whatever it is to be able to tell the story of how we got here. And if it is something that we can do, then fantastic, let's work on those things. So we can stop that trend. But if it's something that happened to us ⁓ or was seasonal or whatever, yeah, right now I don't think AI is gonna know that until you train that model on your business and give that, you know, that new information. ⁓ so I'd be hard pressed to trust the immediate result of whatever it's giving you. trust that you need to go look into it and figure out what it's saying, but yeah.
speaker-1: And you know, sometimes ⁓ the the the c conclusions that people jump to can be a bit dramatic. I mean, I had a client last year where they now this was ⁓ they're selling an app. It's a a s a a a logistics app, and ⁓ they were running a lot of paid ads and they were concerned that their ad guy, so this is an in house employee, wasn't doing something right because they were seeing a lot of click throughs and again no traction. They were worried that the conversion tracking so i within analytics, you know, you can link up your ads ⁓ with the analytics and and see which ads convert best and do all that kind of stuff so that you can then go back and reinform your ads. But you know they they They were worried that their conversion tracking wasn't working right, ⁓ which it was. They were worried that the ad guy wasn't properly doing his job in terms of, you know, the the running the campaigns and so on. And obviously, therefore, he was potentially in trouble. and again, the metrics were correct, the number of click-throughs compared to the number of what the the ads were for was to sign up for the free trial of this of this app. Right. But what actually happened, was happening, was looking at it more deeply, no traffic source, no way that they were getting traffic to their sign up for the free trial page was converting. None of it. So their newsletter, their search engine, their social whatever else they were doing, there was no conversion going on on that page. And so it's a different question. The question actually became, or what it became obvious immediately was To me looking at it, and again I d I don't know that an AI assistant would yet really pick this up well, was that the sales department at this company was insisting on them including on the form all sorts of questions. this is another issue. I mean the politics, you know, half the time the problems that you encounter are nothing to do with the technology or the data or the way the website works. It's the politics of who's saying we have to do this this way. ⁓ and they were asking all sorts of really in-depth questions about this, you know, prospect. ⁓ and you could clearly see, and especially if you've got a screen tracking thing going on as well, you know, people start filling in the form and then they go, Wait a minute, you don't need to know that right now. I'm not telling you that and and they're gone. Right? Now That became a whole different issue because the sales department initially refused to back down on but we need this information. And you can say to them, yeah, you can get this information when somebody signed up for the free trial, they're in the middle of it, you can say, hey, how's it going? Right. but meanwhile, this poor ad guy was getting blamed for non performance, and it was not his job it's not him at all. So, you know, sometimes making the wrong conclusions from correct data ⁓ has some really serious ramifications.
speaker-0: Yeah, that that is a great story and answer to that question because that was wild that would have been wildly off in the end when when in reality there was a couple of different things, you know, going on. And untangling that and getting that more streamlined probably produces better and, you know, in a way it kind of goes back to logic, because I agree with you. If you're asking me for my life story at the front of this you know, ⁓ of this ⁓ one, I don't have time to type it all in and and two, I just wanted to see how your product worked first. ⁓ before I you know got to that point. And by all means, I'll give it to you if it's an amazing product, right?
speaker-1: Yeah. I mean if you actually sign up to use it, of course you will, right.
speaker-0: Right. Yeah. I mean I I I want it. I'm gonna give you whatever you need. ⁓ but yeah, I know I I I do find the a lot of the human elements still, ⁓ you know, especially with AI and you know, other things, you know, along the way, they kind of are the reasons to ⁓ a lot of the the outcomes that we have. So ⁓ I I find data, you know, fascinating. I think AI definitely helps uncover data. like you said, it c it it can spot things, but if it doesn't know the variables that that you're uncovering, you know, from a human eye at that point, the best use at the moment for from AI it seems is to show you the the data points, show you the flags, and then you uncover the actual situations from them at that point that it's not gonna know.
speaker-1: Right. And since I hate designing reports and dashboards and things, you know, that's great. Love it. Do that all day for me. Fantastic, you know.
