In the latest episode of the Loylogic Podcast, we welcome Len Llaguno, founder of KYROS Insights, the world’s only actuarial firm exclusively focused on loyalty programs, to discuss how actuarial techniques are transforming loyalty program design and delivering commercial success.
Len combines actuarial science, machine learning and deep loyalty expertise to tackle the financial complexities behind some of the biggest loyalty programs on the planet. Len and Loylogic have worked together for many years to elevate loyalty success, including transforming how organizations like African Bank optimize their award-winning Audacious Rewards programs.
Based around the philosophy of “Show me the incentive, and I’ll show you the outcome” this episode offers actionable insights into how actuarial science, data-driven strategies, and thoughtful redemption design allied to effective redemption marketplaces can transform loyalty programs into sustainable, profit-driving assets.
Talking points include:
- Member Equity as a Growth Driver: Using actuarial analytics to gain the insights needed to understand the behaviors behind loyalty-based growth.
- Designing effective loyalty programs: Leveraging data to identify member actions that increase CLV and EFP, driving both loyalty and financial growth.
- Data-Driven Decision-Making: Harnessing predictive models to de-risk liability, optimize redemption activities, grow loyalty program success and amplify ROI.
- The Redemption Marketplace Advantage: Why offering a broad choice of rewards options – which members actually want – is critical for engagement and long-term profitability, because burn drives earn.
- Balancing engagement and financial integrity: how redemption marketplaces power loyalty success.
- Liability Management: Properly accounting for redemption costs ensures programs avoid short-term decisions that actually harm customer experience and long-term loyalty.
- Future-Proofing Loyalty Programs: The evolving role of actuarial science in ensuring financial integrity and sustained member loyalty.
Ready to redefine loyalty using financial science? Listen now. A full transcript of the podcast is also available below.
(01:36) Len, how did you end up bringing your actuarial expertise to the world of loyalty?
Len Llaguno: "Maybe Austin it would be good to start with the question of, what is an actuary? Not a lot of folks in the loyalty space have interfaced with actuaries before. The stereotype of an actuary is we're sort of like the math nerds that work for insurance companies, and we're building the models to predict, for example, how long you're going to live, right? And obviously that's a really important model to have if you're trying to price a life insurance policy. So, the work that we're doing, fundamentally, is trying to predict over a long horizon, how long somebody is going to live, as a long-term prediction exercise. We're trying, as actuaries, to predict over long horizons and use that information to make smarter business decisions today.
"So many years ago, when I was a young actuary working for a big actuarial consulting firm, one of the big hotel chains came to us and said, 'Hey, my auditors are asking me a bunch of questions about all of these loyalty points that we give out to our to our members whenever they stay with us, and they're saying, Hey, you're issuing all these points that may not get redeemed for years into the future, but you need to book this liability on your balance sheet, as these points are eventually going to cost something, and that amount is x, right?' The hotel chain had no idea how to estimate that number, so the auditor said, 'You should go talk to an actuary. Actuaries are good at this.' Luckily, I got staffed on this project, and it was so interesting. It was way more interesting than sort of traditional actuarial work in the insurance industry! And I found it so much fun.
"The main thing that we started to realize when we got into this was, you know, traditional actuarial techniques that were built for the insurance industry, 50 years ago, didn't work that well for loyalty programs. You know, loyalty programs are just much more fluid and dynamic than an insurance policy or insurance portfolio, generally speaking, and you know that's because, by definition, you're trying to change people's behavior with the loyalty program, right? And so, these traditional methods that really were quite old, developed decades ago, just weren't responsive enough to really pick up the signal that was necessary to accurately predict these long-term redemption behaviors.
"I did my degree in computer science and actuarial science, and so at the time, I was doing a lot of machine learning type work and, although back then it wasn't called data science, I was doing a lot of data science type work. And, you know, I thought that there was something really interesting at the intersection of modern-day machine learning, data science and actuarial science. And I thought if we could really tap into that intersection, we could build something really interesting for loyalty programs that would solve a lot of their problems. And so, I set out to build that, and I've been on that journey for more than a decade now, probably coming up on like 15 years, trying to work through and optimize that.
