Category Archives: Loyalty Finance

An Inside Look at Loyalty Program Benchmarks for the Hospitality Industry

When analyzing your loyalty program strategy, knowing the financial benchmarks against which you should be comparing the success of your program is vital.


Unfortunately, such competitive information is not readily available without extensive research or an expensive team of consultants.


In an effort to uncover valuable loyalty program insight for our readers, we’ve taken it upon ourselves to assess the competitive landscape of some of today’s largest loyalty programs.  Our goal: establish key loyalty program benchmarks across industries where loyalty programs play an indisputable role in generating significant revenue — namely, hospitality, frequent flyer programs and finance.  


As part of our research, we’ve selected several leading companies within each industry, carefully examining the following:

  • Loyalty program liability
  • Loyalty program point expiration
  • Loyalty program membership
  • Loyalty program revenue
  • Implementation of ASC 606 & IFRS 15
  • Program liability sensitivity


We start this series by taking an inside look at the hospitality industry – arguably one of the largest sectors of the loyalty program industry. Companies examined include:


  • Marriott International
  • Hilton Hotels
  • Hyatt Hotels
  • IHG
  • Wyndham Hotels
  • Choice Hotels
  • AccorHotels
  • Best Western International


We hope these insights prove valuable in guiding you towards better decision making for your loyalty program.



Loyalty program liability (a quick recap)


Loyalty programs are a tactic for increasing customer engagement, ultimately generating additional sources of revenue and profitability for a company.


However, establishing and managing these programs also generates new costs. The costs associated with future redemptions make up the loyalty program liability.  


Actuarial analysis can forecast your expected liabilities, redemption patterns, and ultimate breakage. This is key to prevent having too much revenue locked up in deferred revenue or liabilities.


Within the hospitality industry, loyalty program liabilities are typically recognized as a current or noncurrent liability. All in, these accounts represent the fair value of anticipated redemptions associated with loyalty program points issued to date.


Total revenue vs. loyalty program deferred revenue liabilities, 2017

CompanyTotal RevenueLoyalty Program Liability (Deferred Revenue)*
Marriott$22,894 million$4,940 million
Hilton$9,140 million$1,461 million
Hyatt$4,685 million$561 million
IHG$1,784 million$1,066 million
Wyndham$1,347 million$90 million
Choice$1,007 million$128 million
Best Western$380 million$119 million

*Restated to reflect ASC 606/IFRS 15 where necessary


These deferred revenue liabilities represent the potential performance obligations that may need to be satisfied in the future. While on a cash basis, the company may have already collected on these obligations, to properly account for loyalty program revenue, revenue is not recognized until these obligations are performed (i.e., the points are redeemed).


Generally, the larger the hospitality company, the larger their program liability. There are a few noteworthy exceptions in IHG and Best Western, where we see lower total revenue (comparatively speaking) with a significant percentage tied up in program liability.  


These liabilities represent a massive investment in members and, therefore, a huge opportunity to drive value for the organization. At this scale, even a small 1% improvement in the ROI on these liabilities could drive tens of millions of dollars of value creation.  For example, with Marriott’s $4.9 billion liability, a 1% improvement in the ROI would drive $49 million of value creation.


Of further interest is the split between current and non-current liabilities.


Current vs non-current liabilities for loyalty programs 2017

CompanyCurrent liabilityNon-current liability
Best Western33%67%


On average, a significant piece of these performance obligations is expected to be redeemed after 12 months (the general definition of current vs. non-current liability). Therefore, accurate breakage and ultimate redemption rate forecasting is essential, otherwise current obligations may be underserved.


The table above is ordered from largest to smallest based on revenue, and the loyalty programs of larger companies tend to have a higher proportion of non-current liabilities. This suggests as a program grows, redemption behavior is drawn out over a longer time period.



Loyalty program point expiration

Incorporating an expiration rule into your loyalty program can incentivize members to maintain a regular cadence of activity in your program


The introduction of a new expiration rule many increase the uncertainty of liability and breakage estimates. However, as program expiration rules mature, member behavior can potentially be better predicted.


