4 Ways Predictive Analytics Can Transform Your Customer Loyalty Program

Predictive analytics is revolutionizing the ways companies leverage customer loyalty programs to forge personalized, dynamic relationships with their members.

 

Once upon a time, loyalty programs could only promise a blanket set of incentives or, at best, personalized offerings based upon a customer’s previous purchases.

 

Now, predictive analytics is making it possible for businesses to court customers with exciting, new solutions that they may have never considered.

 

Predictive analytics allows for far more than targeted marketing, however; it’s a window into how customers think and, perhaps most importantly, can help predict a member’s behavior in the future.

 

Through the use of advanced software and cutting-edge machine learning algorithms, predictive analytics gives your company the power to discern critical information from a customer’s data trail.

 

Thanks to predictive analytics, questions such as, “When are customers most likely to make a purchase?” “Are they considering migrating to a competitor?” “Which strategies work best to increase or restore member engagement?” can be answered faster and easier than ever before.

 

Let’s explore some of the ways your company can take advantage of predictive modeling to draw insights from your customer loyalty analytics and improve return on your loyalty program investment.

 

How loyalty programs are using predictive modeling

loyalty program analytics

 

One of the best things about customer loyalty programs is that they generate vast amounts of customer data. Using this big data, companies can construct more strategic approaches to member engagement and maximize the return of those interactions.

 

The practice of distilling insights on future outcomes from massive webs of data is known as predictive modeling. Information drawn from customer loyalty programs can be harnessed to create predictive models that increase loyalty program ROI. Despite its futuristic overtones, predictive modeling is hardly confined to the realm of far-off, underdeveloped technologies looming on the horizon.

 

Instead, it is a method used by industry leaders throughout the world to bolster loyalty program ROI consistently and reliably. In fact, research by McKinsey demonstrates that companies that build predictive models based off of customer data experience a 126% increase in profit margins.

 

Some of the ways predictive analytics is frequently used to increase loyalty program ROI include:

 

1. Targeted recommendations

Targeted Recommendations

 

The more attuned loyalty programs strategies are to its members’ needs, the more likely these  members will engage with the company.

 

Research shows that customers who are actively engaged with a brand’s loyalty program make purchases at almost 90% greater frequency, and spend up to 60% more per transaction. It’s no wonder that companies are taking advantage of predictive modeling to better calibrate their recommendations to match member profiles.

 

While targeted recommendations can reinvigorate member engagement, it’s important to combine historical purchasing data with real-time purchasing information in order to create the clearest picture of what members need.

 

For example, if a customer recently booked five nights at a hotel your enterprise manages in Hawaii, suggesting they sign up for a tour of local waterfalls through your company’s website would be a viable way of expanding sales for your business and increasing value for your customer.

 

Failure to target customer needs will discourage even your most loyal members.


2. Personalized rewards

 

Almost as important as your loyalty program offering clients targeted, relevant suggestions is that it offers targeted, personalized rewards. Many companies use predictive analytics to anticipate the types of rewards that best incentivize members to continue engaging with the organization.

 

By doing so, loyalty programs can ensure that members of all types and preferences are motivated to consume more, without suffering the negative effects of losing customers who aren’t interested in certain types of rewards.

 

Most importantly, personalized rewards demonstrate to your members how grateful your company is for their business, and serve as palpable indicators of how beneficial their relationship with your company has been.

 

3. Promotions at the right time

promotions at the right time

 

Needless to say, offering discounts on Christmas trees in July is less likely to drum up sales than presenting these same discounts in late November. However, it’s more than seasonal promotions that depend upon the right timing to be successful!

 

By looking at the times during which members actually engage with their loyalty program, companies can engender a model that captures the most appropriate time to reach out to its loyalty program members.

 

In fact, many modern companies use information obtained via loyalty programs to narrow down when customers will be most receptive to new offers.

 

4. Predicting customer value

 

While many companies use customer loyalty analytics to improve their alignment with customer needs, they often overlook a critical application of loyalty program data: identifying which members are actually worth engaging with.

 

Let’s go back quickly to our earlier example about the Hawaiian hotel. Say a newlywed couple rents out one of its honeymoon suites for two weeks. By the time they’ve finished enjoying their honeymoon, they’ve likely made more than a few onsite purchases and accrued a tidy sum of loyalty rewards points.

 

The hotel’s marketing department can then craft a series of promotions that encourage the couple to use these accumulated loyalty points towards other similar rewards, long after the honeymoon has ended. This, in turn, maintains their engagement with the loyalty program, and fosters a closer relationship with the hotel brand.

