From Data Chaos to Loyalty Success: Insights with HEMOdata (#709)

Luke: People often think of risk management as being something that only applies to their business lives, but it’s also something that we apply in our everyday lives.

Luke: We just do it more naturally.

Luke: So people who say they’re not good at risk management, they actually probably are, they just don’t realize they’re doing it.

Dustyn: Successful loyalty programs are the ones that engage with the users, hyper-personalize the experience, pull on the right heartstrings to bring customers back over and over.

Dustyn: Don’t understand your customers, and like you said earlier, the demographics of them, how are you supposed to…

Luke: The biggest risk is the risk of doing nothing, and how that leads into the mindset of that fear of failure.

Luke: Failure is okay if you learn from it, and you’re able to manage your risks going into it, and can somehow take that forward.

Lisa: Ozzy Osbourne and Prince Charles, they’re both the same age, and they both like music, but inherently they’re completely different human beings of different likes and different interests.

Lisa: And so, profile level data capture is not what we’re talking about as well.

Lisa: We need to think about the multi-layered approach.

Dustyn: Your job is to cut down a tree, and you’ve got an axe and a chainsaw, then both of those tools can cut down the tree.

Dustyn: But if you’re an arborist and you need to cut down 10 trees a day, then you probably want to have the chainsaw, right?

Luke: Don’t jump in at the top.

Luke: Understand your use case, understand your goals and objectives, and then look at all of the components within the organisation, your people processes, technology and data, and make sure you’re considering all of those, not just trying to go for those quick wins.

Paula: Hello and welcome to Let’s Talk Loyalty and Loyalty TV, a show for loyalty marketing professionals.

Paula: My name is Paula Thomas and I’m the founder and CEO of Let’s Talk Loyalty and Loyalty TV, where we feature insightful conversations with loyalty professionals from the world’s leading brands.

Paula: Today’s episode is hosted by Lisa Brightwell, who founded and leads Bright Insights Consulting, a consulting firm based here in the UAE.

Paula: With over 20 years experience in the region, Lisa specializes in crafting innovative loyalty strategies that harness customer intelligence to drive engagement, differentiation and long-term growth.

Paula: Enjoy.

Lisa: Hello and welcome to Let’s Talk Loyalty and Loyalty TV.

Lisa: I’m Lisa Brightwell and today I’m delighted to be joined by Luke Cann and Dustyn Smith, the founders of HEMO Data, a UAE based company helping businesses harness and manage data more effectively.

Lisa: As loyalty professionals, we all know that data is the foundation of every successful program.

Lisa: But many of us really struggle with fragmented systems, messy data flows and the growing pressure to be AI ready.

Lisa: Today, we’ll explore how HEMO Data helps businesses overcome these challenges, what it really takes to prepare your data for AI, and what the future of data-driven AI might look like here in the UAE and beyond.

Lisa: Hello, and welcome Luke and Dustyn to Let’s Talk Loyalty and Loyalty TV.

Lisa: I am personally very excited that you two are my first guests on the program.

Lisa: So thank you for coming.

Lisa: And I’m excited to talk about data and AI with you guys today.

Lisa: So I need to start though with the first question, which is, can you please, both of you, give me a book that potentially has inspired you, impacted your work or life at some point, or just influenced you in some way in your career?

Lisa: And just tell us about it.

Lisa: Dustyn, you want to go first?

Dustyn: Yeah, sure.

Dustyn: There’s been quite a few books over the years that have had an impact on my career growth, but the one that really stands out is by an author called Gary Keller.

Dustyn: It’s called The ONE Thing.

Dustyn: And yeah, it’s one of those books that kind of really pulled on the right heartstrings for me at a time of my career where I was kind of like, felt like I was, I needed that next step of growth.

Dustyn: And it’s all about kind of balance, work-life balance, being efficient, prioritizing.

Dustyn: And yeah, I think the title of the book kind of explains a lot.

Dustyn: Like it’s what is the one thing that’s going to make the biggest difference to today or this week or this month and make sure you get that out the way first.

Dustyn: And yeah, ever since then, I’ve found a lot of peace in kind of like being able to let go of things, delegate.

Dustyn: There’s a lot of learnings from it that have had a big impact on me as a professional.

Lisa: It’s funny, when Paula asked me that same question, I gave a book that also talked about balance.

Lisa: I think you get to a certain point in your career when you just have to figure that out, right?

Lisa: How do you balance work and living your life?

Lisa: And so it sounds like that’s pretty similar.

Dustyn: It’s one of my mantras now, right?

Dustyn: I work to live rather than live to work.

Dustyn: And I think earlier on in careers, a lot of us are guilty of kind of burnout and like pushing ourselves beyond kind of happy levels or sustainable levels.

Dustyn: So yeah, for me, it came at a really important time.

Dustyn: I was living in Singapore at the time, actually, and it was kind of like my daughter was on the way and I was just like overworking, trying to chase titles and money and all that kind of stuff.

