Your 9-Step Checklist for Loyalty Program Financial Reporting

Year-end means a lot of things for many people. Holiday music, ice skating, pumpkin spiced lattes.


For accountants? The fun starts after closing the year-end books.


With the recent changes in loyalty program revenue recognition standards, this process has come under fresh scrutiny. Loyalty program accountants must now incorporate the new reporting standards ASC 606 and IFRS 15, while still optimizing their financial statements for profitability.


Rather than get lost in the details (as it’s all too easy to do), we’ve compiled a quick 9-step checklist for loyalty program financial reporting. As you enter the holiday season, we hope this will speed up your processes and bring better financial insight to your program.


Step 1: Validate data

Accuracy is everything in accounting.


Naturally, the first step is to ensure your records are accurate and correct. Good data is essential.


Each accounting period should be checked to ensure that individual transactional data matches with the total outstanding point balance used in the liability calculation. Underlying data for breakage estimates must match with data used for the books.


In general, prior period trends should also be consistent. Examine the number of points earned, points redeemed, and points expired each month. Month-to-month point volumes should appear similar when compared to historical years, notwithstanding some exceptions.


A few exceptions to watch out for include:

  • Flash promotions increasing redemptions
  • Temporary grace periods decreasing point expiration
  • New expiration rule increasing point expiration


Step 2: Allocate redemptions to points earned

Accurate breakage estimates require correctly allocating redemptions to actual point earnings.


Loyalty program point allocation has similarities to inventory valuation. As customers use their points, redemption costs should be allocated to specific point earnings. Much like when physical inventory is sold, revenue and costs of goods sold is applied for each unit sold.


The industry standard for loyalty programs is First in First Out (FIFO). Under FIFO, point redemptions are tied to the member’s earliest earned points (first in) that remain outstanding.


Unlike inventory valuation, there is no required method for point allocation. Some programs might find using FIFO isn’t realistic or advantageous:

  • Programs with expiration rules varying by point
  • Programs whose members choose which points to redeem


Regardless of which method you follow, a clean system will tie redemptions to specific point earnings.


Step 3: Update breakage and fair value estimates

update breakage estimates

Breakage lies front and center for loyalty program accounting. Your program is optimized for customer engagement and increasing revenue. Breakage is the variable that balances these two goals.


Breakage drives most of the financial strategy for a loyalty program. With the new standards, it’s more important than ever to land on an accurate estimate.


Updating breakage estimates

Breakage is the percentage of outstanding points that will ultimately go unredeemed. This estimate is used to calculate loyalty program liability.

Breakage = % of points that go unredeemed

Loyalty Program Liability = Outstanding points x (1 – Breakage) x CPP

Cost Per Point (CPP) = Expected cost of each point that will be redeemed

Revenue Recognition Rate = Deferred revenue / future points expected to be redeemed


Breakage estimates are updated with each new period, as new customer data rolls in.


For example:

Last Period:

Deferred Revenue Balance = $900

Points Ultimately Expected to be Redeemed = 1,200

Revenue Recognition Rate = $900 / 1,200 points = $0.75

Points Redeemed in Period = 200

Revenue Recognized in Period = 200 x $0.75 = $150


Next Period:

New Deferred Revenue Balance = $900 – $150 = $750

Remaining Points Expected to be Redeemed Based on Prior Breakage Estimate = 1,200 – 200 = 1,000

Remaining Points Expected to be Redeemed Based on Updated Breakage Estimate = 950

Updated Revenue Recognition Rate = $750 / 950 points = $0.79


Because we now expect fewer points to be redeemed (higher breakage), the revenue recognized with each future point redeemed increases.


The dangers of inaccurate breakage estimates

In an ideal world, your breakage estimate is spot on. As members redeem their points, deferred revenue is recognized and your accounts balance out.


In reality, estimates require continual adjustment. However, if you significantly over or underestimate breakage, significant revisions will be required:

  • Breakage estimate too high and your deferred revenue will be too low, requiring a one-time (potentially) material increase in program liability;
  • Breakage estimate too low and your deferred revenue will be too high, resulting in stuck revenue.


Accurate breakage estimates require predictive actuarial models

The consequences of inaccurate breakage estimates can be avoided with robust actuarial modeling.


Actuaries excel at estimating program liabilities from uncertain cash flows and consumer behavior.


However, traditional actuarial science methods, such as those used in insurance, are not ideal for loyalty program applications; the loyalty program industry is far more dynamic and fluid than the insurance industry.


Predictive analytics and modeling is the best solution for estimating accurate breakage rates. These advanced actuarial models can calculate breakage estimates down to the individual member level.


Fair value per point estimates

Fair value per point (FVPP) is a component of calculating deferred revenue for a loyalty program. FVPP is a dynamic measure that can change with each period.


A number of variables can change FVPP:

  • Changes in utilization distribution
  • Changes in award charts
  • Temporary promotional rewards
  • Changes in foreign exchange rates
  • Inflation
  • Seasonality in standalone value of rewards (i.e. flights, hotel nights)


Assess actual member behavior versus forecasts

Models and estimates are only worthwhile if they are accurate. Each period, assess actual results to your forecasted values for breakage, FVPP, and redemption patterns.


Ask yourself questions such as:

  • How did the volume of points redeemed compare to our model predictions?
  • Did members redeemed their points on the award types our model predicted?


If your answers aren’t quite what you’d hoped, you’ll need to assess and adjust.


In analyzing your model performance, you’ll be able to identify what’s driving changes in your estimates. This is essential to communicate a clear story to your CFO and auditors. A member-level model is particularly useful for constructing a clear narrative.


Step 4: Record accounting entries for reporting periods

record corresponding accounting activity

Each financial reporting period will require updating key loyalty program accounts. The following items will change and require the corresponding accounting activity:

  • Points earned
  • Points redeemed
  • Points expired
  • Updated breakage estimates

Points earned

Any new points earned will require an associated entry to deferred revenue. The value should be based on the relative standalone selling value of the points.


The amount of deferred revenue recognized should incorporate your latest breakage estimate.


For example, if breakage = 10%, each redeemable dollar should result in deferring 90 cents.


Points redeemed

Points redeemed during the reporting period will result in recognizing new revenue and reducing deferred revenue liability.


The revenue recognition rate represents the proportion of deferred revenue realized per every redeemable point.