"Every single loyalty program has this very difficult problem of quantifying and managing the liability related to outstanding points that they're issuing. This estimation problem is very hard. There's all kinds of complex accounting rules that you have to apply, and often it's a huge item on the balance sheet. It's financially material. The CFO cares about it. The auditors care about it. And even then, there's all kinds of other stakeholders within the organization that are seeing this massive cost, and they're wondering, what is the ROI we're getting on this? And you know, how do we optimize that ROI? All of these are very difficult problems that, if done poorly, if answered poorly, can create a lot of financial risk and volatility for the organization. And so that's what we try to do Austin, is we try to solve all of these problems for our clients. We strive to build the world's best financial operating system for loyalty programs, and basically take all that financial complexity and just make it very, very simple.”
(05:51) Fantastic. So, as I touched on in the introduction, you've been collaborating with Loylogic and the work that we've been doing with African Bank over the past couple of years. To provide context and taking into account your explanation there of what you're doing, what has the work entailed, and how has this contributed to the success of the program so far?
Len: "Yeah, we've been working with the team at African Bank, and they've just done such a tremendous job. Their head guy, Nceba, is a tremendous leader, visionary and just all round, really great guy. He's led them to win several awards, both domestically, in South Africa and internationally, too. So, it's been wonderful to work with them.
"We do a wide range of actuarial analytics for them. First thing we do is we help them understand and forecast redemption behavior, which is really critical to understanding redemption costs and liability. You know, redemption cost is the single largest expense in the loyalty program business model, so it's really important that you have a really good understanding of what those costs are. So that's sort of the first thing we do for them.
"We also build all kinds of models to predict customer lifetime value, or CLV. CLV is an estimate of how much profit a customer will generate over their lifespan as a customer. And this is profit, so it's a bottom line number. It's profit net of the redemption costs. And making a net of redemption cost is really important because it's a bottom line notion, as opposed to liability, which is just focused on cost, right? So, customer lifetime value being net of redemption cost being a bottom line notion, it really gives you a sense for the net economic picture and helps put the liability into context.
"So, you need both of these, this understanding of liability, and you also want to have this understanding of customer lifetime value to get the full picture of a program's financial foundation and economic performance. So those are the type of things that we do for African Bank, and we use these models and the insights we can gain from these models to help uncover risks that they should be mitigating and opportunities that they should be trying to exploit, right to ultimately help them drive business performance."
(08:08) I think it's fair to say that we're talking about making the loyalty program as sustainable as possible and unpacking that member equity. Thinking more broadly, away from just the African Bank example, how would you define member equity, and why is it critical for loyalty programs?
Len: "Yeah, that term member equity is a really, really important one. So, let's start with defining it. I spoke about customer lifetime value. This is the expected profit that a customer will generate over their lifespan as a customer, net of redemption costs. When we think about customer lifetime value or CLV, you can really decompose that into two components. There's the profit that a customer has generated to date. It's a known quantity. We know how much profit they've generated. And then there's the profit we expect the customer to generate in the future, right? The expected future profit, right? And we call expected future profit EFP.
"EFP is really the one you mostly care about, because the profit to date, you know, you can't influence that. It's already happened. The only thing you can influence is the future profit. So, most of our time is spent focused on EFP, expected future profit. Now, expected future profit is sort of a long-term prediction exercise, again, right? Because we're trying to predict not just how much profit they're going to generate next month, but many years into the future, over a very long future horizon. It's an actuarial prediction exercise at the end of the day. So that's what EFP is, the expected future profit for a given Member.
"Member equity is simply the sum of EFP across all of your members. So EFP is an average per member. And member equity is the total profit across all members, right? That's what member equity is. Why is that critical? The reason is every single loyalty program is trying to increase customer lifetime value. That's a fundamental goal of every single loyalty program, because the whole point is to win the loyalty of the customer, so they keep coming back. And we want them to come back, not just next month, but the month after that, the month after that, the month after that, the year after that, the year after that. We want them. We want to improve their retention forever into perpetuity. The perfect way to quantify the impact of improving long-term loyalty and long-term retention and putting that into dollars and cents is CLV. Or, more specifically, EFP, the future part, right?