Dustyn: And then the grand scheme of things like that wasn’t what was most important.

Dustyn: It was more being a present dad.

Dustyn: So yeah, it helped me out a lot from that perspective.

Lisa: Yeah, great.

Lisa: Luke, you have to top that one now.

Luke: Yeah, and I think, again, for me, there’s been obviously many books, but some of the ones that really stuck with me, Richard Branson, Business Strip Bear.

Luke: And the reason why a lot of what he writes and talks about resonates is about risk management and how we manage risk and are able to take risks.

Luke: And particularly the biggest risk is the risk of doing nothing and how that leads into the mindset of that fear of failure and actually failure is OK if you learn from it and you’re able to manage your risks going into it and can somehow take that forward.

Luke: And I read this kind of quite early on in my career and it’s just something that’s really resonated and stuck with me all the way through.

Luke: And people often think of risk management as being something that only applies to their business lives, but it’s also something that we apply in our everyday lives.

Luke: We just do it more naturally.

Luke: So people who say they’re not good at risk management, they actually probably are.

Luke: They just don’t realize they’re doing it.

Luke: And if you can think about that and bring that into kind of your business mindset, you can really kind of speed up progress.

Luke: You know, in my career, it’s been about getting projects out and delivering things on time.

Luke: And so, yeah, if we can all kind of think about how we manage and control our risks a lot better, then we’ll get to those better outcomes.

Luke: So yeah, very interesting books.

Luke: And I say he has that theme all through a lot of his books.

Luke: But yeah, something that stayed with me for a long time.

Lisa: It’s interesting.

Lisa: I’ve also read that book.

Lisa: And it’s a good segue for my next question, because I think risk is really important for entrepreneurs, because I don’t think you can be a true entrepreneur without actually, one, failing, but two, being prepared to take that risk.

Lisa: And so it kind of leads me really nicely.

Lisa: Thank you for that.

Lisa: Onto the next question is, you know, you guys are the founders of HEMO Data.

Lisa: Can you tell us a bit more about why did you set up?

Lisa: What was the reason?

Lisa: What inspired you?

Lisa: Whose idea was it?

Lisa: And tell us a bit more about it, please.

Dustyn: Well, funny story.

Dustyn: We were, Luke and I both race in a go-karting league.

Dustyn: So we’re kind of these type of dads that like cars and like driving and we both race in a kind of dad’s go-karting group called The Fats and The Furious, right?

Dustyn: So it’s not serious.

Dustyn: It’s a bit of fun.

Dustyn: But one day after karting, we both just said, like, what do you do?

Dustyn: What do you do?

Dustyn: And I just exited my last company and Luke had already kind of started another company and we’re like, oh, we should probably have a conversation about this.

Dustyn: Like there might be some synergies here.

Dustyn: So we went for a coffee the next day.

Dustyn: And after the coffee, Luke was like, oh, do you want to come around to my house?

Dustyn: And then we’ll go see the office because he had a small office.

Dustyn: So we got the keys and another long story short, but my granddad was one of my heroes and he used to breed chow chows.

Dustyn: And you don’t really see chow chows in this part of the world, right?

Dustyn: They’re quite a rare breed of dog.

Dustyn: Anyway, I walked into Luke’s house and this chow chow greets me and he’s got a chow chow.

Dustyn: So I was like, that was a sign for me.

Dustyn: I was like, my granddad’s here.

Dustyn: He’s telling me this is the right thing to do.

Dustyn: But jokes aside from that, once we sat down, we looked at what could we do as a company.

Dustyn: My skill set and Luke’s skill set are completely different, but actually very complimentary.

Dustyn: And we both kind of agreed that the maturity of this region when it comes to how organizations are using technology and data was kind of on this ambitious journey, but had a lot to learn.

Dustyn: So we set up HEMO Data with the vision that we want to kind of be a part of that journey and use our experience from around the world to upskill and educate this region on how to be a leader around the world from that perspective.

Luke: Yeah, and I think it’s very important that again, going back to some of the business concepts you have is recognizing your own weaknesses and where you have those gaps in capabilities.

Luke: And from speaking with Dustyn, it’s again, that pairing was very good.

Luke: And I think, you know, that’s together, we were able to take our objective to the market, build up the business to what we have now.

Luke: And like I say, be able to help our customers, you know, improve their data maturity.

Luke: And it’s been a really exciting journey to have been on so far.

Dustyn: Yeah, and back to your question earlier about like risk and that, and back to the comment around balance, like we were both at the stage of our lives where we got kids, we’re in a position like, do we want to go and work for someone else?

Dustyn: Or do we want to try and do something ourselves?

Dustyn: And the chow chow was kind of the North Star that was like, no, we have to do this.

Dustyn: But it just worked out really well when it was either you go and throw yourself into another job where you kind of burn out for another CEO, or you go and do it for yourself.

Dustyn: And yeah, three and a half years in, no regrets.

Dustyn: Business is growing, team’s awesome.

