API-first loyalty engine recognized by Deloitte & Google as a CE Tech Rocketship


Loyalty program liability - with Len Llaguno

Learn all about loyalty program liability from Len Llaguno, the Founder and Managing Partner at KYROS, the world's only actuarial firm focused solely on loyalty programs.

Picture showing blogpost author
Len Llaguno
Founder and Managing Partner at KYROS
Best ecommerce loyalty programs blogpost cover.

In our latest installment of “Ask a Loyalty Expert”, we are joined by Len Llaguno, the Founder and Managing Partner at KYROS, the world’s only actuarial firm focused solely on loyalty programs. An actuary by training, Len has taken the unique set of actuarial skills and knowledge obtained through years of rigorous study and brought it into the world of loyalty programs, where he makes highly precise long-term predictions about things such as customer lifetime value, breakage,  redemption rates, customer spend, and lots more. 

Actuarial science meets loyalty program liability

Part of what makes Len and KYROS so effective and successful is the use of actuarial science, an area of expertise that tends to slip under the radar of professionals beyond insurance. Actuarial science, however, enables predictions over the long horizon, leveraging data to make informed estimates many years into the future. This is a really critical skill for tackling problems like liability management and customer lifetime value, and one that can save businesses millions of dollars, as Len explains below.  

Predicting over the long horizon is also very different from data science. Data scientists are broadly trained around analytics and predictive models, which are typically very short term in nature, e.g. who’s going to open this email or who’s going to convert on this campaign - as opposed to what Len does at KYROS, which is making predictions such as how many points are going to get redeemed over the next 10 years. That makes it a very special class of predictions.

It’s nevertheless difficult for most actuaries to make loyalty predictions. Loyalty and insurance are very different in that the former is very fluid and dynamic. By definition, with loyalty programs, businesses are trying to change people’s behaviour, which requires models that are much more responsive to behaviour changes, and able to reflect that in the predictions. 

The big innovation that KYROS has brought to the table is figuring out how to take the best of actuarial theory and merge it with modern-day machine learning and data science, and come up with a brand-new actuarial tool-box that works really well for problem-solving in loyalty. It’s much better than the traditional actuarial methods, which were developed 50 years ago and aren’t responsive enough to what loyalty programs need, especially nowaadays. 

Below, Len tells us about the work he does, explaining in great detail the concept of loyalty program liability and customer lifetime value, as well as the very real consequences of not getting them right. We also learn more about how KYROS has been supporting businesses in getting the most out of their loyalty programs.

What is loyalty program liability? In your own words.

A liability is typically focused on the cost, but as we know, no good business decision is ever made just by looking at the cost. Customer lifetime value allows us to compare the benefit component against the cost. So, in order to get a complete financial picture, you want to be able to understand both customer lifetime value and liability

The easier way of thinking about loyalty program liability is to recognize that every time a loyalty program issues a point, that point is going to cost the company something at some point. It’s effectively an IOU that you’re issuing to your customer. And it’s worth something - if they redeem. All that liability means is that all the outstanding points that aren’t expired and haven’t been redeemed cost something - and the question is how much do these points cost? That’s the simplest way to frame what loyalty program liability is. 

The macro level concept is how much a company is going to have to pay for those points.

How do you reconcile redemption rates and engagement? After all, the higher the engagement rate, the more likely the customer will redeem their points.

This is a really good question. People talk a lot about redemption rates, which then drive engagement. However, as I mentioned earlier, no good business decision is ever made just by looking at the cost. It’s no use looking at redemption rates or the liability in a vacuum - that is a pointless exercise if you’re trying to make a business decision. 

Instead, you need to look at the cost vs. the trade-offs. That’s where customer lifetime value really comes into play. That’s actually what we’re trying to optimize. So, if your redemption costs are increasing or you’re expecting more of your points to get redeemed, because your customers are getting more engaged, that is absolutely okay, provided that your top-line revenue is increasing more than your redemption costs. In other words, that means that the profits you’re generating are going up, and the fact that your redemption costs are, too, doesn’t pose a problem.

Fundamentally, it’s that bottom-line profit that we’re trying to optimise. So, when businesses ask what’s the optimal ultimate redemption rate (URR)? Do we want the URR to increase? They’re asking the wrong question. The right question is how are we increasing customer lifetime value? And you can’t answer this question correctly unless you know your redemption costs and liability. You need both of those.