Let’s take a closer look:


Beginning of the Period:

Deferred Revenue Balance = $900

Points Ultimately Expected to be Redeemed = 1,200

Revenue Recognition Rate = $900 / 1,200 points = 0.75


During the Period:

Points Redeemed =200

Deferred Revenue Recognized = 200 x 0.75 =$150


Points expired

Points expired during the period require no adjustment, as long as the deferred revenue account incorporates point expiration estimates.  


Final adjustments

This list is by no means exhaustive. A few noteworthy additional items to adjust include:

  • Adjust deferred revenue balances with latest foreign exchange rates
  • Breakage estimates (with associated updates to the deferred revenue account)


Member-level predictive analytic models help quantify the magnitude of exchange rate risk, and aggregate results up to a total currency level.


Step 5: Seek an actuarial opinion

Actuarial opinion to certify accuracy

A company’s CFO and auditors are required to certify the accuracy of company financial reporting statements. Given the financial statement ramifications of an inaccurate breakage estimate, actuarial opinions are sought to justify loyalty program liability.


An actuarial opinion is signed by a credentialed actuary as proof of thorough review and vetting of loyalty program liability assumptions, and provides the following:

  • Support for booked breakage estimate to auditors
  • Peace of mind for the CFO (booked liability is materially significant to financial statements)
  • Protection against key material risk factors (inaccurate estimates of loyalty liability or breakage)


The opinion provided will usually be presented as a range of reasonable estimates, any of which are actuarial justified for booking purposes. Often, actuaries will present their analysis to the CFO and auditors verbally, in addition to a physical document.

Step 6: Prepare financial disclosures

financial disclosure reporting

There’s already a long list of required disclosures for loyalty programs. After ASC 606 and IFRS 15, the list has only gotten longer.

Required disclosures Include:

  • Liability change during the year
  • How much revenue recognized in the period is included within opening liability balance
  • When the company satisfies its performance obligations (e.g., when the reward is used)
  • Nature of the goods and services the company has promised to transfer
  • Estimate of the timing to satisfy the liability
  • Significant judgments
  • Method used to determine transaction price and allocate it to performance obligations


A deeper dive (and examples) into the financial reporting requirements is available here.


Step 7: Hedge exposure (optional)

An optional but prudent step is to consider your foreign exchange exposure. If your liability is significantly exposed to foreign exchange risk, your modeling and liability estimate becomes much more complicated.


An effective currency risk management strategy involving forward contracts can minimize and hedge your exposure to foreign exchange fluctuation risks.


Step 8: Assess progress against your financial plan for the year

While Steps 2 through 7 account only for the loyalty program liability, there are additional transactions and measures to track each month. These include:


Cash Flow Implications

  • Points earned bring in revenue
  • Points redeemed carry a cost

Forecasting Implications:

  • Forecast liability and cash flow
  • Anticipate year-end financial position
  • Determine budget availability
  • Plan for the medium- to long-term horizon


Comparing actual transactions and breakage estimates to forecasts will help identify if your program is over- or under-performing. Of course, random fluctuations can be expected, but large variances should be investigated and understood.


Variances can be examined manually, but will be most effectively assessed through drilling down with member-level models.


Over time breakage will shift. As frequent customers accumulate more points, they become “power users” (i.e., customers with a larger share of the overall point distribution). It’s worth noting that these power users will tend to push the overall breakage rate down.


Step 9: Uncover further opportunities to optimize

identify opportunities to optimize

Finally, it’s worth remembering that the financial reporting process highlights many metrics that can be optimized for better overall performance.


Customer lifetime value (CLV) represents another key opportunity. CLV is the present value of all net cash flows expected for each member. This metric can be calculated through implementing a member-level model.


CLV represents a member-specific metric to focus on for maximizing the profitability of your customer loyalty program. The goal should be to increase CLV for each member each month. This is achieved through a mix of driving customer engagement and purchases.


By implementing a CLV model, program managers can also identify and target new members with a high CLV-to-acquisition-cost ratio.


It can be tempting to focus primarily on minimizing your program liability. After all, it’s the only piece of a loyalty program that shows on your financial statements. However, long term profitability and growth depend on maximizing CLV and customer revenue net of cost.



As the year winds down, deadlines get tighter and deliverables become more urgent. When assessing your next reporting period, give this 9-step checklist a try:


Step 1: Validate data

Step 2: Allocate redemptions to points earned

Step 3: Update breakage and fair value estimates

Step 4: Record accounting entries for reporting periods

Step 5: Seek an actuarial opinion

Step 6: Prepare financial disclosures

Step 7: Hedge exposure (optional)

Step 8: Assess progress against financial plan for the year

Step 9: Uncover further opportunities to optimize


As we wind down on 2018 and look ahead to 2019, understanding what goes behind each of these steps will help you continue to improve the accuracy of your financial reporting, and provide valuable insight to your company as it looks for new ways to optimize its financial performance.




Turn insight into action with predictive analytics solutions that help you 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.


3 Things Every Accountant Should Know About Loyalty Program Liability

Ever since the roll-out of new accounting standards, ASC 606 and IRFS 15, accounting departments have had to change the ways in which they deal with loyalty program liability. The primary shift in procedure has been that accountants must now defer revenue to cover the liability upfront, and recognize it only at the point of redemption.


The easiest way to visualize the role of an accounting department in managing loyalty program liability is to consider the following parallel: If loyalty program liability were a fire, accountants would be the firefighters tasked with putting it out. Wielding deferred revenue as a firefighter would a hose, it’s the job of accountants to carefully select just how much to defer. If they defer too little, the flames of the liability will burn through the company’s income. Deferring too much, however, will flood the company with “stuck” revenue.


Fortunately, there are ways for accounting departments to correctly forecast how much revenue they’ll need to set aside in order to mitigate incoming liability without creating a trail of stuck revenue in their wake. Read on to discover the three things every accountant needs to know about accounting for loyalty program liability.



1. There are new standards to keep in mind

international accounting standards


The rules regarding revenue recognition have changed. In a collaborative effort between the International Accounting Standards Board (commonly known as IASB) and the Financial Accounting Standards Board (FASB), an updated method of revenue recognition has been created that eliminates discrepancies between both organizations’ guidelines. This new, conjoined set of accounting standards is known as Accounting Standards Codification (ASC) Topic 606: Contracts with Customers, or, in its short form, ASC 606/IFRS 15 Accounting Standards.