Dustyn: But the kind of the business is built around, we’re dads first and we’re family men first.

Dustyn: And we work hard, but we also, it’s not at the expense of ourselves or the team’s kind of wellbeing.

Dustyn: So we very much care about culture and that feeds into how we work with our customers as well.

Dustyn: It’s got to be a good fit for them and it’s always customer first and everything else is a byproduct of that.

Lisa: That’s probably the reason why you’ve been so successful in such a short period of time, right?

Lisa: Hopefully.

Lisa: So let’s talk about data.

Lisa: Yeah, everyone talks about data.

Lisa: It’s a subject that we always go into.

Lisa: But what are the common mistakes that people make when it comes to data?

Luke: Well, I think with that is people want to jump right in at the top and they’re not thinking about the overall journey of what it is they’re trying to achieve and how then data needs to support that.

Luke: So often what we see is a lot of the basics are being missed and you can’t kind of start building your house with the roof.

Luke: You have to have your foundations in place first.

Luke: So it’s things like having the data quality.

Luke: We all know the saying, garbage in, garbage out.

Luke: It’s been with us through numerous cycles of IT trends over the years and the same is especially true in now the age of AI.

Luke: So you have to focus on that data quality and also the non-technical side, the people side, making sure that you have the right processes in place.

Luke: And it’s all of that that comes together, which companies often forget.

Luke: They try to jump right in.

Luke: Maybe they’re just throwing an individual technology at a problem.

Luke: And that’s not actually the solution.

Luke: They’re not thinking about it in terms of a bigger picture.

Lisa: There’s a large quantity of data that’s not usable.

Lisa: It’s ultimately not the goal here.

Lisa: And I think that’s also what we see a lot of people just trying to get acquisition of customer data.

Lisa: But sometimes the quality is not there.

Lisa: And then you end up in a situation where it’s non-usable.

Dustyn: Yeah, and I think like everyone’s capturing a lot of data now.

Dustyn: If you’re a company, right, there’s data you’re capturing in every function of the business.

Dustyn: It’s turning that data into something that you can translate into insights and then use to make decisions, which I think is a big piece.

Dustyn: A lot of organizations are missing, not just in this region globally, I’d say it’s still a problem.

Dustyn: There’s some organizations that get it really right.

Dustyn: And they’re the ones who are kind of thriving around the world.

Dustyn: But if anyone says they don’t have any data problems, they’re lying to themselves.

Lisa: No, I get it.

Lisa: And I think I listened to a Let’s Talk Loyalty podcast before I was a host with Paula.

Lisa: And there was one lady, I think she was from AC Roma.

Lisa: And she was talking about one dimensional data as well.

Lisa: Because you can have someone that…

Lisa: And she gave an example of Ozzy Osbourne and Prince Charles.

Lisa: And they’re both the same age and they both like music.

Lisa: But inherently, they’re completely different human beings of different likes and different interests.

Lisa: And so profile level data capture is not what we’re talking about as well.

Lisa: We need to think about the multi-layered approach to how we capture not just profile demographics, but behavioral data layered together to then utilize in the right way.

Lisa: And I think that people often don’t think about that as well.

Lisa: They just think about, oh, I’ve got great data.

Lisa: I’ve got the birthday.

Lisa: I’ll send them a happy birthday email.

Lisa: We talk about this a lot.

Lisa: But that for me is not using the data in the right way or collecting the data in the right way as well.

Dustyn: Yeah.

Luke: And I think that the whole point about that collection is, again, why are you collecting it?

Luke: What’s it supporting?

Luke: So obviously, there’s rules and regulations around the world, you know, in this region, particularly emerging regulations as well.

Luke: So if you don’t know why you’re collecting it, you shouldn’t be collecting it.

Luke: And again, having it linked to a use case.

Luke: So when we’re going through, what is the understanding we want to have of our customers?

Luke: How are we going to encourage them to engage?

Luke: How do we kind of give that feeling that we understand them, even though we’re dealing with, you know, hundreds of thousands or maybe millions of customers for some companies.

Luke: So you’ve got to get it right all the way through to be able to enable those use cases and to really be able to understand your customer in that level of detail as well.

Dustyn: We’re seeing it in particular a lot at the moment with like organizations having ambitions to like adopt AI.

Dustyn: But AI is just, it’s a tool, right?

Dustyn: And so organizations are going, yeah, we need to use AI, but they’re not mapping it against what Luke mentioned, the use cases, the KPIs, the business objectives.

Dustyn: And so a lot of AI projects are failing.

Dustyn: Like I think it’s what 95% of pilot AI projects are failing.

Dustyn: I think that’s a study by Informatica and MIT, isn’t it?

Dustyn: That comes from, but it’s an incredible amount of projects that are failing because the foundations are under place.

Dustyn: And AI as the tool, you still need the people, the processes, the technology to support that.

Dustyn: And then the data to be trusted, because Luke said earlier, garbage in, garbage out.

Dustyn: If your AI is learning from bad data, you’re going to get bad results from it.