"Here's how it all connects to you. If a loyalty program is increasing customer lifetime value, we're increasing member equity. If we're increasing member equity, it means we're expecting more profits in the future. And if we're expecting more profits in the future, it means the company is more valuable, because companies are valued based on how much profits are expected in the future. Member equity is a powerful concept because it directly links the loyalty program and everything the loyalty program managers are doing to financial results and enterprise value creation. Does that make sense?"
(11:36) It does absolutely. So, I guess the obvious follow-on question from that is, how deep do your analytics go? What sort of insights do you provide into optimizing that lifetime value of loyalty program members?
Len: "The first thing you need to have are good EFP models to predict that future profit. You want to predict it at the individual member level. It's really important that you're able to look at all the characteristics of each individual member and make this forecast of how much profit they're going to generate in the future. Once you have these EFP models, there's still a lot more analysis that needs to be done.
"What these models enable you to do is study the customer journey from an economic perspective. So, what does that mean? Most companies certainly look at the customer journey from a customer experience perspective, and this is extremely valuable, and they should absolutely do this, but that is not what we're talking about here. When you look at the customer journey from an economic perspective, what you're focused on is identifying where the inflection points are in the customer journey, where EFP really increases the points where a customer does a certain behavior, and that behavior work results in EFP taking a jump upwards. What that means is, when the customer does that behavior, they become much more likely to return and spend more in the future. Ultimately, if you can identify those behaviors, and you can get more people to do those behaviors than they otherwise would, you're increasing EFP and creating incremental value for the organization. That's the basic notion.
"What we excel at is taking a rigorous, scientific approach to doing this, which really enables us to quantify the incremental value the program is generating and then communicating that to stakeholders and also finding the levers to drive even more incremental value, and then, of course, communicating that ongoing improvement of incremental value to stakeholders. And what this does at the end of the day is help our clients get recognized for the value that they're creating through the loyalty programs.
"For far too many programs, this value just goes completely unrecognized and unnoticed, because it's very difficult to quantify. So, a lot of important stakeholders within the organization just sort of think as loyalty as a cost center rather than a value generator. And it's this sort of process and analytics that helps to change that perspective."
(13:54) That's interesting, and I'm going to go slightly off script here, Len, if that's okay. We're talking about a very scientific approach to loyalty programs, which is clearly transformative. But how do you take into account the emotional loyalty, that emotional connection that a customer has with a brand because they've experienced great service or something memorable has happened, or whatever? How do you build that into your modeling?
Len: "Yeah, so it's sort of implicitly in the data. Even though I don't have a variable in the data that measures the emotional reaction that somebody had when they did a redemption, they nonetheless had that emotional reaction and therefore embedded in the future behavior that they're going to do. I'm a big believer that there is actually a huge emotional reaction that happens when you do a redemption for something valuable and something meaningful to you.
"A personal anecdote here Austin. Last spring, I took my wife and my kids on a spring break in the Caribbean for free, because I was able to redeem a ton of points, and we were all able to go. It was wonderful. We had such a great time. And what happened was, because it was so much fun, I told everybody about it! All my friends heard about it. All my family heard about it, and they probably heard about it multiple times! And the same thing happened this Christmas. We went back to Canada to visit my family. We all got to fly for free. And I tell you what, everybody heard about it, and so that sort of emotional connection and engagement I was having by just sort of talking about it, really reinforced my connection to the brand and my loyalty. That's just a personal lane, though. I think that that happens a lot.
"A lot of contemporary thinking is all about the customer experience and trying to drive unique and bespoke customer experiences for people. And yes, you should absolutely do that. But that doesn't mean that points aren't also really, really powerful, or that the ability to actually redeem isn't really, really powerful in driving emotional connections. I think it's quite the opposite. The beauty also, of points, is you can do that at scale. I fly United because I'm here in Chicago. Think of all the United customers that are having this same experience that I am. That's probably millions. And that level of scale is very difficult to do with bespoke, one-off experiences. They are certainly going to wow your customers, but you can only do so many of those per year."
(16:38) Do you have any other examples of how predictive analytics are driving impactful customer engagement strategies?
Len: "Predictive analytics is certainly a massively underutilized tool for loyalty programs, I think. And so let me try to explain why. Do you know who Charlie Munger is Austin?"