How will these new protocols affect Accounting for loyalty programs?

In addition to replacing a variety of prior regulations, this latest standard outlines a five-part model that’s widely considered to be more thorough and prescriptive than earlier statutes. ASC 606/IFRS 15 Accounting Standard addresses the following elements:


  • Contract Identification
  • Identification of Performance Obligations
  • Determining Transaction Cost
  • Allocation of Transaction Cost to Performance Obligations
  • Recognition of Revenue


One of the biggest changes the new standard will introduce is the elimination of viewing contracts as singular transactions. In the context of loyalty programs, this will signify that both the upfront delivery of a good or service, as well as the fulfillment of any accompanying loyalty point redemptions will be considered distinct events for which accounting must occur.


Moving forward, accounting departments will have to consider the performance obligation as their basic unit of account. Performance obligations are typically defined as promises by a company to confer either goods, services, or a combination thereof upon a customer.


How will fragmenting the performance obligations within a contract change accounting practices? Principally, it means that companies now must carry out the following:


  • Identify distinct performance obligations, and allocate revenue accordingly. Separate margins must be applied for each individual performance obligation identified.
  • Pinpoint the event that corresponds to the eventual recognition of revenue for every individual performance obligation. Depending on the situation, revenue will either be recognized at the point of completion, or once a given interval of time has elapsed (such as when loyal rewards expire). Which of the options will be exercised is a function of how the precipitating service or good is transferred to the client, or the exact verbiage of the governing contract.


Equally as important as knowing which parts of a contract to recognize separately is knowing when to recognize them. As they say, timing is everything, and under the new standard, when revenue deferred for mitigating loyalty program obligations can be recognized will be more tightly controlled.



2. Accurate breakage estimates are essential

loyalty program


Now that companies must defer revenue to cover the costs of incoming loyalty points, being able to correctly anticipate breakage is more important than ever. How much revenue a business must defer corresponds significantly to its predicted breakage rates, so it’s essential that accounting departments have a reliable model of how redemptions will unfold.


The formula below demonstrates the role of breakage in anticipating loyalty program liability:


Liability = Outstanding Points * (1 – Breakage) * Cost Per Point

Outstanding Points = Points that have been issued but not yet redeemed or expired

Breakage = Percent of outstanding points that will ultimately go unredeemed

Cost Per Point = Expected cost of each point that will eventually be redeemed


When accounting for loyalty programs, it’s important to note that deferring the wrong amount of revenue can have unfortunate results, and both over- and under-estimations of how much to set aside can place a company in peril.


Companies that set aside too much revenue risk setting the stage for an outcome known as “stuck revenue.” This creates an unnecessary depression in revenue, and keeps the “stuck” funds from being applied in more effective ways.


In contrast, deferring too little revenue can lead to companies being forced to restate their income as a consequence of not having enough tucked away to cover the incoming barrage of redemptions. Income volatility is something neither companies nor shareholders want, and it’s easily preventable by using reliable breakage models.



3. Actuarial opinions are useful for justifying booked liability

Accounting for Breakage


In much the same way that a company would hire a general contractor to oversee the construction of a new wing for their headquarters, so too should they consult with experts before proceeding with loyalty program accounting predicated upon an ongoing breakage assumption. While redemption rate modeling can be accomplished to a high degree of accuracy, doing so involves taking a constellation of massive, interwoven data sets into consideration and distilling  them into a lucid picture of individual-level member behaviors. Actuaries have the expertise necessary to interpret this data and mine from it the answers your company needs in order to make breakage estimates you can trust.


Despite the informal-sounding nature of their title, actuarial opinions must be backed up by a full-scale report featuring graphs, exhibits, and texts that outline how the actuary arrived at his/her calculations. Having a nuanced, granular-level outline of member behavior crafted by actuarial experts wielding potent AI-driven analytics software is an important step towards justifying your company’s booked liability to regulators and auditors.


The bottom line

The new standard will bring divergent regulations into alignment and require that companies no longer view transactions as singular contracts.


Instead, companies will be expected to defer revenue until all performance obligations have been met or enough time has passed to bring the outstanding liability to a point of expiration.


Deferring the wrong amount of revenue can lead to highly undesirable outcomes, so it’s important that your company inform its deferral decisions using breakage estimates constructed using robust, analytics software that gleans future individual-level predictions from big data. An actuarial opinion is a reliable way for your company to obtain this information, and move forward with confidence in its decisions. How will your company ensure its booked loyalty is correct?




Turn insight into action with predictive analytics solutions that help you maximize the economic value of your loyalty program. Contact us for a free consultation.


How Well Do You Know Your Loyalty Program Members?

Successful customer loyalty programs have a clear focus: customer experience. You want to design an engaging, rewarding program that inspires member interaction and drives increased sales.


If you aren’t reaching this standard, it may be time to take a closer look at your loyalty program members.


Thanks to the internet, customers have more choices and higher expectations than ever. In order to thrive, companies need to extend their brand through a loyalty program. This requires they obtain valuable member insight. In other words, you need to have customer intelligence.


And we’re not just talking about preferences and spending patterns (although that’s certainly important). We’re talking about understanding your customers’ financial value to your company — now, and in the future.


But obtaining this information isn’t easy. Loyalty programs face shortcomings when attempting to understand and assess member behavior — especially from a financial standpoint. Ultimately, the goal is to find the right balance between breakage and customer engagement. To do this, you need robust customer intelligence.   


In this article, we’ll explore how companies can get a clear window into their loyalty program members and  provide strategies to optimize this insight for better financial performance.


Use data-driven analysis

data driven analysis


We’ve entered the age of analytics. Big data is making big business decisions.


In the past, customer financial intelligence meant using the most recent historical data; companies would measure how valuable a member is simply by looking at how much they’ve spent in the past.


Now, thanks to advanced predictive analytics, big data is helping companies predict future behavior. Using this method, companies can identify valuable members based on an estimation of their future spend.


Let’s take a look at each strategy in a bit more detail.


Use past behavior to predict future performance

Some companies think of their loyalty program as just another channel for incremental sales. Despite having massive amounts of customer data, their programs don’t have the analytical tools to give them the customer intelligence they need. As a result, these programs struggle to predict their loyalty program liability.


Many programs still rely solely on prior data (descriptive data) to serve as a guideline for future customer behavior.