Lisa: Bad results, yeah.

Lisa: We’re going to go into AI in a bit later because it’s a big topic for me, certainly in the loyalty space.

Lisa: But let’s talk a little bit about data and loyalty.

Lisa: And I think we need to be transparent.

Lisa: We work together, right?

Lisa: So us as consultants in my organization and with you, HEMO Data, we regularly work together on many projects because as loyalty consultants, we don’t feel like we can be doing the best job we can with our clients without having a really solid data foundation.

Lisa: And so that’s why we know each other really well, which is why I really am happy.

Dustyn: It’s like the perfect marriage, Lisa.

Lisa: Don’t say that, my husband’s watching, yeah.

Lisa: But so what I think is really important to discuss is for, why is it important for loyalty program owners to own that data discussion?

Lisa: Because often I feel like they don’t think it is the…

Lisa: It is not them within the overall organization.

Lisa: It might be for their loyalty program, but why is it really important that they are kind of part of that overall data conversation internally?

Luke: One of the things is when you’re looking at a loyalty program and how the data feeds that, again, it’s back to the earlier point, it’s all about understanding your customer.

Luke: How is your customer engaging with you and being able to understand the needs of your customer, but you’re not able to do it on a one-to-one basis.

Luke: You have to handle, again, all of the customers of that organization and treat them as though you are having one-to-one conversations with them.

Luke: So if you’re not able to influence the organization and have control over enough of the data flows, because that data can be coming from all different sources across an organization.

Luke: So also increasingly from the offline experiences and in-store experiences as well, bringing what data can be captured about the customer into their profiles.

Luke: So again, you can unify that digital customer experience with the offline.

Luke: And building that better picture with them.

Luke: If you don’t have the oversight of that from a loyalty program point of view, then you’re going to be missing out on parts.

Luke: And that’s just going to hurt the effectiveness of the program.

Luke: It’s really great listening in some of the previous podcasts that we’ve had on this channel, where there’s a lot of those sorts of themes talking about.

Luke: If you don’t have the data to be able to understand your customer, then the loyalty program is not going to be a success.

Luke: And so then, you know, why, you know, you have to re-structure the loyalty program or even question whether you should have one or not there.

Luke: So it’s critical to be able to getting that program right and making it work for the company.

Dustyn: Yeah, and successful loyalty programs are the ones that engage with the users, hyper-

Dustyn: personalize the experience, pull on the right heartstrings to bring customers back over and over.

Dustyn: If you don’t understand your customers and, like you said earlier, the demographics of them, how are you supposed to personalize it?

Dustyn: You just can’t.

Lisa: Yeah, 100%.

Lisa: And having worked with me on multiple projects now, from a loyalty program and data project standpoint, what have you seen as the major challenges from a loyalty program standpoint?

Dustyn: I mean, I could probably talk about an example here.

Lisa: Go for it.

Lisa: Go for it, if you want.

Dustyn: We’ve worked on many projects before.

Lisa: Don’t just say the name, yeah.

Dustyn: But one that stands out is when we worked on, there was a hospitality organization that they were contemplating, should we create a new loyalty program?

Dustyn: But the hesitation from the exec team was that there was potentially, I think, millions of users that would be disrupted if they changed the program.

Dustyn: And the kind of work that we did together with yourselves was like understanding the data, the sources, the online, offline, the people involved, just the whole end-to-end data flow.

Dustyn: And the outcome of that was astonishing, right?

Dustyn: Because where they thought they had millions of users, there was actually only a few hundred that were reachable users.

Dustyn: And so in the end, like they did decide to go ahead with the loyalty program, and it’s been a huge success.

Dustyn: But what they also identified, other than it was a good idea to go ahead with a new loyalty program, was that there was massive kinks in the whole flow of the data.

Dustyn: Because right from the checking clerk at the hotel, who was manually writing down names and email addresses of new loyalty members or guests, there was massive user error, duplication issues.

Dustyn: There was no kind of process or policing of that.

Dustyn: There wasn’t the right tools or technologies to capture that information.

Dustyn: And then the data was going into all different sources and systems across the organization.

Dustyn: So there was never really any central source or truth of the data.

Dustyn: So yeah, good decision to go ahead with the loyalty program.

Dustyn: And as you know, it’s been hugely successful, but uncovered a whole bunch of other stuff that they didn’t even know was an issue in the first place.

Luke: And I think also with that, it’s a great example of where it’s not just an IT problem to be able to solve from a systems and data point of view.

Luke: And this is where you have to have the business working together.

Luke: And so to be able to do anything with your data, it’s always a business problem, not just an IT problem.

Luke: And especially so for, again, loyalty programs, you need to have that coordination across multiple areas of the business to be able to get to the outcomes that you’re hoping to achieve.

Luke: And having clarity in that is important, but also the business processes that feed that, the people working on those need to have clarity on that.