speaker-0: Absolutely. Yeah. Some of the some of the biggest time syncs, right? And then ⁓ AI comes along and just makes it easy. In terms of that, like yeah, use these fields over here and this access. Done. Awesome. Perfect.
speaker-1: Right. And and to be honest, I use it. I actually have a I design my own ⁓ Claude prompt, which ⁓ actually goes through websites and it's designed to point out some areas to me. I I th my prompt, you know, is designed on based on my my kind of experience of where things can go wrong. ⁓ and it just flags stuff for me and then it's much quicker than me going through it. So I mean, you know, that's great. But then I get that report and then I start using it and that's where I think I add, hopefully I still add value. because then I can go to a level of thinking that AI can't do yet.
speaker-0: Think I think that is the perfect combination of like what I've been trying to say along with this show and and my socials that combined with the reason why you would hire that smart employee, in this case, you know, you as a consultant, you know, coming in to help businesses, that's a perfect combination, right? You've got it pulling everything and going, Hey, I did use AI. Here's what I pulled down. Here's my assessment on top of that from my train. ex professional experience that this is what the story is combined with the AI information. It's a perfect blend and that's gonna produce, in my opinion, the best the best results. You got the best of both worlds there.
speaker-1: Right. Yeah. Yeah.
speaker-0: What kinds of opportunities tend to hide in plain sight typically?
speaker-1: ⁓ you know, well, earlier we talked about, for example, the site search engine. ⁓ and I said that it's a great source of market research, which which it is, and I I've several times worked with clients that have, for example, a a huge amount of content on their site or they've got lots of different products, and you see people doing searches for things which are within their area of expertise, but that they don't currently offer. And so right there, you know, you've got literally, hey, there's a there's a clear demand from your visitors for s for this. You could do this. And ⁓ so I actually ⁓ wrote a book last year, which I should probably mention at some point, called Website Wealth, which is full of these kinds of stories to which is designed to hopefully inspire business owners. It's it's all in English, so you don't have to ⁓ worry about the the technology, it's not a training manual on how to use any particular tool. ⁓ but lots of stories about things, business strategy decisions and actions that we've taken that have really made a difference to the business. And and one of those is is literally doing exactly this, developing new products or services based on what we've seen in visitor patterns. I mean, one one story that I have in my book is is ⁓ I was working with a manufacturer of industrial waxes. And so this is a this is a high level B2B business, right? but they, for example, were doing they were a very well known, they were appearing in a lot of search results and so on, and you could tell that a lot of people were coming to their website because they they were consumers, but they wanted to make their own candles. And so they were looking for beeswax and duck wax and all this kind of stuff. And obviously they were they were not going to buy from this business candle. ⁓ at an industrial level. And so the business had been basically sending them away. But, you know, when we saw the numbers, it was like, wait a minute, do we really want to just send these people away? And this business actually ended up creating a little side e-commerce site that ⁓ sold, you know, consumer level amounts of of the product. ⁓ it really didn't take very much maintenance. It didn't need to do a whole load of marketing because they were already getting this traffic. They could easily on their main B2B website have a button that said, you know, if you're a consumer, click here and off you go. And this little side business just ticked along and did all sorts and and really did quite well. You know, and that was basically a decision that was taken looking at the data, looking at what we could tell from the data, and saying, do we want to capitalize on this? And this is another thing that I that I also work with ⁓ businesses on is, you know, are there ours other opportunities that the data shows us that we're not yet taking advantage of?
speaker-0: mean that's that's a great example of I mean you had enough numbers to go, maybe we should. ⁓ and you know now they're capitalizing and getting, you know, more income at at the end of the day and not sending those, you know, individuals ⁓ like that's that's like the perfect example of of data of what you could do at this point. Like it's an opportunity if you want to take that. You know, here's the numbers, right? And from from your story it sounds like they did very well, which is amazing.