Many people argue that loyalty is a cost center and this ties in with the points you make in your article - do you agree with this statement? 

I don’t believe it’s a cost center, I believe it should be viewed as an enterprise value generator. If the loyalty program is truly doing its job, then you’re increasing customer lifetime value. You’re getting people to be more loyal and increasing purchase frequency. Ultimately, that means future profits. 

Companies are valued based on how much profit they’re going to make in the future, so if the loyalty program can increase retention, which increases future profits, then the loyalty program is generating enterprise value. That’s why the way I view loyalty programs is as enterprise value creators. Customer lifetime value enables you to look at it that way. The problem is that most people aren’t able to accurately measure customer lifetime value, and therefore can’t always value loyalty programs in that way. 

The only thing that finance is looking at is the liability. They’re only looking at that, because they have to quantify the liability and cost, per accounting regulations. There is no regulation that says you have to quantify customer lifetime value, so most people don’t. When you’re in a situation where all you’re looking at is the cost, then you’re going to lean towards viewing loyalty as a cost center. 

The trick is to reframe it and say, actually, what we should be looking at is the customer lifetime value and future profit, and how we’re growing it. We call that member equity. If more companies had the capability to look at loyalty through that lens, then I think it would be easier to see loyalty programs as enterprise value generators.

What KYROS is trying to do is bring these really difficult predictive modelling exercises and make it easy for our customers to work these things out.

Why do you think more businesses aren’t zooming in on customer lifetime value?

I think most people in the loyalty space have heard of it and understand the concept. And if you were to ask loyalty marketing professionals, then they would be open to knowing more about it and actually being able to measure it. The problem is that they don’t have a lot of experience with it, because not a lot of companies are using it in great detail. And the reason they aren’t is because it’s hard. Trying to predict how much each individual customer is going to spend and what each of them is going to do over the long haul is a real feat. Predicting what someone is going to do tomorrow is hard enough, let alone the next three or four years.

Some loyalty programs have millions of members, and creating a framework for making predictions for each of these members is very, very difficult. KYROS has built some incredible capabilities, but doing so has been a journey that I’ve been on for over a decade now (since 2012). Finding that intersection of people who have those skills - data science skills, actuarial skills, loyalty business acumen, data engineering skills, finance and accounting is extremely hard, but it’s what’s needed to be able to make accurate customer lifetime value predictions. That’s why at KYROS, we’re trying to build those capabilities and package all of that up and make it easy for loyalty programs. 

How to shift loyalty from a cost center to a profit center - what are some methods? Best practices? How can businesses ensure they don’t fall into the trap of the former?

The most important thing is being able to quantify member equity. For those who don’t know, member equity is the sum of the expected profit that your members are going to generate in the future. You want that sum to be the bottom-line profit. Obviously, there’s top-line revenue and costs of goods sold, and then because we’re talking about loyalty programs, you have to subtract redemption costs, associated with all of that generated are points, which are also going to come with a cost. Once you subtract all of the above from your top-line revenue, you end up with your gross margin. And again, you have to subtract things such as operational costs like rent, depreciation, interest, salaries and so on, before you get your net profit number.

However, you want to focus on your gross margin and be able to forecast it years into the future - and that  has  to take out redemption costs, because, again, we’re talking about loyalty programs. You can’t quantity that redemption cost component unless you really, really understand liability, ultimate redemption rates, breakage, etc. That’s why the concept of expected future profit is so intertwined with the liability.

A visusalization of how profit is generated as redemption happens.
A visusalization of how profit is generated as redemption happens. Source.

You have to quantify these things: expected future profit and then sum that up across all of your customers, which then equals member equity. That's a really powerful KPI, and you want to see the number growing. The reason that’s important is because, as I mentioned earlier, companies are valued based on how much profit they’re going to generate in the future. Member equity is a measure of how much profit is going to be generated in said future. It's a predictive and forward-looking KPI. If you’re seeing it grow, that means you’re creating enterprise value.

This is how we're able to link the loyalty programme to enterprise value creation. Instead of it just being a marketing tool, it's now an enterprise value generator. It's the bridge that gets you there. So member equity is super important to being able to make that connection.