Austin: “No, I don't.”
Len: "Charlie Munger is basically Warren Buffett's right-hand man. Well, maybe even saying right hand man is, not doing justice, he's Warren Buffett's partner at Berkshire Hathaway, a legendary investor and businessperson, right? One of the things he once said was, 'show me the incentive, and I'll show you the outcome', which is a very simple quote that just speaks to the power of incentives to drive human behavior. A loyalty currency is simply an incentive mechanism to drive the behaviors you want, at this most sort of basic form. It can be an incredibly powerful tool to drive member equity if you know what behaviors you want, which you know I spoke about earlier, we now do because we've studied the customer journey from an economic perspective and can identify those behaviors. It could be an incredibly powerful tool if, one, you know the behaviors you want to drive, and two, you know the optimal incentives to drive that behavior. The problem is the incentive that is optimal is different for each individual person.
"For example, I might require 1,000 bonus points to do something, whereas you Austin, maybe you require 5,000 points to motivate you enough to do the same thing. If you really want to maximize the effectiveness of your currency as an incentive mechanism, you need to do it with precision. You need to create personalized points-based offers for each member that maximize the likelihood that each individual is going to do the desired behavior. The problem here is most programs are not doing that. They're using their loyalty currency more as like a sledgehammer to incentivize where sort of everybody gets the same thing, rather than like a scalpel, where the right offers are sent to the right people with precision. And when we do this, you know, we're seeing incredible impact, like a two to five times improvement on ROI, on your campaigns. And so that's very worthwhile. And I think it's, you know, a great opportunity for all the loyalty programs out there.
"The key to it really is sophisticated predictive modeling, machine learning and a couple of proprietary algorithms that we've developed, or the tools that we found are extremely useful to unlock this value. You know, it's a great example of how predictive analytics can be really impactful and helpful to drive customer engagement strategies."
(19:16) I guess there's a nice segue there into redemption marketplaces, which obviously is where Loylogic's strength lies. How important is it from your perspective that you have a wide range of rewards options that entice and can be personalized, etc.? And secondly, how do you ensure that the proper liability accounting principles are in place to ensure the ongoing success, the sustainability, if you like, of that program?
Len: "Let me tackle Part B of that question first, because I think there's a lot to say that is really important. I mentioned earlier that redemption cost is the single largest expense in the loyalty program business model. So that alone makes it extremely important. You should spend a lot of time really understanding it, but it's unlike other business expenses. You know, if you're in the business of making widgets, you buy all the raw materials to make your widget, and then you make your widget and you sell it. And you know, immediately, without any uncertainty, the cost of making that widget, because you incur that cost immediately. That is not true with the loyalty program.
"With a loyalty program, you're issuing points today, you're selling your widget basically today, but you're not actually going to know how much it's going to cost you until people redeem those points, potentially years into the future. And by the way, a ton of them are not going to redeem the points. That's breakage that also affects the cost of these points. It's very, very different from other types of business models, and so it makes it even more challenging to try to understand what that cost is, what the redemption expense is going to be, which, again, is your single largest expense in your loyalty program business model, right?
"So, it's not really a good idea to run your loyalty program or any business without really understanding that expense, particularly as it's the largest one. So, we need to do this to ensure that the program is standing on a strong financial foundation. But a lot of programs are not spending a lot of time to do it right. It's very hard. It requires expertise, sort of at the intersection of actuarial science, data science, finance, accounting, economics and, of course, loyalty programs, which is just sort of really hard to find, and for most loyalty program managers, this is literally the most boring part of a loyalty program. They're loyalty marketers, they're excellent at, you know, building engaging strategies and campaigns and communicating with customers and creating incredible experiences, right? They're not experts at, you know, debits and credits on your balance sheet, or the nuances of actuarial analytics. Most of them, from my experience, don't really care too much about that stuff. It's extremely boring. Program managers are just not excited about doing this work. So, this work often gets sort of under invested, and that's why we exist. We find it interesting, but other people find it very boring and very hard, and just make it some into something very, very easy for loyalty programs."