Luke: If they don’t know their part in the data flow and the importance of data quality, why it is important from their business area into how it flows through the rest of the business, again, the outcomes are going to be impacted.

Luke: And in this example, it was something that they were blind to.

Luke: And so it was only with going through the steps that we’re actually able to realize that, yeah, okay, you have to, again, incorporate members from the business into the program itself as well.

Lisa: Yeah, I couldn’t agree more.

Lisa: I say all the time, people think loyalty programs operate in their silo.

Lisa: They’re in a department that’s separate from the rest of the business.

Lisa: But actually, implementing or optimizing a loyalty program is complete business transformation.

Lisa: We’re looking at people, process, tech, data.

Lisa: And if all of those elements aren’t working well together, then the program is going to invariably have some challenges and potentially might not be working as well as it could.

Lisa: So I couldn’t agree more.

Lisa: So are there some easy steps that program owners can look at to try to evaluate their data structure?

Lisa: Because as you rightly said with this example, on the outset, it looked like, great, we’ve got millions of members, things seem fine, but the reality was totally different.

Lisa: What advice can we give?

Lisa: What easy steps can we give to start, to get people to look in at, how can I evaluate this myself where I might have potential challenges?

Luke: I think it’s starting off at the top, understanding what are your goals and objectives and what you’re hoping to achieve.

Luke: And then everything comes down from there.

Luke: So if you know what you want to achieve, you know the types of data points that you can be looking to collect, and then you’ve got some questions.

Luke: Do I already have the data and is it good quality?

Luke: Or do I need to get this data through some, maybe it’s a change to the app that customers are engaging with, or some other way of being able to enrich that data to get to those objectives?

Luke: And then we said it’s not just about the technology side, it is very much about the people and processes side.

Luke: So, you know, look at your structure as an organization.

Luke: What needs to change?

Luke: Do you need to bring in those people from the business who understand the business processes to be able to support those initiatives like sales and customer services teams, for example.

Luke: How are they feeding into, you know, that overall loyalty framework and loyalty program that is being run?

Luke: And so if you start from very clear objectives and definitions, like I say, everything can then flow down from there, and it should become a lot easier to start breaking this big problem down into smaller chunks that as a business you can start to address.

Dustyn: And just to add to that, like from the data side as well, if you do it right from the beginning, you can maintain that data quality, you can put in the right rules, controls around the processes and people to make sure that you’re not just making everything nice now and then six months later, you’re going to be having dirty data again.

Dustyn: The other thing that I also find is super important is using the right tool for the right job, right?

Dustyn: Because there was a stat that came out recently from, I think, it’s Martech Academy, something like that.

Dustyn: I can’t remember the name.

Dustyn: Don’t quote me on that.

Dustyn: But there’s like over 15,500 just within Martech.

Dustyn: The Martech stack 15,000 technologies out of Silicon Valley alone and they’re all saying they’re doing Gen.AI, they’re all saying they’re doing the best analytics, and so it’s really difficult for folks who use technology to choose the right tool for the right job.

Dustyn: There’s so much overlap and there’s so much potential kind of synergies between these tools, but actually making sure you really understand the objectives and the use cases and then mapping a technology against that is critically important.

Dustyn: Even if you forget technology, right, if you look at other tools, you’ve got your job is to cut down a tree and you’ve got an axe and a chainsaw, then both of those tools can cut down the tree.

Dustyn: But if you’re an arborist and you need to cut down 10 trees a day, then you probably want to have the chainsaw, right?

Dustyn: Even though the axe can do the job, it’s going to be a much more painful way of doing it.

Lisa: It’s a nice analogy.

Paula: Yeah.

Dustyn: Got a few of those.

Lisa: But yeah, I mean, I often find that when it comes to tech, what happens is that a lot of tech companies, and there are thousands out there across, certainly in our industry, in loyalty for everything from, you know, analytics tools to loyalty management platforms to martech, but you get companies come in and they’ll present these fabulous technologies and they’ll do a really good sales job.

Lisa: And people are like, that’s amazing, I’ll take it.

Lisa: But to your point, don’t really think about what they need it for, but also what potential other platforms they have internally within the business that could also do some of these elements.

Lisa: And you get overlap.

Lisa: And then sometimes you’re spending money unnecessarily.

Lisa: And so it’s really important to understand what you need.

Dustyn: That’s really important, right?

Dustyn: Because the other thing that we see very often when it comes to common mistakes is these, these silos of data.

Dustyn: So you’ve got finance who use their set of tools, marketing their set of tools, sales their’s, product their’s.

Dustyn: And so, and they’re all capturing customer information.

Dustyn: There’s very rarely a single source of truth for the data.

Dustyn: And so executives are still ending up in meetings going, no, this is what my data says.

Dustyn: No, this is what my data says.

Dustyn: So, so having that kind of integrated technology stack, understanding what tools you’ve already got, because you’re right, you might have tools that could already do some of the work.

Dustyn: And then the other thing is around the charging models of a lot of these tools are consumption based or, and so if you’re paying for a tool that captures data and you’re deciding to capture all your data, then you’re going to be paying for that.