A couple of examples could include:

  • Forecasting reward usage with a 3-year moving average for redemption rates
  • Forecasting loyalty program sales using last year’s results applied to a pricing factor


While using historical metrics to forecast future behavior may bring a certain sense of comfort (read: familiarity), it can also prove insufficient, as past behavior doesn’t always correlate to future performance. And it can’t assess breakage.


The opportunity in predictive modeling

According to the International Institute for Analytics, only 6 out of 10 loyalty program managers feel confident building predictive models.


Many managers don’t understand they should segment their members into groups in order to tailor program benefits and rewards.


There is underutilization of customer behavioral intelligence as well, with nearly 80% of programs relying heavily on customer transactional data, while ignoring customer needs or preferences. Looking back at historical data for forecasting has its limitations. If you take too long to understand your customers’ behaviors, you’re losing business.


Segment and forecast with predictive analytics

segment and forecast


Analytics and artificial intelligence are the next opportunity for improved customer intelligence.  


By now, we know that companies are sitting on huge amounts of valuable customer data. But how can this data be used to predict future behavior?


Predictive loyalty program analytics can take your historical data, apply it to different conditions, and predict outcomes.  


This can be used to forecast thousands of scenarios and behaviors, including:

  • Behavior of the entire member cohort
  • Segmented member behavior
  • High value member behavior
  • Low value member behavior
  • Impact of seeding inactive members’ accounts with free points
  • Impact of promotions on behavior of high-redeeming members


For example, analytics programs can run scenarios to predict under which conditions Customer Group A will spend more than Customer Group B. This can give tactical insight for determining when to boost loyalty program points (or other forms of program currency) for Customer Group A to encourage more spending.


In the end, this allows you to better prepare for different scenarios that could impact both earning and redemption rates in the near future (think: including upcoming holidays and campaigns) and far down the horizon.  



Calculate customer lifetime value (CLV)

customer lifetime value


While digging through past data and loyalty program analytics undoubtedly offers critical insights, one metric will provide you with more valuable information than perhaps all other metrics combined.


That metric is customer lifetime value (CLV).


CLV combines historical transaction data with forecasted future transactions, to give a present value estimate of the free cash flow created over a member’s lifetime. CLV can be calculated for individual customers and used to prioritize loyalty program resources for those customers with higher CLV.


Customer future value (CFV) and customer potential value (CPV) are predictive variations of CLV.  

CFV isolates the CLV equation to include only future expected value:

CFV = Expected future revenue – expected future redemptions costs


CPV measures the change in CFV for every reward point earned. Customers with the highest CPV have the potential to become high value customers, once properly incentivized.

CPV = Change in CFV / Change in points earned


You can calculate CLV, CFV, and CPV to rank and prioritize each of your members. Loyalty program resources can then be used on the highest-ranking customers, or the ones with the greatest CPV.  


The metrics described above give you a financial strategy to assess and prioritize program resources to drive the greatest value.


Assess Net Promoter Score


Analytics and assessing a customer’s lifetime value lead to predicting future behavior and identifying high value customers.


But what if you don’t have any high value customers to begin with?


A qualitative approach can sample your customers’ opinions towards your loyalty program. The Net Promoter Score (NPS) is a score from 0 to 10 ranking how loyal your customers are.  


Consulting firm Bain & Company originally created the term in 2003.  Now, the majority of Fortune 500 companies use it.  


Essentially, NPS is assessed by asking customers: “How likely would you recommend our products/services to someone you know?”


Depending on their response, customers can be grouped into the following categories:

Score Label Behavior
0 – 6 Detractor Likely not valuable
7 – 8 Passive Neither high nor low value
9 – 10 Promotor Highly engaged


Results can be segmented further according to other customer characteristics. NPS can also serve as a barometer for your current performance, giving you a quick snapshot of what your customers think about your program and brand.  


Think of it as a quick grading system for your customer loyalty program. If your program doesn’t inspire passives or promoters, you may need to reassess the implementation of your loyalty program strategy.


Bottom line: test, assess, and re-test

A profitable customer loyalty program with increasing customer value, an optimized breakage to engagement rate, and high customer satisfaction is the goal of most loyalty program managers.


Companies already collect vast amounts of customer data on transactions, behaviors, and demographics. The big opportunity lies in applying predictive analytics to understand customer behavior, identify high value traits, and convert more program members into high value customers.


When combined, these strategies can give you the insight you need to craft a loyalty program that delights customers and shareholders alike.




Get actionable financial insight and begin optimizing your loyalty program today. Contact us for a free consultation.


New Accounting Standards Increase Importance of Accurate Breakage Estimation

Recent changes by both the Financial Standards Accounting Board (FSAB) and the International Accounting Standards Board (IASB) are making companies reexamine the way they record and interpret revenue from customers enrolled in loyalty rewards programs.


The newly-developed standards demand that companies defer revenue generated at the time that loyalty points are accrued, and that they reintegrate that revenue into their income statement once points are redeemed.


The final tally of how much revenue is deferred (and recognized at a later point) is, in large part, a function of the anticipated amount of breakage. As a result, it’s important that companies correctly forecast the way breakage will affect them in the future.


When companies predict too much breakage, they fail to defer enough revenue. Conversely, when they underestimate breakage, they defer too much revenue and depress it more than necessary.


Many companies employ methods and models for estimating breakage that tend to underrepresent how much breakage will actually occur. This, in turn, results in deferring more revenue than appropriate. Doing so establishes the groundwork for a phenomenon known as “stuck revenue.”


I’ll explore stuck revenue in greater detail later in this post. But first, let’s review how breakage affects your company’s loyalty program liability.  


How to calculate loyalty program liability

Loyalty program liability is the cost of open obligations a company has to members of its loyalty program.  

Accounting for Breakage


Whenever a loyalty program member receives a point or mile , they become holders of a currency. This currency, be it a Starbuck “star”, a hotel loyalty point or any other type of organization-specific currency, represents a cost that the company will eventually have to absorb upon redemption. Because they can culminate in a cost for the company, a loyalty point is seen as a liability.


Commonly known as loyalty program liability, its impact is a direct function of breakage. The simplest formula for calculating loyalty program liability is:

Liability = Outstanding Points * (1 – Breakage) * CPP

Breakage = % of outstanding points that will ultimately go unredeemed

Cost Per Point = the expected cost of each point that will eventually be redeemed


In this model, breakage, or the percentage of outstanding points that will ultimately go unredeemed, must be correctly identified, or else loyalty program liability will be misrepresented. Breakage in this context is sometimes referred to “liability breakage,” as it represents the average breakage rate for all points previously issued.