Point expiration varies by industry. It is common for credit card and frequent flyer points to be issued without an expiration rule. The hospitality industry, on the other hand, almost universally incentivizes more frequent usage.


Expiration policies by hotel, 2017

CompanyRequired activity period
Marriott24 months
Hilton12 months
Hyatt24 months
IHG12 months
Wyndham18 months*
Choice18 months
AccorHotels12 months
Best WesternNone

*In addition to the activity-based expiration rule, Wyndham points expire 4 years after issuance


Hospitality expiration policies are generally based on activity, rather than the period of time since the  points were originally earned. They typically require a minimum base activity in order to maintain points and eligibility. This can be as little as using 1 point every 12 months.


Hospitality program expiration rules are generally straightforward, with few caveats. They simply encourage regular participation, while not overly penalizing low activity.


Taking a look at the table above, about half of all programs cap their expiration rule at 12 months, with the other half expiring after 18 to 24 months.


We recently saw the impact of adding an expiration policy when IHG implemented a 12-month expiration policy back in April 2015. Upon recommendation from an external actuary, the implementation resulted in the releasing of $156 million from IHG’s loyalty program liability.


Ultimately, the decision on the ideal expiration policy for your program should be based on customer lifetime value (CLV); having a lax expiration policy can be well worth the investment if it results in better engagement and a net increase in CLV.



Loyalty program membership


Strong loyalty program membership can be analogous to brand clout. But it’s not necessarily indicative of a highly functioning program.


Loyalty program members vs total revenue as of 2017

CompanyMembersTotal 2017 Revenue
Marriott110 million$22,894 million
Hilton71 million$9,140 million
Hyatt10 million$4,685 million
AccorHotels41 million$2,099 million
IHG100 million +$1,784 million
Wyndham68 million$1,347 million
Choice35 million$1,007 million
Best Western33 million$380 million


It’s no surprise that loyalty program membership correlates with total revenue. Larger hospitality companies command greater market share; they simply have a larger pool from which to recruit loyalty program members.


Take a closer look at the numbers, however, and you’ll notice that not every chain is capitalizing on the opportunity. With only 10 million members, Hyatt appears to be underutilizing its loyalty program, when stacked against its peers. Increasing program participation appears to be an opportunity to drive revenue growth.


Contrarily, Best Western appears to have a very active program. Total membership exceeds or equals that of its larger peers. This may be indicative of high member engagement, or perhaps, easy program enrollment.



Loyalty program revenue

You’ve heard it again and again: loyalty programs drive customer engagement, leading to increased revenue and more profitable growth.


Our analysis confirms this.


Loyalty program member contribution, 2017

CompanyLoyalty member contribution
Hilton57% of room nights
Marriott50% of room nights
IHG43% of room revenue
Wyndham~33% of room nights
Hyatt30% of room nights
Best Western44% of room revenue


The companies in this table are listed by 2017 revenue, from high to low. Arranged this way, we start to see a trend: loyalty program utilization appears to correlate with higher company revenue.


This supports the hypothesis that loyalty programs can be a major source of untapped revenue for a hospitality company. Hotels like Hyatt and Wyndham may be able to drive stronger sales growth by improving their loyalty programs.


As a result, opportunity can be uncovered in the analysis of multi-year sales growth and member loyalty program utilization. The analysis could confirm a multi-year trend and provide direct evidence for the top and bottom-line improvements generated by loyalty programs.



Implementing ASC 606 & IFRS 15


ASC 606 was made effective for public entities in December 2017, resulting in significant changes for loyalty programs. Its counterpart, IFRS 15, drove changes internationally.


Compliance is now required for all public entities.


The ASC 606 / IFRS 15 implementation drove several key changes:


  • Removed inconsistencies from reporting
  • Improved the revenue recognition framework
  • Improved the comparability of revenue recognition practices across entities
  • Provided more useful financial statement information
  • Simplified financial statement preparation


Before the implementation of these new standards, companies employed two primary methodologies  of loyalty program accounting: the incremental cost model and the deferred revenue (i.e., “multiple-element”) model.  