Dustyn: Whereas if you do what Luke said earlier and understanding the business objectives and what data do you need to capture in order to get the answer to your question, then you actually end up paying a lot less for these tools if you’re efficient and you’ve done that kind of homework in the beginning to only capture the data that you need in order to get to the result you’re looking for.

Luke: Yeah, and I think as well, we work with a lot of customers where again, it’s that IT led, we’ll throw a new tool at this, we’ll throw a new software solution at this and thinking about it in isolation.

Luke: And again, what we want to look at in a lot of scenarios, particularly for those loyalty programs, is having a clear data flow, again, all linking to that objective.

Luke: So having the right integrations, and again, to get to that point, it’s having the right people in the business, the right organizational structure.

Luke: It’s a lot easier to throw a solution into the mix of all of the solutions I already have, rather than try and actually think about business processes and organizational outside of my particular area.

Luke: And I think that’s why there’s probably a lot of resistance to that.

Luke: And this is why some of the organizations take this approach.

Luke: It’s just easier to kind of put a sticky tape over it rather than think about it holistically.

Luke: And where we see those organizations that get that right, it becomes a lot more effective.

Luke: The loyalty program is more effective.

Luke: It runs more efficiently.

Luke: You have a better understanding of the customer, better engagement.

Luke: And everything is just working harmoniously together.

Lisa: And you know, it’s interesting when having worked with you guys now for quite a few years, you’ve taught me a lot as well.

Lisa: And I’d consider myself someone that obviously loves data, likes to utilize data to drive consumer behavior.

Lisa: But the multiple things that we look at now, together, it blew my mind originally the things that I was not even thinking about.

Lisa: And one of those things that I always think about now is the data culture.

Lisa: Because there’s one thing having your objective, your systems, your process, your technology.

Lisa: But I also think how important it is to educate internally and have a data culture explaining to people that may not understand, it may not be their job to understand the importance of data and driving a data-first kind of culture internally.

Lisa: And I think that’s really shifted the way that we train or create training documents for loyalty program strategy as well, just to kind of explain to the people that work in organizations the importance of data and drive that culture from within.

Lisa: And I think that’s been, I’ve certainly been on a journey of learning with you guys.

Lisa: And I think that it’s really important for people to understand all these different layers.

Luke: Yeah.

Luke: And I think, again, if you bring the organization with you, then the organization stands to benefit of a whole.

Luke: So improve the data literacy and data maturity of everyone versus a few people in a few departments.

Luke: So, again, if that’s something that you’re able to get right, the whole organization benefits.

Luke: And not only benefits from the efficiencies in data flow and data utilization and how all that’s working, but you’re also reducing your overall risk from someone taking some data and uploading it to the internet or putting it into an AI tool that perhaps they shouldn’t be, which is all too common now.

Lisa: I wish everyone does on chat, TPT, yeah.

Luke: Absolutely.

Luke: So again, you’re not just educating about the reasons why they have importance to a particular data flow and how that works its way up to the loyalty program.

Luke: It’s also you’re protecting your business and elevating almost every angle of your business in bringing that whole data maturity up.

Luke: So the organizations that are getting it right are the ones that are going to be best set up as we go more and more into the AI era.

Lisa: You’ve just given me a nice segue to our next section.

Lisa: Thanks, Luke.

Lisa: But AI, let’s talk about AI.

Lisa: We talked a lot about data and AI is that buzzword that everyone’s talking about.

Lisa: AI readiness, AI.

Lisa: And there are for me in the loyalty space, some programs that are doing a really good job of one, structuring their data because that’s the first step and using a multitude of AI tools because it’s not actually one.

Lisa: It usually is multiple.

Lisa: And I think my colleague Amanda Cromhart recently did a podcast with Rob Pope from Maya One in Australia.

Lisa: And I would recommend people to listen to that because what they’ve done is amazing with their data and utilizing AI.

Lisa: But so for me, what is being AI ready actually mean?

Lisa: So if you’re a loyalty program listening to this, what does it mean to be AI ready exactly?

Dustyn: What’s funny, we just two days ago ran a master class with the British Chamber of Commerce on AI readiness.

Lisa: See, it’s a buzzword as well, AI readiness.

Lisa: But what does it actually mean?

Dustyn: It goes back to the, I mean, we live in Dubai, right?

Dustyn: And I know this is a global audience, but Dubai is hugely ambitious to kind of adopt AI super quick.

Dustyn: And it’s an outstanding number of projects that are failing because the foundations are not in place.

Dustyn: So it goes back to the foundations.

Dustyn: And I’ll let Luke add more to this in a minute.

Dustyn: But there’s all sorts of considerations and understanding of AI.

Dustyn: That means AI projects are not working.

Dustyn: But also the asking the question, what are you trying to solve?

Dustyn: What’s the problem you’re trying to solve?

Dustyn: Then look at how you can get to that with the kind of right foundations in place.