Understanding breakage is a prediction problem. It requires the ability to predict how members will redeem their points during their lifetime with the program.


Companies frequently believe that they won’t be able to zero-in on breakage rates properly, but with today’s machine learning and computing capabilities, sorting through the massive wake of data produced by individual customers is now a possibility. By analyzing the behavioral patterns of individual members, companies can begin to understand member behavior at a micro-level, and gain a grasp on breakage levels within the program.



How the new accounting standards work

New accounting standards have made it so that most loyalty programs must defer revenue from loyalty program customers until it is “earned” upon the redemption of points.  

New accounting standards


For the past 16 years, the FASB and the IASB have sought to develop a uniform, principles-oriented standard to which all industries should adhere. Citing differences between the stipulations of generally accepted accounting principles (GAAP) in the U.S. and those of the IFRS, the board decided to make a set of amendments that would see improvements to both sets of protocols.


The chief issue with GAAP was that they prescribed a broad variety of industry-specific regulations for transactions that were economically similar; conversely, the IFRS saw a signature lack of specificity that got in the way of application and integration.


Originally, the guidelines outlined in the U.S. GAAP that addressed revenue recognition were SAB 101: New Revenue Recognition Guidelines and SAB 104: Revenue Recognition. In both of these sections, revenue was recognized as soon as a transaction was completed, and neither section provided guidelines for accounting for loyalty programs.


Eventually, FASB’s Emerging Issues Task Force, known as the EITF, delivered an outline on accounting for loyalty programs. The new guidelines contained the following approaches for revenue recognition:


Incremental Cost Model

Using this model, companies log-in revenue at the moment of purchase. Simultaneously, they incur a corresponding liability to the cost of redemption.

Deferred Revenue or Multiple Element Model

Some corporations prefer to employ this alternate approach, which views the rewarding of points as a distinct transactional element. Using this method, companies defer the recognition of revenue that is associated directly to the accrual of loyalty points to a time in the future in which either the customer redeems them, or they expire. In contrast to the incremental cost model, this model calculates the deferral amount using a fair value approach.


Regardless of which of the two models a company may have opted for, under the new guidelines, revenue will have to be deferred for most loyalty programs. Therefore, the emerging standard is most similar to the deferred revenue model. This will result in lower immediate revenue, where only when redemptions actually occur will the revenue be recognized. The liability becomes a statement of value in a way, as it produces a statement of income for when consumer redemptions actually transpire.


Of course, this is in significant contrast to the incremental cost approach, which lacks redemption-based income statement benefits. If one were to analyze this difference from the perspective of short-term finances, it could have an influence on how program finances are managed. However, those evaluating the way these rules affect the overarching economics of a loyalty program in the long-run will be pleased to discover that they are not actually affected.


Deferred revenue and breakage

There is a simple method for identifying the amount of revenue that will be deferred over the course of a month, reflected in the following formula:

Amount of Revenue Deferred Monthly = [Points earned in a month] × (1- [continuing breakage]) × FVPP

Fair Value Per Point (FVPP) = expected fair value of each point that will be redeemed


(Note that this formula is not an exact roadmap to the actual quantity of revenue deferred each month, due to minor nuances caused by the integration of “Relative Stand Alone Selling Price”)


This model makes the assumption that (1- [continuing breakage]) is reflective of how many of the points earned in the month will eventually be redeemed. This is different than [liability breakage], which represents the overall average breakage rate for all points previously issued.


An easy example of this principle in action is to imagine a local pizza chain called “Dough My Gosh.” For every slice a customer purchases, they accrue 10 “Doughllars.” After a customer has earned 50 Doughllars, they can redeem them for a free slice of cheese pizza, valued at $2. Each month, the succulent slices sold by Dough My Gosh generate nine thousand Doughllars, at a cost of four cents per Doughllar.


Assuming that Dough My Gosh has a breakage rate of .15, the amount of deferred monthly revenue can be calculated in the following manner:

Amount of Revenue Deferred Monthly = [9000] × (.85) × .04.

Thus, the final sum for Dough My Gosh’s deferred monthly income is $306.


Stuck revenue and its risks

As we’ve learned, upon the redemption of points, the recognition of revenue occurs. At this juncture, one of three potential situations are likely to unfold:


1. The original estimate for breakage was accurate. In the case of our hypothetical pizza company, the $306 that had originally been deferred will now be able to be considered “earned,” and will be eligible to enter the revenue stream.


2. The breakage was overestimated. In a situation like this, the entirety of the deferred revenue is recognized, but it isn’t sufficient to absorb to the impact of redemptions. This results in companies potentially seeing negative hits to income levels caused by the disparity between the amount of now-recognized deferred income and the expense of fulfilling point obligations.


3. The breakage estimate was insufficient. In this case, the company creates a pool of stuck income resulting from the excess of deferred revenue remaining in the liability. This can only be resolved by updating the breakage estimate.


The chances of revenue becoming “stuck” are high

Stuck revenue is a likely outcome that should be diligently avoided. Estimating breakage is no simple task, and a lot of companies attempt to gauge it using simplistic models. In fact, many companies still rely on historical breakage models. Also known as “vintage-based models,” they tend to generate estimates that under-represent the level of breakage. In an upcoming piece, I’ll discuss the pitfalls of these types of models in more detail.


In some instances, I’ve seen the breakage estimate dip below 20-30% of the actual rate. Depending on the scale of your program or company, this could result in tens of millions in revenue stuck in accounting limbo.


This phenomenon culminates in many businesses not recognizing the amount of stuck revenue they hold, as they cannot detect the biases in their breakage models until they develop more accurate, unbiased alternatives.


How to avoid creating stuck revenue

Without question, the simplest way to reduce the chances of creating stuck revenue is to better calibrate breakage estimation models. Here’s what one should expect from an ideal model:

Leverages predictive analytics and actuarial science: Understanding breakage is fundamentally a long term prediction problem. Actuarial science provides the toolbox to predict over long horizons, while modern machine learning and predictive modeling gives the toolbox to leverage the vast amounts of data produced by loyalty programs. All this leads to more accurate and insightful breakage estimates.   