 

With the right predictive models, your company can hone in on members with the highest customer lifetime value (CLV), as these members will generate the greatest amount of free cash flow for your business.

 

The formula for customer lifetime value is as follows:

 

Once you know the amount of free cash flow that an individual customer will generate for your company, you can change the strategic focus of your marketing efforts to engage primarily with those most likely to take advantage of your promotions. The higher the CLV of the members you engage, the greater the potential ROI of your loyalty program.

 

However, it’s worth noting that as members engage more with your loyalty program, your loyalty program liability will increase accordingly. Make sure you take the appropriate steps to mitigate this liability.

 

The bottom line

Predictive modeling has the potential to increase your company’s loyalty program ROI by helping you make sense of mountains of customer data, all while giving you insight into new ways of engaging with program members.

 

It’s equally important to take advantage of metrics such as customer lifetime value, and target your loyalty program’s marketing efforts accordingly to help control loyalty program liability. After all, inefficiencies in marketing expenditures account for a substantial percentage of lost revenue.

 

Loyalty programs depend upon proper execution to generate ideal returns, and robust  predictive analytics offers a significant competitive advantage to organizations looking to make the right strategic decisions — today, and in the future.

 

 

 

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 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.

3 Things Every Marketer Needs to Know About Loyalty Program Liability

Every marketing professional wants to engage their customers and inspire lifelong brand loyalty. But in order to develop marketing campaigns that strike a chord with clients, marketers need to understand the needs, expectations, and predispositions of their target demographics.

 

One of the most effective strategies for both engaging customers and gaining key insight into their behaviors and expectations is to instate a customer loyalty program. Whether you’re giving away free coffee, points that can be redeemed for prizes or discounts, or other incentives, customers love loyalty programs — and they play a key role in driving both customer satisfaction and brand loyalty.

 

However, the success of a loyalty program depends not only on its design, but on its execution. This includes safeguarding against accompanying financial risks.

 

Chief amongst these risks is a phenomenon known as loyalty program liability, or the cost incurred by companies once all outstanding rewards points have been redeemed.

 

If correctly anticipated, companies can defer the necessary amount of revenue required to absorb the incoming liability without sustaining any financial injury. To do so, however, they must first know two things: the cost of redeeming each outstanding point, and the percentage of outstanding points that will ultimately be redeemed.

 

What do marketers need to know about loyalty program liability, and what metrics should they focus on in order to best understand the financial risks (or rewards) facing their company? Read on to learn more!

 

A brief overview of loyalty program liability

loyalty program liability overview

 

The simplest way to uncover the cost of your company’s loyalty program liability is to use the following formula:

 

Total Number of Outstanding Points x URR x CPP

 

URR, or ultimate redemption rate, refers to the percentage of outstanding points (or whatever other form of currency your company disperses to loyalty program members) that will eventually be redeemed. The cost per point, or CPP, is the cost the company incurs during the redemption of each point.

 

Once you’ve figured out the values for both of these indices, you can unearth you company’s loyalty program liability by multiplying the total number of outstanding points by the URR and multiplying the resulting amount by the CPP.

 

Revenue should be deferred upfront

deferred revenue

 

So, when should companies concern themselves with the financial impact of these points? Though it may seem counterintuitive, domestic and international regulations prescribe that revenue used to satisfy obligations to program members be deferred at the moment of issuance — not at the point of redemption.

 

This need to defer revenue can lead to three potential scenarios. The first is that not enough revenue is deferred, culminating in your company being forced to restate its income levels. The second scenario is that your company defers too much revenue, leading to a phenomenon known as, “stuck revenue.”

 

The third, and ideal, scenario at which companies can arrive is one in which just the right amount of revenue has been deferred.

 

Knowing how much revenue to defer is critical to contending against loyalty program liability, and can be best accomplished by using granular-level measurements that capture the future actions of individual members. After all, acquiring correct estimates for URR is entirely hinged upon knowing the way customers will behave in the future.

 

Without salient URR or breakage estimates, your company will not be able to generate an accurate forecast of its upcoming liability, and won’t be able to defer the corresponding portion of its revenue.

 

By crafting loyalty programs that incentivize customers to make purchases with company cards or other devices that monitor spending patterns, marketers can cultivate an abundance of  harvestable data. In this respect, marketers can shine, because this data allows their company’s finance and accounting teams to better predict the behaviors of individual customers.

 

Though extracting insights from such large swaths of information may seem like a monumental challenge, the good news is that recent developments in the field of predictive analytics and artificial intelligence have made unlocking the secrets submerged within Big Data a reality.