speaker-1: So again it it's looking at the data which which is correct, right? Here are all the people that are not are not staying or are not doing business. I mean another example is is people who come from outside your area. there was a company that made ice cream out in the northeast and their ice cream was very well known, very popular in their area, but they didn't ship they did ship it, but not very far because of you know, the ice cream's going to deteriorate and be useless, right? and again, you just looking at the data of how many people come from outside the area that they ship to, and again, saying them saying, Do we want to do anything with these people? and are you then you just you can either say yes, we we don't care, just let them go, or you could say, are there any other creative ideas, for example, we could have we could be selling gift cards on the site so that they can have a shipping a shipment to somebody, you know, who's in the area that they want to send. So it it's it's it's looking at your data and saying, does this tell us any stories that we could use? And at least then we can make the decision, do we want to use this or do we not? ⁓ but not just disregarding it. And again, that dashboard, if you just use the basic dashboard, it's going to say, you know, here are the number of visitors that are actually useful to us. Here are the number that are not useful to us in the immediate term. And you just you know, that's it. And the question is, do you want to think more about that?
speaker-0: missed opportunities. You you're not digging into, you know, what are they coming for? Like all all the things you just said, really. All of the good ideas that you could have uncovered and and had the decision or the choice to maybe do something about. You've given up the choice by not feeling feeling behind the door, looking behind the door so to speak and figuring out. ⁓ so you've actually, you know, talked a little bit or ⁓ a bit about quieter parts of websites, ⁓ you know, sometimes creating more business value than the areas that leadership tend to focus on. You know, why does that happen? Is that just more of ⁓ they're focused on those dashboards and the reds and the greens, or is it just that ⁓ they're not really speaking a lot so they don't tend to focus on those areas?
speaker-1: I think again it it's you know, this is thinking at a different level. So if the business owners are really not focused on this as as a as something that they're interested in specifically, which I as I as I said is perfectly understandable, ⁓ you know, they're reliant on what the agency or their team or AI tells them. and, you know, that's kind of like marking your own homework, right? If that isn't giving them the in-depth analysis or the in-depth thinking or questioning that they need. I mean, the the team doing the reporting is maybe not going to ask that question. Does the evidence support the decision that we're recommending? They may not be asking that. Or they may not be asking that from a high enough level. ⁓ And so unless you you need to get the senior leadership into the mindset of let's look at this in more depth. Let's not just accept what what people are telling us. But and and and not to say that people are wrong, that their teams are wrong or their agencies wrong, but just let's let's just look at this one more time and and see if there's a bigger picture that we can apply or see if there's another question that we need to ask.
speaker-0: Yeah, I I think that's the the interesting and the dangerous point, right? Like that becomes evident then if you have the individuals who are creative enough to take that information and go, Okay, this, this, and this could possibly be happening and or this is the opportunities I see that we could be, you know, trying or taking from that. And if you don't have that part to go with the data part, then it doesn't really matter anyways. But if you have that to be able to read or hear the interpretations of what's happening, then yeah, that's what really starts separating you from the good and the bad. What or the the better businesses we'll call it.
speaker-1: I think there's a lot of temptation to just assume things are humming along. 'cause, you know, if there's no obvious bad news, ⁓ you you you think that everything's working, there's nothing else you need to do, maybe you've got bigger problems, ⁓ y you know, but and you don't realise that maybe you could do something with your website that would as that would actually address some of those bigger problems. and and you know, it's an area that a lot a lot of business leaders are are less familiar with. ⁓ a a and so it's it it it all kind of compounds.
speaker-0: Yeah, no, I I think that's a great example because if it looks like we're hitting our targets, everything's working fine, I'm probably not putting a second effort or a second think into what else could we be doing? What are we missing? Like everything's working great, we're hitting the number. What do you mean? You know, like until you actually put some effort and thought and make sure I at least investigate to ⁓ like validate that that's actually true, then it's just a missed opportunity.