Monitors breakage at an Earn Month level: Using this information, companies can track breakage for points accrued in a particular month and ensure the congruence of initial breakage assumptions with the behavior of actual breakage. By aligning these two data sets, businesses can diminish the possibility that revenue will end up stuck.

Is easy to update and updates frequently: This permits companies to increase the speed with which they recognize revenue and decrease the amount of time it takes to discover stuck revenue.

Demonstrates the uncertainty in breakage estimates in quantifiable terms: Frequently, companies prefer to err on the side of caution with regards to their breakage models. They believe this helps them prevent circumstances in which they fail to defer enough revenue to cover costs. By quantifying the degree to which their estimates are uncertain, companies can more prudently decide how much revenue they should recognize, versus how much they should maintain in a fund intended to serve as a buffer in the event estimates change in the future.


Bottom line

The changes in IFRS regulations will change the landscape of loyalty program accounting. They will lower the amount of revenue that companies receive at the moment of a transaction and will make it a necessity for corporations to insure against the coming wave of member redemptions with stores of deferred revenue.


Deferring revenue does not need to negatively impact the economics of a company and won’t do so if proper breakage estimates are used. However, if companies fail to use accurate, flexible models with the ability to evolve, they run the risk either overestimating breakage, which results in deferred revenue that is too low, or underestimating breakage, which leads to stuck revenue. Many companies have stuck revenue, and fail to realize it due to inadequacies in their breakage models.


In my future posts, I’ll get into the specific strategies companies can use to improve their breakage estimations. Make sure to check these out for valuable insights into how to correctly calibrate your breakage estimations with the realities of your business.




Turn insight into action with predictive analytics solutions that help you maximize the economic value of your loyalty program. Contact us  for a free consultation.


A Professional’s Guide to Loyalty Program Liability

To the great delight of customers, many companies offer loyalty programs. These programs allow customers to receive rewards for the purchases they make, with repeated purchases from the same company resulting in an ever-increasing, compounding array of incentives and kickbacks. Customers become motivated to direct as many of their purchases as possible towards the same organization, and businesses reap the rewards of more purchases and a loyal customer base.  It’s the perfect win-win scenario.


Except when it’s not.


While customer loyalty programs are a tried-and-true method of drumming up consistent business, potential risks must be carefully considered when implementing one into your company’s marketing framework. Loyalty programs can result in more sales, but they also carry what is known as loyalty program liability.


Loyalty program liability is the eventual cost to your company of the redemption of all outstanding loyalty points. If accounted for properly, they can be an effectively-wielded strategy for increasing customer engagement and strengthening the consistency of your company’s relationships with clients.


Conversely, 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.


Fortunately, these financial risks can be mitigated using careful planning and sophisticated analytics tools.  A loyalty program should be viewed as an investment, and, when prudently executed, can return far more than what it cost to implement.


Read on to find out how your company can leverage the benefits of loyalty programs while limiting the risks associated with loyalty program liability.


The basics of loyalty program liability

The impact of customers redeeming loyalty rewards is a balance sheet liability that can cost companies billions of dollars.

Loyalty Program Liability Basics

Though structures vary, the essence of a loyalty program is this: A company offers its clients a certain amount of “currency” per every unit of a designated dollar amount spent. In practice, this might look like Walmart offering shoppers 20 rewards points for every $10 spent, or a pet store offering one “Barky Buck” for every three cans of dog food purchased.


Of course, these currencies mean nothing if they’re not able to be redeemed for products or services, so the second part of the loyalty program formula is to allow customers to redeem the accrued currency for company offerings. Many times, these offerings are simply free or reduced inventory items, but often, the most valued (and desired) options can only be attained by earning enough of the loyalty program’s currency.


In each case, companies are forced to eventually assign the currency real value by making it exchangeable for tangible items. In turn, the delivery of these items in exchange for the rewards points comes at a cost to the company.


For example, that free, steaming hot cup of coffee given by Starbucks to the loyal client actually costs Starbucks some big money. While a single cup doesn’t amount to much, multiply it by the millions of Starbucks customers getting free coffees and the cost skyrockets. And what is this cost known as? That’s right —  loyalty program liability.


What loyalty program liability means to your company

All liabilities matter, and loyalty program liability can impact both the financial health of an organization and the way it’s perceived by the market.


Loyalty Program Liability - Points


The principle reason why loyalty program liability matters is that because, like any other variety of corporate liability, it can negatively impact the financial standing of a company.


The most direct way it can harm the financial health of a company is when companies opt to operate on a model that overestimates breakage. Breakage is the accounting world’s way of describing services that are paid for by a customer but not actually used.


A classic example of this is the sweeping tide of gym memberships that get activated at the beginning of every year by inspired would-be gym goers, bent on finally keeping their New Year’s resolution.


Similarly, every year companies make millions off of unused gift cards for which money is paid, but no products are consumed. While breakage can result in unanticipated profits, relying on it solely to underwrite unsustainable advertisement promises can have devastating effect on a company.


Changes in regulations concerning how companies must classify rewards points are also certain to heighten the impact of loyalty program liability. As of 2018, the International Finance Reporting Standard (IFRS) and US GAAP has mandated that companies categorize rewards points as deferred revenue, considering them separate parts of a sale. This signifies that, at least initially, companies will have to decrease their listed profits from whatever they’ve actually generated to the smaller amount that results after the value of the accompanying rewards points is subtracted. This is particularly true in the US, where the the change in accounting rules is more dramatic.


Although this doesn’t mean that companies cannot eventually incorporate the profits earned from breakage after points expire into their bottom lines, it does mean that, at least in the short term, the value of rewards points must be factored into reports of revenue. For any company, depressions in revenue reports are an important concern, as they affect investor confidence and can change the market valuation of the organization.


Bottom line

Like any other type of liability, loyalty program liability can affect the financial well-being of a company. Due to new regulations, businesses will now be forced to view rewards points as independent occurrences from the event that incurred them, and investors will view them as revenue deferred. This means that rewards points can bring down the revenue reports of a company at any given moment, even if, eventually, they come to increase them.


Most importantly, however, effectively managing loyalty program liability requires measured, strategic, interdepartmental cooperation between accounting, financial and marketing departments — which is where we now turn our attention.



Loyalty program liability accounting

Accounting departments need to accurately hone in on ultimate redemption rates and costs per point to correctly quantify outstanding levels of loyalty program liability.