With the change, all companies are now required to report liability using the deferred revenue model, and separate the initial transaction from the subsequent transactions, as loyalty points are redeemed.


Limited impact with ASC 606

Many of the US GAAP companies examined in this analysis had already been following a deferred revenue model, and therefore had little to no change in reporting. But in a few cases, companies saw an increase in deferred revenue accounts.


Best Western, a privately held company, has until December 2018 to adjust to the new accounting provisions. The company currently follows an incremental cost model, which will be rendered invalid by ASC 606. As is,  Best Western has not shared plans for the transition to the new accounting standards.


Net liabilities increase for some companies with IFRS 15

The updates required by IFRS 15 were less significant than those required by GAAP. However, this required adjustments with two of the examined hospitality companies.


Prior to IFRS 15, IHG accrued revenue as soon as customers earned points, and a liability was established to cover the cost of redemptions (the reimbursement IHG would pay to the hotel owners). After IFRS 15, they now defer revenue until points are redeemed, based on the standalone selling price of the value of those points to the member. The result has been a net increase in IHG’s program liabilities.


Similarly, AccorHotels now views its performance obligation to be unsatisfied until points are redeemed or expire. As a result, revenue associated with their loyalty program is deferred in an amount that reflects the standalone selling price of the future benefit to the member. The accounting change has driven an increase in deferred revenue and, similar to IHG, a net increase in AccorHotels’ liabilities.


The increase may be the result of the new fair value requirement. Previously, loyalty programs may have deferred revenue at fair value to the hotel owner. With IFRS 15, hotels must now defer revenue at fair value to the customer.


For example, let’s say the fair value to a customer of a free night’s stay at a hotel is $100. The hospitality company has agreed to reimburse the hotel owner $70 for a free night (often, the hotel is not fully booked, so this room likely wouldn’t have rented anyways). Before IFRS 15, the hospitality company would have a liability for the $70 they would owe the hotel owner upon redemption. Now, however, they must defer $100, and can only recognize the remaining $30 when the free night is redeemed.



Program liability sensitivity

Among loyalty programs, a major point of contention is breakage. Breakage refers to the loyalty rewards points that go unredeemed, and therefore become booked as revenue without a corresponding expense.


A company that estimates too much breakage will not defer enough revenue, while a company that forecasts too little breakage will defer too much revenue.


Should an overestimate in breakage occur, the resulting revised financial statements could show a material hit to retained earnings. Conversely, deferring too much revenue will result in “stuck revenue,” and tie up valuable funds. To avoid these reporting adjustments, it’s well worth investing in accurate breakage forecasting.


While disclosing breakage isn’t a reporting requirement, it’s an essential component of calculating reliable loyalty program liability.


You won’t find companies sharing their breakage estimates, but many do report breakage sensitivities. You can gather some directional evidence and benchmarking from how breakage estimates impact the the balance sheet:


CompanySensitivityTotal Loyalty Program Liability
Marriott10% decrease in breakage increases liability by $269 million$4,940 million
IHG1% decrease in breakage increases liability by $10 million$760 million
Hyatt10% decrease in breakage increases liability by $30 million$561 million


Generally speaking, a 10% decrease in breakage estimates can drive a 5-15% change in program liability.


The consequences of having to make a 10% adjustment in breakage could transform a company’s balance sheet. Solvency ratios, liquidity requirements, deferred revenue forecasts, and expense estimates may all be affected.  


Make sure you have a robust framework for estimating breakage.  



Applying these insights to your program


Customer loyalty programs are not new. Still, as you explore the world of numerical analysis, you’ll find that limited benchmarking data is available.


The hospitality industry is no exception.


Our 2017 analysis included top companies.  Future analysis may expand beyond this list.  


  • Marriott International
  • Hilton Hotels
  • Hyatt Hotels
  • IHG
  • Wyndham Hotels
  • Choice Hotels
  • AccorHotels
  • Best Western International


Compare yourself to the competition (with a grain of salt). While the exact magnitude may vary, the basic rules and trends remain. And start by asking the right questions:


What is our ideal breakage rate?

Does our program liability make sense?