Luke: Yeah.

Luke: And I think we’ve got this concept now of the AI iceberg.

Luke: So we’re seeing all of these very clever AI solutions that companies are putting out there and letting us use, particularly from a loyalty and customer engagement point of view.

Luke: And then other companies are looking at that and think, I want that.

Luke: And they want to jump in to, again, putting in the technology that enables that.

Luke: The problem is, if you don’t have the data behind it, good quality data, data that you understand, data that’s actually going to align to the outputs that you want, it’s not going to have that same effect.

Luke: And we’ve already talked about, there are some staggering facts out there and stats on the amount of projects that are failing while trying to do this.

Luke: And let’s take again, like chatbots, for an example, we’ve all seen those and we’re getting the obviously AI driven chatbots.

Luke: And if they’re done well, they can be extremely convenient for the customer and how they engage.

Luke: They can get answers to their questions 24 hours a day.

Luke: It takes loads off contact centers and customer service teams.

Luke: And so they’re all the great use cases.

Luke: If you get that wrong, it can actually be a total detractor for your customers.

Luke: So you might think you’re providing them with this great shiny new service and they’re not actually getting that.

Luke: You’re actually turning customers away.

Luke: And it’s because we don’t have all of this work under the surface that the organization that get it right have done.

Luke: You know, understanding the data quality, the business processes and flows that we’ve talked about.

Luke: But even things like, again, the ethics, around whether they should be using certain types of data in their AI use cases and that side of things as well.

Luke: So it’s important that companies, again, go back to the basics, understand actually to enable my use cases, I need to put in my foundations.

Luke: I can’t just jump straight to the tip of the iceberg and expect to have these kind of great AI products that are servicing my customers.

Lisa: And so I think everything we’ve talked about about data and strategy feeds into the AI tools.

Lisa: How do people look into what tools could be interesting for them?

Lisa: I mean, that is probably one of the things I would struggle with is that, okay, I’d want AI tools to facilitate making recommendations on my data.

Lisa: But it’s probably similarly with the technology statement you made before, so many platforms now, so many tools.

Lisa: How do people navigate that?

Luke: Well, it’s going back to basics.

Luke: You start with the objective and to Dustyn’s earlier point.

Luke: You know, AI is still a tool, and a tool still needs to have a purpose that’s clearly defined.

Luke: We need to understand why and why it’s doing something and what it’s doing to be able to enable that.

Luke: And again, I think a lot of companies are making the mistake where, again, just rushing in, seeing the first technology, competitors have it, and we’re just going with that.

Luke: There’s not the, let’s take a step back, let’s understand why, what is the use case for this tool?

Luke: And again, when you think about that and you think about that use case, like we did, or should be doing, is understanding what’s my ROI on actually using this tool.

Luke: Is it the right tool?

Luke: We’re going to start to get better results, but I think we’re still in that stage with AI tools where customers are rushing to get what the competitor’s got or the latest solution, again, without thinking about the foundations or the reason why they want that.

Luke: And that why is very important.

Luke: What is it that you are trying to achieve and why do we want to do it?

Luke: And if you’re clear from that point, everything else under that will start to fall into place.

Dustyn: And there’s some incredible examples.

Dustyn: I can’t remember all of them from the other day, but the folks are getting AI right are smashing it.

Dustyn: So one of the stats was like Spotify increased user engagement by 40% just by doing personalized playlists.

Dustyn: There was another stat from AWS, I think of something like 360,000 hours saved for the use of AI on some concept.

Dustyn: But then there’s also horror stories, right?

Dustyn: We were talking with a customer the other day where someone in the organization was tasked with finalizing a go to market strategy and they thought, oh, well, just kind of copy and paste this into chat GBT and then kind of fix it up, improve it and then go and present it to the C level.

Dustyn: Next thing you know, competitors gone, what’s my competitors?

Dustyn: Oh, gosh.

Dustyn: Go to market strategy and then the whole thing comes up.

Dustyn: So things like with chat GBT, unless you tell it not to use your data to train, it will.

Dustyn: And it’s on by default.

Dustyn: And it’s not just chat GBT, it’s a lot of these AI tools.

Dustyn: So the kind of responsible use of AI is something a lot of folks don’t even think about.

Dustyn: Even all these trends, you know, where everyone’s kind of turning themselves into these little cartoons, like digital nomad cartoons.

Dustyn: Like the amount of data that is hidden behind a picture is massive.

Dustyn: So just yeah, I think educating yourself on AI first and then kind of using the use case of business objectives second is kind of something that everyone needs to be thinking about.

Lisa: So let’s fast forward three to five years.

Lisa: What excites you about the potential of what you’re seeing in AI?

Luke: I think there’s obviously so many possibilities.

Luke: I think, again, some of the stats that we’ve talked about already, where you have a lot of AI projects that are failing.

Luke: There are those ones that are changing how we live and work and engage with, you know, businesses and brands and, you know, the companies that we want to do business with.