In the article “Loyalty program liability guide” published by KYROS, you talk about how “failure to properly factor in the impact of these material financial costs  on your company's balance sheet can have an unexpected financial cost upon redemption of outstanding rewards points.” How can loyalty program owners mitigate these risks and effectively manage loyalty program liability?

The big risk here is, you make some assumption for redemption costs. For example, you say, hey, 80% of all points I issued are gonna get redeemed. And you run your business with that assumption, and that assumption is key in quantifying your redemption costs. So, the formula you use to guide you is your points issued in a month times your ultimate redemption rate times your cost per point, which then gives you the expected cost of the points you issued in a given month. Given that redemption costs are the largest expense in loyalty program business models, not getting this number right is very risky.

Let’s say you assume your redemption rate to be 80% and you run your business on that assumption for years, and then one day you realise it’s actually 90%. All of a sudden, you find that you that you’ve been running your business and accumulating all of these IOUs, effectively. And perhaps you have $100 million worth of these IOUs just sitting out there, but then you learn that, actually, it should be like $150 million instead. This lands you in a very difficult financial position, $50 million short and with a huge hole in your balance sheet. It’s not an easy situation to find yourself in and it creates a lot of financial volatility. 

That’s why it’s super important to be able to accurately estimate what the ultimate redemption rate should be, and have the mechanisms and processes in place to be able to monitor that assumption. After all, an estimate is an estimate, it’s never going to be perfect, so you need to have the capacity to monitor and update it in either direction as you learn more. Another key issue is that most companies don’t actually have a good mechanism for estimating or monitoring their ultimate redemption rate particularly effectively, and they’re not investing a lot into this function.

A lot of the time these estimates are akin to someone in finance doing a back-of-the-envelope calculation, or maybe the company outsources it to a marketing firm that does the calculation for them. These estimates are often very poorly done. They’re not actuarially sound calculations and the number can be very, very long. We've been in situations where companies that contact us have been doing it wrong for years, and they're off by $100 million, which leads to very, very dire financial situations. And they need to find that money somewhere. 

We've also seen it the opposite way as well, though, where they've been overshooting the ultimate redemption rate by 50%. And so in that case, they're just being way too conservative. They're dampening their profit and they're holding themselves back. Very rarely do we actually ever come in and find the company is doing it well, and doing it right. And, again, fundamentally, it's because most programmes don't have an actuary on staff looking at it with the right actuarial lens, and so they're doing it wrong. That's a big value proposition we can come in and, you know, bring a lot of rigour to that calculation when companies don't have the internal capabilities to do it themselves.

Do you see this changing and companies recognizing the importance of having actuarial pros on staff?

Yeah, slowly, but surely. I mean, KYROS is growing. Companies are coming to us and a lot of the conversations we have with them are very eye-opening, and we’re able to offer them a unique perspective. A lot of our work is focused on helping them understand liability, and why the ultimate redemption rate is moving a certain way, but also helping them solve their business problems. We bring a vastly different perspective; a long term perspective around customer lifetime value. And that's super helpful when it comes to thinking about programme strategy, design and that sort of thing. So, I do believe that companies are starting to appreciate it, but I still think we're on the fringes. I would love to, in my career, help make actuary science a more common thing in the loyalty world. 

However, the biggest problem is that most companies don’t realise they’re doing it wrong. Liability is not exactly the most exciting part of loyalty programs. Frankly, it’s probably the most boring one; and so, the people running these programs don’t really want to focus their time, energy and resources on it, and I don’t blame them. They want to focus on things such as engaging with their customers, coming up with incentives and benefits, and so on. 

While that’s absolutely the right place they should concentrate on, the potential problems brewing on the financial side of a loyalty program that they may not be aware of shouldn’t be ignored either. I think that basic education needs to happen in the industry around the consequences of miscalculating breakage analytics, liability analytics and member equity analytics. This is also something that KYROS is trying to achieve. As such, lots of educational content, videos and ebooks can be found on our website, which is easily accessible to anyone who might want to learn more about the subject.

KYROS Academy courses.
KYROS Academy courses. Source.


To learn more about the things discussed in this piece, check out the KYROS website, where you’ll find a range of resources as well as KYROS Academy courses.