speaker-1: I think that's all that deaf by data actually. Maybe we should talk about that. You know, I I sometimes there's a temptation because you have got AI and you can make a bazillion different dashboards to measure absolutely everything that you can measure. And again, that kind of leads to analysis paralysis, right? I mean it's like, okay, but what are the real questions that we need to be asking right now? so we see all these metrics come in and a lot of them look really good and again, so we assume it everything's everything's right and maybe within that there's something hiding that i it could be really different. ⁓ i you know like you you mentioned earlier you've done a lot of content creation. one thing that I see a lot there is is people looking at initial engagement levels. So you know s number of videos starts. Like if you look at YouTube, I believe that when you see the numbers on the YouTube views, that's people who've started the video. And potentially I can't remember if it's played 30 seconds of it or something like that. ⁓ and then it c counts as a view. What that doesn't tell you is what happens after that. So how many people actually watch the whole thing? And the number I mean, the if you look at those kind of numbers, the the the number of people whose attention falls off after the first thirty seconds, one minute is huge. And if you're investing a tremendous amount in in high level I mean, I've seen this high level B to B video production. And like you've got a ten minute video and ninety percent of your audience only watch the first minute, that's a huge amount of investment that's just wasted. And especially if your key call to action, you know, the thing that you actually want to happen and isn't get doesn't get mentioned until the end. Right. so so looking up the data and saying, okay, we're we're making these decisions and these assumptions ⁓ and again, you know, are we looking at all the correct metrics to to really base base our thinking on?
speaker-0: Yeah, no, I mean that's ⁓ that's all very, very true, very, very valid. we we have to know what's important to our business in particular, right? What are the things that we want to attract? You're right. You can get lost in all the land of data and then y you're not doing any of those things that actually matter to your business because you're worried about everything when maybe you needed four of those things, whatever it was. that you really needed to track, make sure that that was happening. ⁓ so I can I can see it, especially being involved and being in it. And and that's why I've been a huge advocate that sometimes grabbing somebody who is not a part of the organization, has no attachment to it, be able to go just here's what I see right away, ⁓ helps pull you out of the weeds a little bit, so to speak. And then, you know, you can start working on those things.
speaker-1: That's that's great. That's music to my ears 'cause that's exactly what I'm what I'm offering.
speaker-0: That's I mean, th I I see it all the time, especially when we're talking about AI. it's kind of a similar thing. ⁓ you know, AI is in in the data world amazing for that right now. You can get more metrics than you've ever I probably ever asked for or new that you could get. ⁓ but like you said, it's so easy to get lost in it though. what is it that you actually need or want to track or or things you want to watch out for? make that custom, you know, for your business, watch that. And then, you know, maybe keep an eye on a few things over here on the side. I'd I'd be very curious, you know, from your perspective, especially in in in dealing with businesses now with AI being so heavily, the things that you ha you see as challenges when it comes to AI or the things that you recommend for them to pair with. And and maybe it's an example of like an idea I was pitching there. But maybe there's a better version or a better Better way that that you come up with
speaker-1: Well, I I mean, I know that y everybody's talking right now about AI hallucinations and you know, the I think it is a trap that you get something that looks very professional, very well produced, and could be completely wrong. And unless you know how to question it, you don't know if it's wrong. ⁓ I mean that's why I'm saying in my own practice, you know, I I do use AI and I'm not I'm certainly not gonna pretend that I don't. But I take what it tells me and then say, okay, is this can I verify this? You know, this is help this helps me start, this helps me jump in. but is it for real? a and the pieces that it can't do. I I ⁓ I I mean, give you another example. I I was watching screen recordings, so I which I really love because they show you exactly what people do. And there was this one She was a major non profit in ⁓ in New York City and they were having this benefit gala with big names, ⁓ guests and everything. and this was a form that people could fill in to register to s you know, for tick to buy tickets for the gala. And and the screen recording tool was reporting a lot of ⁓ drops. And using a screen then the screen recording tool had an AI assistant and the AI assistant was saying, ⁓ look, hi ⁓ here's the form, and a lot of people are dropping. Right? But it that was where it stopped. It's like, okay, yes, I can see that, thank you. and it was quite subtle. When you watch the screen recording as a human, it was actually quite obvious what was happening, and it was really bizarre. But for some reason They had split their address ⁓ input field over two lines. I don't know why, two separate fields. And both of those fields were required, which made no sense whatsoever. I don't know how this happened. But you could see the person again and again, the person would fill in their address on the first field and continue on, fill in the rest of the form, hit continue, and they got an error. And it and it was just wasn't clear, or it or it didn't continue. ⁓ and Until you saw somebody who came along and and went, ⁓ I see, and they went and split their address and put the second half of the address on this on the second line and boom, that worked. And it was really odd. but the AI couldn't diagnose that. Whereas as soon as you see it with your eyes, you can go, ⁓ look, that's what it is. That's what it is.