Does our program expiration rule help or hurt us?

How does our loyalty program membership compare to that of our competition?

Do our loyalty members drive top and bottom line growth?

Is our loyalty program accounting accurate?

Are we conducting sensitivity analysis on our breakage rates?


If you don’t have the answer to all these questions, that’s okay. Knowing which questions to ask, along with the right benchmarks, is the first step.




Looking to maximize the economic value of your loyalty program? Contact us  for a free consultation.


3 Things Every CFO Should Know About Loyalty Program Liability

As the captain at the helm of your company’s finances, it’s critical that you chart a course that steers clear of dangerous liabilities. Not all liabilities, however, can be averted – in fact, some are part and parcel for increasing your company’s bottom line.


One such necessary hazard is loyalty program liability. This liability arises from the costs incurred at the moment that a loyalty program member redeems some or all of their outstanding points. It’s a by-product of energized customer engagement. However, with high customer engagement leading to a 90% uptick in purchase frequency and 60% higher spend per transaction, it’s not a liability with which companies can afford to dispense.


New regulations have changed the way that accounting teams must allocate revenue to manage loyalty program liability. The principal tenet to which they will have to adhere is that separate accounting will need to be carried out for every performance obligation, and transactions with multiple performance obligations will require that the revenue for each one be logged individually. Most importantly, revenue from the issuance of loyalty points must be deferred and cannot be recognized until either the reward is redeemed, or the customer’s claim to the reward expires.


In the event that your colleagues in accounting fail to defer the correct amount of revenue relative to the scale of the liability, it can send your financial reports into a tailspin. Miscalculations that underestimate the amount of revenue necessary can lead to you not having enough to cover the incoming flurry of costs. Conversely, setting aside too much revenue can relegate funds to a state of financial suspended animation known as “stuck revenue.”


Read on to discover what every CFO needs to know about loyalty program liability.



1. These liabilities carry financial impact

Customer Loyalty


The financial consequences of loyalty program reward points aren’t felt just at the moment of redemption, but also at the moment at which they’re issued. Once rewards points are granted to program members, the business comes into ownership of the associated costs that accompany points that can be redeemed. Depending on the method of accounting employed by your program, these may manifest either in the form of an immediate expense recognition or as a revenue reduction.


Though accounting departments may fixate on current liability levels, the role of finance teams is to know how liability will develop over time. In order to accurately forecast liability levels, the finance department must be equipped with a robust understanding of the cost per point (CPP) and ultimate redemption rate (URR).


As the URR changes over time, failures to incorporate its fluctuations in your company’s financial planning could cause material impact to its financial outcomes. Similarly, because URR and CPP are the primary determinants of loyalty program liability, overlooking them in the development of your financial strategy could lead to the liability corroding your bottom line.


It’s important to avoid the pitfalls that finance leaders encounter when trying to reduce the imprint of loyalty program liability upon their income statements. One of the most common mistakes finance teams make is trying to reduce liability by driving up breakage. Breakage, or the percent of points a customer earns but does not redeem, can reduce the the strain induced by loyalty program liability. However, what it amounts to are customers opting to disengage from your company and the financial consequences that accompany the drop-off in consumer interest.



2. As your company gains loyal customers, breakage rates will decrease

brand loyalty


The purpose of a loyalty program is to incentivize customers to go to your company first for all of their needs related to your product line. The way it accomplishes this is by rewarding them for their business, and adding in a new variable to consider in their calculations. However, for these rewards to have value to customers, they must be worth something, and the final cost of providing them is where companies incur loyalty program liability.


As a result, the more engaged and loyal customers a company has, the higher its liability estimates will be. Though it might seem counterintuitive, bringing up breakage rates can take a massive toll on a company’s finances. With 20% of customers driving 80% of redemptions, increasing breakage can cause the purchasing frequency of some of the company’s most prolific shoppers to wilt.


Disengaging from the strongest segment of your company’s customer base will only lead to significantly-diminished returns in the long-term.


Instead, a more salient approach is to keep breakage moving down, while using an influx of new customers to offset its impact.