Loyalty Program Liability Accounting

Accounting departments are pivotal to the management of loyalty program liabilities. After all, in order to properly calculate the direction in which loyalty program liabilities are heading, you need to know where they stand today.


For many of the largest loyalty programs, these liabilities can amount to billions of dollars:  


Deferred revenue liabilities from loyalty programs (2017)

CompanyDeferred revenue liabilities
American Express$7.751 billion
Marriott$4.940 billion
United$4.741 billion
Delta$4.118 billion
American Airlines$2.777 billion
Southwest Airlines$1.676 billion
Hilton$1.461 billion
Intercontinental Hotels$760 million


At this scale, even small changes in redemption behavior can drive significant financial impact. For example, if a $1 billion liability needs to be restated by just one percent, that will drive a $10 million hit to income during the period in which the liability is restated.


Proper understanding of the ultimate redemption rate (URR) as well as the cost per point (CPP), is key to getting the pulse of existing liabilities. While many companies believe that URR cannot be properly gauged, the reality is that this rate can be determined with a fair degree of accuracy.  What tends to impede companies from correctly evaluating their URR is their neglect of many valuable data points concerning the individual behaviors of their members.


The previous actions of loyalty members can help predict what they’ll do in the future, and by analysing these individually, companies can develop forward-looking databases that can give cogent insights on how likely individual point-bearers are to redeem the points.  


While this may require the analysis of huge quantities of data points across a large membership base, new techniques are making it easier for companies to wrangle this “big data” and uncover hidden insights. In particular, the combination of actuarial science and machine learning has proven to be a robust approach to predicting redemption behavior.


Financial reporting not only requires an estimate of the liability, but also disclosures about the timing of when the obligations will be fulfilled. This adds another dimension of complexity to the models, since the models must estimate the total number of points that will redeem as well as the timing of when they will burn.


Unfortunately, the methods companies use to estimate URR are often too simplistic to make accurate predictions of redemption behavior in the dynamic world of loyalty programs, and can result in materially biased estimates. These methods include approaches that look solely at aggregated historical data, or analysis by member vintage.


A URR estimate biased high means that you expect more redemptions to occur than actually will. This can result in deferring too much revenue, and never seeing the number of redemptions required to allow you to eventually recognize it. In essence, the revenue is “stuck” in the deferred revenue account.


A URR estimate biased low means that more redemptions will occur than you expect. When these redemptions occur, you may find that you don’t have enough revenue to cover the costs to fulfill the redemptions, causing a reduction in income during this period. Eventually, a true up of the liability may be needed to reflect a more accurate URR. This can be quite painful for companies with large liabilities. As noted earlier, even a small restatement of the liability can impact income by tens of millions of dollars.


Obviously, the outcome of having a URR estimate that is either too high or too low is not desirable. The nature of such risks often results in tough questions by senior leaders and auditors on the state of the company’s loyalty program liability. Having a robust analytic framework that uses sophisticated modeling rooted in actuarial theory, along with leveraging predictive modeling tools, helps mitigate risk and proves to these stakeholders that your estimate are accurate.


Bottom line

Proper accounting and financial reporting of your liability requires an accurate estimate of the ultimate redemption rate and cost per point. One powerful way to accomplish this is to integrate actuarial science with advanced computational capacities of modern predictive modeling techniques.



What finance departments need to know

Though loss of cash and an increase in liability is hardly appealing to the finance department, finding the proper balance of customer engagement needs to be strategically executed for sustained competitive standing.

Loyalty Program Finance

It’s important to note that the financial impact of issuing rewards points is not incurred at the moment at which they’re redeemed, but, rather, at the time of their issuance. The second the rewards points are doled out to participants, the company incurs the accompanying costs associated with “potentially redeemable points,” either as a reduction in revenue or as a direct recognition of expense, depending on how the program is accounted for.


While accounting is often focused on current liability estimates, many in loyalty finance roles are focused on future liability (i.e., how the liability will grow over time). And to accurately predict future liability, finance must have a solid understand of URR and CPP, too.


It’s also important for finance teams to recognize that, as user engagement increases and members graduate from being casual participants to more heavily invested users, rates of redemption will fluctuate upwards. This, of course, can be offset by the arrival of more new members, whose engagement is typically less vigorous.


This means that it should be expected that the URR will change over time. Failure to recognize this in your financial planning could result in material variance in financial performance.


The trajectory of the liability is also influenced by loyalty program changes and loyalty campaigns. Understanding how changes in these programs, such as modifications to expiration rules or earning rules, or the addition of a new co-branded credit card, impacts the URR and CPP is critical to building an accurate financial plan.


A sole focus on costs may drive some to wish for high breakage. This one dimensional view should be avoided. Program managers must be wary of trying to encourage an excess of breakage, as doing so involves intentionally disengaging customers from the company.


Best practice is for companies to focus not just on liability, but more holistically on customer lifetime value (CLV). CLV considers both the cost of redemptions, as well as the revenue generated from a lifetime of loyalty from your customers. This is the most important metric for any loyalty program.


Cost considerations for CLV include items such as acquisition costs and redemption costs. Therefore, the ultimate redemption rate and cost per point are critical to understanding CLV.


The other half of the CLV calculation is related to revenue — in particular, expected future revenue. Unlike liability, expected future revenue from your members is not an asset you can put on your balance sheet, and is a big reason why there is so much focus on cost.


CLV puts liability in the appropriate context. Program strategies may increase the URR, and therefore increase the liability. But if the expected future revenue sufficiently increases more than expected future costs, then the strategy is a smart financial choice. Disciplined loyalty finance professionals should insist on quantifying CLV to fully understand the financial health of their program.


Bottom line

Ensuring accurate loyalty program liability is not only critical to satisfying Wall Street’s demand for accurate financial forecasts, but for measuring loyalty program ROI as a whole. The challenge for the finance team, then, is to get this right amidst the technical difficulties of implementing precise predictive models and constantly evolving loyalty program marketing strategies.



What marketing teams should know about loyalty program liability

Marketers can get broader buy in and investment in their loyalty initiatives by accurately quantifying liability and CLV.

Loyalty Program Marketing

Marketing departments are responsible for the way in which a company engages with its clientele, and are the vehicle through which customer engagement is controlled. When it comes to loyalty programs, these levels of engagement predict corresponding levels of redemption. This means that marketing plays a key role in managing loyalty program liability.