speaker-0: Yeah. And it's I think it's a great example of like AI going, Hey, we a problem, which at that point, you know, already already figured out there is a problem, right? But ⁓ in this case it needed the human or or you or who or whoever, you know, that actually's pulled us apart and then it was pretty obvious that maybe we missed QA in in in going through this. and, you know, hey, we're not getting any registrations for something we expect a lot. ⁓ what what what is happening and all of that is a perfect blend of of how you can use all of those aspects to fix the ultimate problem at at the end of the day and and should not blindly trust, you know, AI or or anything else without reviewing the actual data. In this case, luckily you had a screen recording because I mean, yeah, like you said, ⁓ what what lovely behavior at that point to be like 'cause I would have submitted and have been like, what what what the heck's happening? I didn't put my address in. Like why is it time to
speaker-1: You could see people doing that and giving up. Which is also not good for the reputation and the brand of ⁓ you know
speaker-0: Yeah. ⁓ that that is one thing that I I would I would co sign and agree with you on that you gotta be careful with, especially with AI is ⁓ you you can use it. It's a great example of where to use it and maybe don't rely one hundred percent on it because it's like here's a problem, don't know how to fix a problem. Come in with a human set of eyes, you fix the actual issue. You know, great use of both sides, blend, perfect, but that AI tool didn't fix it and now your reputation you know, over and over again of social media all of a sudden it's ablaze with all these bigger names putting on there and then others underneath them kind like, yeah, it's a nightmare, right? We don't want that.
speaker-1: Right, right. And you know, it's funny 'cause I realize that a lot a a lot of this interview, you know, I've been talking about working with with more senior leadership ⁓ on the big question, which is is is the evidence that you're looking at supporting your decision. ⁓ and we just kind of went into the weeds there. But I guess it's all part and parcel of the whole thing. ⁓ I mean I want to start at the big l at the big decis at the big questions and then sometimes you have to sort of dive in a bit and and see what's 'Cause sometimes it i it is, it's something really small that is easily fixed, but that's throwing everything right.
speaker-0: Absolutely. And and I'm glad that you brought that up because it takes me into almost my last question here, which is what questions should leaders be asking before they make the major decisions from analytics?
speaker-1: Well, you know, that's really about understanding your own business and your own goals and and prioritizing what is it we want to do right now. So often I I come in when ⁓ there is a big decision in progress. So it's it's are we going to make a a big investment in in a new marketing campaign? Are we going to make a big investment in redesigning our our website? Right. is Is ⁓ a decision to develop a new product or service supported? Is it true that, you know, of all these different sectors on our website or different audiences that we serve, this one is the most profitable to us? Those kinds of things. ⁓ and and sometimes it it it is more of an ongoing, you know, can you tell us what's happening and what's going on and where we need to focus? But the underlying but the question that that I can't answer is, well, what do you want to be happening? You know, are you clear about the most important things that you're looking at to do? And that's where you start. Knowing your own questions, because that's really not a website question, that's a business strategy question. And we can we can figure out the right data to answer the question, but you you need as business leader, you need to know the questions.