“So that's sort of a lot of the context around why liability, proper liability management, is so important. You did have a question about the importance of having a lot of redemption options, and that is also really, really critical, less so for liability management, but more so for the broader success of the loyalty program. And let me explain what we always see when we look at the customer journey from an economic perspective is that redemption is a key inflection point in the customer journey.
“Whenever people redeem, we see a meaningful and often material increase in EFP. But what that means is you really want to enable people to redeem on the things that they want, right? You want to give them the option to be able to redeem on whatever is most valuable to them. And that's likely what's going to be the thing that creates that sort of emotional reaction when you get the redemption. And that's where Loylogic is extremely strong, right? And the value of what they do is they make it super easy for people to be able to have a wide range of redemption options that they can offer to their members. And so that's how and why having those redemption options is important to the success of a loyalty program."
(24:00) So my final question on the whole redemption marketplace side of things is, how can the methods that you employ ensure redemption marketplaces prevent short term counterproductive measures like currency devaluation?
Len: "This is a great, great question. When companies do that, it's, I think, a great example of not wanting to deal with building really sophisticated actuarial analytics to understand the financial foundation, and oftentimes the outcome is currency devaluation or removing popular redemption items to be annual budget targets. Forecasting redemption volumes is very difficult because programs are just so dynamic, and there's often, like, a very big lag between when the points are earned and when they'll actually be redeemed.
"What we've actually seen is programs not getting the right expertise and helping them produce accurate budgets. And as a result of that, they blow through their redemption budget for the year within just a few months. So, what do they do then? Well, they do drastic short-term things to meet their budgetary targets, right? So often that means shutting down a redemption option to reduce costs.
"The implication of that is, it's a terrible customer experience, right? You're gonna make your customers angry, which is never a good thing. And even beyond that, we know, as I spoke about earlier, redemption is always a key inflection point in the customer journey. So, you're really destroying EFP and member equity if you're not letting your customers redeem when they want to. So ultimately, you sort of get this short-term cost savings, but it comes at the cost of long-term loyalty and long-term profitability, which doesn't seem like the right trade off you ultimately want to make.
"The better solution here is just start your year with a good budget. And yes, in often cases, that means your budget for redemption costs will have to be much higher. But if you can credibly demonstrate that these redemptions drive amazing ROI, then it's absolutely a great idea to spend that money and then. So that's a big part of what we try to do with our engagements with our clients, is give them the tools to be able to articulate that and set accurate budgets so they don't run into these issues."
(26:14) Len, to wrap up the discussion, I want to just look ahead a little bit and look at how we future proof loyalty programs. So how do you envision the role of actuarial science evolving loyalty programs over the next five to 10 years, and what should businesses focus on to stay ahead?
Len: "Actuarial Science is all about long term prediction. We're predicting over long horizons so that we can make smarter business decisions today. So much about the economics of loyalty programs is centered around long-term outcomes, right? Redemption costs. Redemption on points you issue today will not happen for years in the future, right? So how are you supposed to estimate the cost at these points? Actuarial Sciences is the key.
"The value that a loyalty program creates comes from improving long-term retention, many periods into the future. So how do you quantify and prove that you're creating and finding the right levers that allow you to drive even more value? Actuarial Science is key here. If we really want to see the value that a loyalty program creates and learn the tactics that can maximize the value, I think actuarial science has to play a key role for in all loyalty programs. It's a big part of our mission to help programs around the world see that and make it easy for them to get the value of actuarial science in in their program."
(27:36) What practical advice can you provide businesses looking to integrate these approaches into their strategies?
Len: "Every single loyalty program is sitting on a goldmine of data. Every loyalty program has the data needed to be able to do what I described here today. The hard part is extracting the insights from the data. They say, you know, that data is the new oil, which is a great analogy. We're in an era where loyalty programs have basically struck oil, and now they need to build the infrastructure to extract and refine that oil, to capture the value, right? And so, you know, I think what a lot of loyalty programs can do today is really start working to set aside the budgets and make the investments in the analytics needed to really extract the value from that gold mine that they have, and start doing a lot of this, this research.
"From what I can tell and the work we've done, the ROIs are very clearly there. And most importantly, with this work, you could start actually quantifying in a very credible way what those ROIs are, so that you could turn those loyalty skeptics into loyalty advocates."