For the most part, a marketer’s primary focus is not going to be program liability. And it shouldn’t be. With that said, they still have stakeholders in finance and accounting that are concerned about it. Understanding the financial implications of their engagement strategies will help get broad buy-in across departments.


Increasing breakage rates indicates a lack of engagement by members and demonstrates that customers don’t see the program as having value. While it may be beneficial for a company to dump its liability in the short run, this will not be a sustainable strategy for long-term customer engagement. It’s safe to assume that most loyalty professionals, regardless if they’re sitting in finance, accounting or marketing, know this to be true.


The challenge for many loyalty marketers, then, is that business cases often require sound logic and quantifiable evidence. This is where accurate liability estimates and CLV are helpful. If marketers can show that their chosen strategy will sufficiently increase CLV, this shows quantifiable evidence indicating that increasing liability will generate the needed ROI. It’s evidence that marketing, finance and accounting can all get behind.


Beyond building the financial case for a given strategy, CLV can also be used to help identify opportunities and new strategies. This is particularly true when CLV is estimated at the individual member level. This allows you to quantify and identify your most valuable members based on their expected future value, rather than their historical behavior.


This predictive view will have the biggest impact on future profit potential. Focusing your efforts and resources on these opportunities will maximize program ROI.


Bottom line

Marketers, finance professionals and accountants are all key stakeholders in a thriving loyalty program. The key metric at the intersection of their objectives is CLV. Accurate CLV requires an accurate estimate of the URR, CPP and program liability.


All loyalty professionals should demand predictive CLV and, consequently, demand accurate liability estimation.



Final thoughts: Keep your business sustainable

Regardless of where you’re sitting in a loyalty program, you need an accurate estimation of  ultimate redemption rate, cost per point, and loyalty program liability.


For accountants, this means needing to comply with financial reporting requirements.


For finance, this means building an accurate financial plan that ensures that smart financial decisions are being made.


For marketing, this means framing programs and campaigns in the context of how they affect liability and customer lifetime value to get needed buy in from accounting and finance.


While all companies must estimate URR, CPP and liability for financial reporting, disciplined loyalty professionals should not stop there. They should insist on evolving those models to provide accurate customer lifetime value estimation.


And accurate CLV cannot be calculated without first understanding URR and CPP at a granular member level. Accurate liability is the starting point.




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.


How Big Data Can Help Loyalty Teams Optimize Breakage

Breakage, or the percentage of loyalty points issued that will never be used, is an interesting concept. On the one hand, it drives short-term profit. Customers earn points through a rewards program and, for whatever reason, choose not to redeem them. Maybe they forget about the points. Perhaps they don’t really need the rewards. Or, they decide to put off redeeming earned points until some point in the distant future.

Whatever the case, when customers choose not to redeem their rewards points, organizations cash in: they were planning to count these points against their balance sheet, and now have nothing but profit to show.

But while this may be good for companies in the short term, failure to redeem loyalty points can signify a much larger problem: lack of customer engagement. Over the long term, this can spell doom for the business’s loyalty program profit.

The financial challenge for loyalty programs, then, is to find the right balance between breakage and customer engagement. Using big data can help.  


Big data for better financial management

Being data driven will become even more important for loyalty program financial management in 2018. This year, we’ll see the effects of new accounting standards for loyalty programs (ASC 606 in the US, IFRS 15 elsewhere). Liabilities will likely increase, particularly in the US, where the accounting changes are more significant.


These changes won’t affect the underlying economics of programs, but will likely make it more difficult for programs to make important decisions amidst all the noise. Discussions between chief financial officers and loyalty program managers won’t be fruitful without the smart use of data.


And while many companies are already using data as part of their marketing analytics strategy, this same rigor has not been applied to financial use cases. As a result, companies are leaving long-term profit and customer retention on the table simply because they aren’t optimizing this trade-off.


Big data for better financial predictions

Loyalty programs historically struggle to accurately estimate the cost of a point or mile, as well as its future breakage rates. However, by using the right actuarial theory and predictive modeling techniques, along with vast volumes of data, abundance of computing horsepower and advances in machine learning, surprisingly accurate points cost and breakage estimates are within reach — even with mileage expiration 2 or 3 years away.


Unfortunately for finance professionals, traditional methods for valuing loyalty program liabilities are not responsive enough because they are based on decades-old methods developed for the insurance industry.


Loyalty is much more dynamic and fluid, requiring a more responsive approach.


This extends beyond the finance department, too. Loyalty program marketers must know that these liabilities have massive financial footprints, which is why finance is so concerned with decreasing breakage.


Lower breakage will drive material short term increases in costs and reductions in profits. These short-term costs are often warranted if there are offsetting long-term benefits. Marketers need a credible financial measurement framework to quantify these costs and benefits. It’s very hard to get finance buy in without it.


Big data to mitigate breakage risk

Inaccurate liability estimates carry substantial risk. This is, in part, due to the scale of the liabilities. Many programs have liabilities in the billions of dollars, so even a small one percent unexpected true up on a $1B liability will cost the program $10M dollars.


We can also think about this from an opportunity cost perspective: how much profit is not realized because we haven’t optimized the breakage/engagement trade off?


Companies have massive amounts of data — data on historical transactions, data on earning activity, data on past redemption behavior. While many loyalty programs believe that the key to effectively using big data is to look at past behavior, historical data has its limitations.


If done correctly, using historical data can get you to 70% to 80% accuracy. While not 100%, this is certainly better than not using any data at all.


All professionals need to make the best decision possible with the data available. Ultimately, it comes down to leveraging the data to make smart assumptions. This means extracting every ounce of knowledge from data and combining this with collective judgement to arrive at the best answer.


Big data for breakage optimization

Program liabilities are often seen simply as a cost. But let’s reframe the situation to illustrate a huge opportunity: program liability can be a massively underutilized tool for program executives. All of those outstanding points provide incentive to drive desired behaviours. Most programs rejoice when they see a member with lots of points about to expire (it will get those points off the books at no cost!).

But this is short sighted.

Instead, we could use those points to motivate a dormant member to come back. Yes, this will increase redemption costs, but it can also increase future revenue. The challenge is finding members where the upside is greater than the cost.

And therein lies the opportunity — and a powerful reason to invest in big data and financial analytics.



The original version of this content appeared on Travel Data Daily.


Turn insight into action with predictive analytics solutions that help you maximize the economic value of your loyalty 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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