speaker-0: Yeah, ⁓ absolutely. ⁓ I mean more of a if you know the direction, I can help show you the data points to support or don't support that direction and you know, help uncover I guess the rest of the pieces to the puzzle is kind of ⁓ how my mind is is seeing that or hearing that conversation right now. and I'm I'm sure those get into fun conversations as well when you get into those those kind of level of ⁓ you know, discussion. ⁓ but Coming up towards the end here, ⁓ I wanna ask ⁓ one and ⁓ one question in particular for for you with with all of your experience, all of the clients you've worked with and where you're still headed, you know, what does success look like for you today or going forward?
speaker-1: You mean success personally for me? I mean when when do I feel good is is obviously when I've worked with a client, we've figured stuff out. The client has has hopefully maybe learned something that they that they didn't know or didn't realise that's really advantageous to them. I mean it's you know and and it's been fun. I mean obviously it's more fun working with fun clients.
speaker-0: Personally.
speaker-1: You know, when we've done something creative, that's that's always something that really inspires me as well. You know, we've come up with this idea to do something different or or you know, we've solved a problem that we couldn't work out for ages and then all of a sudden you go, ⁓ that's it you know, that that's I mean, you're a data guy, you probably have the same list, right?
speaker-0: I I love ⁓ I think that's the one thing about AI that if I if I have to answer a question ⁓ and I can only pick one thing, it's the fact that we can be more creative than we've ever maybe thought we could be. obviously there's some, you know, dangers with that, but I'm experimenting. I call it's I understand I'm experimenting too, I think is the big key. I'm experimenting with a lot of different things and trying to see where that creativity can go. I I am building something right now ⁓ that does have ⁓ a lot of these points, a lot of dashboards, a lot of reporting in it. So I find this very valuable to me personally, but I'm sure this is gonna be very valuable to ⁓ every business. Every business has data and dashboards and they need this kind of info to drive decisions and and be able to hopefully stay afloat, grow, be better, be a leader in their industry, all all of these points. So ⁓ so yes, I'm very much ⁓ you know, a a part of looking at it. I wouldn't say I'm a I'm a true data analytic. I have appreciation for what data can absolutely tell you and and what you should lean towards versus my opinion. The data is the proof, not my opinion at the end of the day.
speaker-1: Right. Absolutely. Yep.
speaker-0: So with that, ⁓ you know, that's taking us to the conclusion here, ⁓ where can people connect with you, ⁓ find out more about you, buy your book, ⁓ and anything else you have going on?
speaker-1: So as we're speaking, my website is under redevelopment, but hopefully by the time this comes out, maybe that will be done. So that's websites that win, websites that win dot com. ⁓ you can buy the book at all good booksellers online and and offline as well. ⁓ I mostly like connecting with people on LinkedIn and ⁓ I do offer a decision confidence call. So that's a a free 20 minute call just to if, you know, you're thinking through something or you just want to kick something around. ⁓ obviously it's not a a full engagement advice thing, but it it's designed to just say, hey, is there a question here that, you know, you've basically got a handle on and you're looking at the right data off the top of my head? Or is it something that we probably sh you know might be worth dig digging into more deeply? ⁓ so if you'd like to have an initial kick around conversation with me. please ⁓ go ahead and book that a decision confidence call.
speaker-0: Wonderful. And we'll be sure to include everything ⁓ you know, in the links and make sure that the people know where to go. And I ⁓ love ⁓ checking out my guest books. So I will be taking a look at that book as well. ⁓ and ⁓ see what else I can learn further from Justice Convo. ⁓ so Philip, ⁓ it's been wonderful ⁓ having you here. I appreciate you taking in the time to educate myself and the audience. And ⁓ I mean, it's just been a great conversation. Hop hopefully this has been rewarding for you as well.
speaker-1: Thank you, enjoyed it a lot. Thank you.
speaker-0: Appreciate it. And and with that the meeting has been adjourned and ⁓ we will see you all next time, everyone. Take care.