Another tactic is to work alongside marketing teams to find ways of driving down CPP. In this way, liability can be decreased without sacrificing desirable customer engagement.



3. Customer lifetime value (CLV) lets you know what you’re getting out of holding on to the liability


When it comes to loyalty program liability, CFOs should always ask themselves, “Is the juice worth the squeeze?” One useful metric for correctly answering this question is customer lifetime value (CLV). CLV takes into consideration costs borne from redemptions, as well as the revenue generated by customers throughout the course of their engagement with the company.


One particularly important element upon which CLV focuses is revenue. In contrast to liability, projected future revenue isn’t something you can add to a balance sheet – and that’s why there’s traditionally such an emphasis on cost. However, the goal of a liability program is to increase revenue, so that is a component that should not be left out.


By looking at CLV, CFOs can better contextualize their redemption rates, and better decide how to proceed appropriately. Simply stated, without a strong understanding of CLV, it’s impossible to adequately assess the health and viability of your loyalty program.



The bottom line

Loyalty program liability can have material consequences upon a company’s financial standing. However, CFOs should resist the urge to simply drive down redemption rates, and instead, employ holistic metrics such as CLV to inform their long-term strategies.


With careful attention paid to forecasts of customer behavior, finance teams can draft a blueprint guaranteed to keep the company in the black.




KYROS provides sophisticated predictive analytics solutions that help companies optimize the financial performance of their loyalty program. Want to maximize the economic value of your program?  Contact us  for a free consultation.


Measuring Customer Loyalty: Must-Have Financial Metrics to Calculate Loyalty Program ROI

A/B testing is often the tool of choice for measuring the effectiveness of individual marketing campaigns.


One such example of this in practice might unfold as follows: Seeking to increase sales of a particular product, a company sends one group of customers (known as Group A) a specific offer. Then, they compare how the purchasing patterns of Group A change relative to those of a different group of customers, Group B, that did not receive the offer.


The difference in revenue between these two groups is the return on investment (ROI). This type of measurement is most effective at points when there is a clear closing date for the campaign and the results are clearly and neatly juxtaposed against a baseline.


However, for loyalty programs, actually measuring customer loyalty and determining loyalty program ROI can be more challenging, as there is no clearly defined end in sight. Instead, they extend in perpetuity (or at least for the lifetime of the member in the program). In these types of scenarios, companies find themselves needing to influence behaviors over an indefinite period of time, rather than aiming to simply cause one particular response at a given moment.

The crux of the problem with measuring loyalty program ROI is that it’s hard to predict consumer behavior over the long term. In order for companies to adequately gauge ROI, they need to correctly forecast how the loyalty program will impact the frequency of desired customer behaviors in the future, and not just rely on historical data.


So, what exactly should loyalty teams be measuring when determining loyalty program ROI? Let’s take a look.


Look-alike analyses look at the wrong things

A commonly-used method for understanding incremental lift, the “look-alike analysis” is vulnerable to self-selection bias and informational blind spots. However, there are other approaches that produce superior results.

Look-alike analyses


In order for companies to rate the efficacy of a loyalty program, they have to be able to chart out its effect on consumer behavior. As mentioned earlier, it’s important to note that I’m referring to future behavior, not past performance. While prior behavior can provide valuable historical context, the scope of its use is rather limited.

One example of the limitations seen in these “look-alike” analyses (of which I’ve observed many companies employ to quantify the incremental lift created by customer loyalty programs) is the blind spots in behavioral profiles which result. Incremental lift, of course, refers to the increased amount of spending in which a member engages above the quantity that they would have leveled-off at in the absence of the loyalty program.


A look-alike analysis works in the following way:

Customers are grouped based on the characteristics they display when they first join the program. One such group, for example, might comprise 40 middle-aged men that joined the program with the company through Channel Z.


This group can then be divided into two distinct subsets: those that choose to sign up for the loyalty program, and those that do not. The company then follows these customers throughout the course of a predetermined period of time and compares the volume of spending between the two sub-groups. The estimate of the incremental lift generated by the loyalty program can be found by looking at how much more the group of people enrolled spent compared to those that were not enrolled.