Luke: So, I think we’re going to start to see more and more of that.

Luke: You know, every solution is now powered by AI.

Luke: And so, I think in a few years’ time, it will just be, this is the solution.

Luke: The AI part will be kind of a given.

Luke: And so, again, those use cases across all walks of life are just phenomenal.

Luke: And I think, you know, it’s already here to stay.

Luke: And its impact on loyalty programs is just going to continue to accelerate.

Luke: And the businesses that get their foundations in place, you know, get themselves into a position to enable these use cases, whatever it is, either things we understand today or what may come up in a few years, they’re going to be the ones, the leading organizations, the leading loyalty programs in the future.

Luke: The MAYA ONE example, being a brilliant example of that, where they totally turned themselves around and have these enabled use cases now.

Luke: And you know, the success of their program will take them into the future.

Luke: There’s a lot of hard work that went into that under the surface.

Luke: So organizations can’t just look at that and think, I’m going to replicate that structure as a loyalty program.

Luke: It’s all of the other elements under the surface, that iceberg that needs to be in place, and organizations have to be thinking about and getting right if they want to get to that kind of level.

Luke: And so yeah, over the next three years, it’s going to be really interesting, because the ones that get it right are still going to be here.

Luke: The ones that don’t are going to fall by the wayside.

Dustyn: Yeah, for me, we asked Linda Boone, right?

Dustyn: And even some of the big providers are not getting it right.

Dustyn: What’s the one you used the other day?

Dustyn: Was it Who Remembers the Metaverse?

Luke: Well, yes.

Luke: Yeah, I think that’s a good example.

Luke: That was a big rush.

Luke: And then now no one’s talking about it.

Luke: Or very few people are talking about it.

Luke: And yeah, we used that the other day.

Luke: Who remembers that?

Luke: Because AI has just come in and replaced it all.

Lisa: So I was going to ask you a question, but I feel like I know the answer.

Lisa: And it probably sounds silly to ask.

Lisa: But the question was going to be, will AI change loyalty program design?

Lisa: But I think it’s inevitable that it will do.

Lisa: And so I don’t feel like asking you that question, because I know the answer.

Lisa: But we can discuss it.

Lisa: I mean, it already has.

Lisa: But it also, the way that loyalty program owners will think about the design, the value proposition, is what can the data, what can I use the data for to give more value back to my customer outside of just kind of the normal things that we’re used to?

Lisa: What could I do to, I don’t know, in an opaque way to surprise and delight people?

Lisa: I think it already is changing my opinion on how we construct a program design.

Lisa: So it’s going to be an interesting time ahead.

Luke: Yeah, absolutely.

Luke: And I think, again, being able to give customers that increasingly personalized experience, making them feel like you understand them a lot more.

Luke: That AI piece is really going to accelerate that.

Luke: But again, you have to have the data feeding in and all of those points that we’ve talked about so far, they all have to be in place to be able to enable that.

Luke: So again, it will be interesting to see how that plays out in the next few years for loyalty programs as well.

Lisa: Yeah, absolutely.

Lisa: And so if people want to find out a bit more about HEMO Data and what you guys get up to, how can they do that?

Dustyn: Well, we do share quite a lot of educational content.

Dustyn: So our LinkedIn page, it’s HEMO Data.

Dustyn: Our website’s hemodata.me.

Dustyn: And yeah, anyone who wants to connect with Luke and I, we always love speaking to people and learning from others as well.

Dustyn: So yeah, but the website and probably LinkedIn are probably the two most easy places.

Luke: I don’t think we should give our telephone numbers out on a global podcast.

Lisa: You can just message me later.

Lisa: I guess final thought then, any final thoughts you want to add for today?

Lisa: Anything we didn’t discuss that you’d like to leave in our listeners’ minds?

Luke: Well, I think it’s a case of going back to basics, like understanding, don’t, like you say, don’t jump in.

Luke: At the top, understand your use case, understand your goals and objectives, and then look at all of the components within the organization, your people, processes, technology and data, and make sure you’re considering all of those, not just trying to go for those quick wins.

Dustyn: I’m different, Luke.

Dustyn: This is where our personalities are different.

Dustyn: I’m like a risk taker, so I’d be like, take a risk, try something, if it doesn’t work, loan from it, and move forward like that.

Lisa: Amazing.

Lisa: Thank you guys so much for being here with me today.

Lisa: I know that I’ve learned a lot over the last few years working with you, and it’s been great to have a chat and let the listeners hear some of that learning as well.

Lisa: So thanks so much.

Lisa: And if anybody wants to get in contact, like you said, your website and your LinkedIn profiles, so I’m sure a lot of people will be reaching out to connect.

Lisa: And thank you for the listeners and the viewers for watching Let’s Talk Loyalty and Loyalty TV today, and I hope they enjoy.

Dustyn: Yeah, thank you very much.

Luke: Thank you for having us here.

Paula: Thank you so much for listening to this episode of Let’s Talk Loyalty.

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