This approach is beset by two significant problems, however. The first problem is the challenge inherent in tracking customers who are not enrolled in the program. For most programs, customers who aren’t enrolled simply cannot have their behavior monitored. The second issue is the self-selection bias that accompanies this method — that is, customers that choose to join are inherently more likely to continue spending with a company (otherwise, why would they join?).

I’ve seen this self-selection bias express itself powerfully, resulting in unrealistic and unviable estimations of incremental lift. In fact, I’ve even experienced some companies abandoning this strategy outright because the answers it provided seemed so unreliable.


There are, however, better ways of assessing the utility of loyalty programs. In particular, predictive “future value” metrics are very useful. These are metrics that predict the future value of each member. My three favorite predictive future value metrics are as follows:


Customer lifetime value (CLV)

Customer lifetime value (CLV) calculates the figure for how much free cash flow any individual member creates over the course of their lifetime within the program. For many loyalty programs, CLV could be determined using this formula:


CLV is most valuable when you can estimate it at the individual member level. You can then aggregate this information based on the channel in which the member joined to easily discover the value generated from each channel. Assuming the amount is both large and positive, then you can assume that a particular channel of acquisition is effective and reliable (and breathe a sigh of relief).


Moreover, a CLV that gets larger as time progresses is a fantastic indicator of performance when evaluating whether or not a loyalty program is succeeding at bringing about an increase in customer engagement.


Customer future value (CFV)

With this metric, we measure the expected future free cash flow produced by each member. As you may have guessed, it focuses on future member behavior.

Customer future value equation


Though, superficially, this metric resembles CLV, it restricts its focus to upcoming free cash flow. Previously-generated free cash flow is not integrated into the analysis. This approach is great for evaluating enrollees during their lifetime with a loyalty program (in contrast to at the moment of acquisition) due to the fact that it concerns itself with a future value that can be influenced. For healthy programs, this metric should be large and show an upwards momentum (though, it will probably bump into an upper limit, depending on your business model’s parameters).

CFV should be estimated for each individual member. This enables the identification of members with the most significant future value, and turns the focus of targeted marketing on them. In this way, your company will reduce the amount of investment wasted on courting members that don’t produce returns, ultimately driving an increase in long term financial value

Customer potential value (CPV)

Though CFV is useful for targeting and identifying which members hold the most future value, it’s also important to identify which enrollees have the potential to convert into high CFV customers if properly incentivized. These individuals are called “high potential members,” a name that distinguishes them from their “high value” counterparts.


You can measure this by zeroing-in on the marginal change in future value for additional earned points. Members with high potential are those whose CFV goes up with every new point they accrue. Those with a positive CPV generate positive value every time they earn a new point, whereas those with negative CPV values cost more than what they bring in every time they tally up another point.


CPV is helpful for optimizing a loyalty program’s financial value. If we recall from entry-level economics, profit reaches its peak when marginal cost and marginal benefit are equal (or when the value of marginal cost subtracted from marginal benefit equals zero). This signifies that optimal levels of long-term profit are attained by a loyalty program that has a CPV of zero. Though a CPV of zero may not be practical, an understanding of CPV is helpful for advancing your company closer to a theoretically optimal position.


Once again, if your company has an estimate of individual-member-level CPV, it will be able to identify members with the highest potential and aim its targeted marketing towards them. Similarly to properly estimating CFV, doing so will bring down the marketing waste incurred by engaging members that don’t create returns, and drive long-term financial value.


Bottom line: While a bifurcated, “look-alike” analysis leaves companies with blind spots in both their predictive scope and ability to capture ongoing customer behavior, there are other approaches that give them the information they need to evaluate and optimize the value of their loyalty programs.

Customer lifetime value (CLV) informs a company of how much a member will generate over the course of their lifetime in the program, including value already created. Customer future value (CFV) addresses the future value a member will bring in, which excludes value already created. Customer potential value (CPV), in turn, tells companies which members are likely to convert into high-value contributors. Knowing these figures allows companies to reduce how much money is wasted on engagements with little-to-no returns.


When measuring customer loyalty, use predictive “future value” metrics or lose revenue

It’s important to focus your company’s investigative efforts on predicting value at the individual member level in order to not miss out on opportunities to increase revenue.

Future value metrics


It’s shocking how frequently these predictive future value metrics are not developed or taken advantage of.

It seems that a large percentage of companies fall short in their efforts to correctly prioritize CLV because of the obstacles inherent to predicting these numbers.

To correctly predict CLV, a company must able to predict behaviors (e.g., buying patterns by type, volume and frequency of purchases, how often a member redeems) at the individual level, and, more importantly, correctly anticipate how they’ll evolve far into the future. Long term prediction is, of course, very difficult.

However, once you’re able to surmount these formidable obstacles, you’ll find your company in a very favorable financial standing. Not only will your company have a financial model that  clearly details how much your company can invest per member without sacrificing economic viability, you’ll also be able to group together members in a precise, strategic fashion that increases the effectiveness of your targeting efforts.


A number of loyalty programs use CLV metrics, but they tend to suffer in one key dimension: redemption cost.


Frequently, these CLV models incorporate redemption cost assumptions based on the breakage models used for financial reporting of the liability. Typically, this breakage assumption is a single aggregate number for the entire program. The CLV models apply this aggregate breakage expectations uniformly across all members.


Of course, this is not a realistic assumption, since there are marked differences in breakage between members. In fact, a commonly-touted statistic is that only a meager 20% of members are responsible for 80% of redemptions. This indicates that employing an assumption hinged on aggregate breakage figures will not accurately reflect the behavior of most members.


Instead, such an assumption will depress CLV and overstate costs for the majority of your members. As a result, a significant portion of your members will have their estimated future value diluted, distorting the economic picture and leading to missed opportunities.


Consequently, the first step towards developing these metrics is to thoroughly understand loyalty program liability. As I stated earlier, the most important driver of the cost component of CLV, CFV and CPV for loyalty programs is the cost of redemptions. Companies need to know how to properly estimate ultimate redemption rate (URR), liability, and cost-per-point (CPP) at the individual member level to get started.


Bottom line: In order to get the most out of these predictive future value metrics, you have to use them correctly — and precisely.  Using aggregate rates of breakage will almost certainly misrepresent the value of each customer and produce inaccurate conclusions. This leads to companies missing out on opportunities for unlocking potential revenue by engaging members who actually are, or could be, converted to high future value members.

Instead, companies must have a well-defined understanding of their breakage rates at the individual member level in order to produce predictive valuation levels that steer your business towards optimized member engagement.


In conclusion

If you want to know which customers to engage, having accurate predictive “future value” metrics to evaluate the lifetime, future and potential values of individual members is essential. It’s important to derive these metrics at the individual member level so members can be differentiated and prioritized.

When measuring customer loyalty, the biggest metric companies fail to properly estimate is the cost of redemptions. Companies that use aggregate cost assumptions in their predictive value models can find themselves overestimating redemption, therefore drawing distorted financial assumptions for most of their members. This results in missed opportunities to drive value. If you were to multiply this by the entire membership of a loyalty program, these missed opportunities can culminate in millions of dollars worth of atrophied growth.

Correctly mining data provided by customer behavior is the most salient method for companies to get the individualized predictions of behavior and capitalize on the chance for increased value. Make sure to read my next piece to discover valuable insights on how to best project the future actions of customers at the individual member level.



KYROS provides sophisticated predictive analytics solutions that help companies optimize the financial performance of their loyalty program. Want to maximize the economic value of your program? Contact us for a free consultation.


Loyalty Program Finance: The Struggle for Progress

In my previous articles, we talked about some of the struggles loyalty marketers and accountants face when booking loyalty program liabilities.

Now we turn our attention to loyalty finance professionals, who face significant pressure to ensure accurate liability and solid loyalty program ROI.

Unfortunately, there are not many resources for finance professionals that support loyalty programs. This makes a loyalty finance professional’s struggle for progress very challenging.

In this article, we’ll look closer at the progress loyalty finance professionals aim for, along with the obstacles often face.

Read more »

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