 

Loyalty programs are advantageous for finance, too

 

Without question, loyalty programs are tremendously helpful for marketers. They provide concrete incentives for customers to continue engaging with a company, and give the marketing team a renewed series of opportunities to keep clients abreast of emerging offers.

 

Moreover, they produce data that allows marketing to calculate the customer lifetime value (CLV) of individual members. CLV denotes how much free cash flow a particular person will generate for the company throughout the course of their time with the company.

 

By gathering an aggregate of the individual CLVs of loyalty program members, marketing can provide their finance counterparts with a clear window into the financial value of their customer loyalty program.

 

CLV can also help marketing teams target those customers with the most value. One of the greatest inefficiencies that besets modern companies is the amount of money that’s wasted on failed attempts to engage disinterested clients. By understanding a member’s cumulative lifetime value, you can know whether the return generated from their engagement justifies the cost of engaging them.

 

The company can then reinvest the money saved from not targeting less lucrative members to drive up the frequency of purchases made by their most engaged members. Thus, a strong CLV estimate may just lead to the conservation and expansion of company revenue.

 

Simply stated, well-designed loyalty programs drive up sales and increase a company’s bottom line, making them an invaluable strategy for marketing and finance departments alike.

 

The bottom line

Loyalty program marketers have a tremendous opportunity to increase brand loyalty while helping drive company revenue. Not only do they have detailed insight into their members, but they can provide critical information to finance about loyalty program liability, helping their company optimize its customer engagement strategy.

 

Loyalty program liability can be combated using precise estimates of user behavior drawn from granular-level information. This information will give your company the data it needs to construct an accurate model of its program liability, and draw the insights needed to generate a real return on its loyalty program investment.

 

 

 

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

How to Judge the Success of Your Customer Loyalty Program

Is your customer loyalty program successful?  If your company offers discounts, promotional deals or free rewards, knowing if your loyalty program strategy is generating a return on its investment is paramount.

 

Imagine discovering your large promotional budget fails to attract or retain loyal customers. Not good; you’re in business to improve profitability, and, if you’re not getting measurable returns, your efforts are likely being lost.

 

Yet customer loyalty programs are clearly worth the investment, with loyal customers spending 67% more than new customers. They can not only generate significantly more revenue for your business, but play an equally important role in garnering brand loyalty.

 

Customer loyalty program goals

The goals of any customer loyalty program strategy are clear:

  1.     Improve customer experience
  2.     Improve customer retention
  3.     Encourage more sales

 

Given these goals, how do you define and measure your success?

 

One way to measure your success is by surveying customer loyalty metrics or assessing customer behavior. Hosting customer surveys, tracking customer satisfaction, or calculating a net promoter score (NPS) can identify positive or negative feedback trends in customer loyalty.

 

Still, while these can be leading indicators for longer-term success, they don’t offer a clear financial gauge of your program’s impact, nor can they assess its profitability.  In order to assess your program’s financial return, you need to focus on several key financial metrics.

 

Customer lifetime value (CLV)

customer lifetime value

 

It costs significantly more to acquire a new customer than it does to retain an existing customer.  In fact, in some cases, it can be up to 25 times more expensive.

 

But what if we flipped this around?

 

An existing customer can be worth much more than a new customer.

 

Customer lifetime value (CLV) measures the profitability of an existing customer. A formula that can be used for many loyalty programs is as follows:

 

 

Customers are assets

Consider each customer as an asset that could appreciate or depreciate over time.  Some assets lose value as they age, requiring more cost to maintain an ever-decreasing output.  Other assets, such as financial investments, generally increase in value over time. The goal of measuring CLV is to ensure your customers are increasing assets.

 

A good question to ask yourself is, “How much value are we creating, in exchange for what it’s costing us?

 

Customer lifetime value can be used to categorize your customers into buyer personas as well, so you can focus on the highest CLV members. CLV can be further broken into customer future value and customer potential value to isolate and forecast future behaviors or potential outcomes.

 

If CLV increases with time, your program is moving in the right direction.

 

Customer retention rate

 

A well-defined customer loyalty program strategy is aimed at maximizing potential revenue from members.  A clear indicator of customer loyalty success is customer retention rate (CRR).

 

For a given period of time:

 

Customer Retention Rate = (Ending Customers – New Customers) / Initial Customers x 100 

 

Why is it easier to sell to existing customers than to gain new customers?

 

Existing customers are easier to reach

We all know how difficult it is to make new friends.  As adults, our time is focused on work, errands, taxes, and staying healthy.  We have barely enough time to enjoy ourselves. It’s no wonder it takes so much effort to enter a person’s life.  You must find common ground, shared interests, and find time to connect in an ever-busier schedule.

 

In contrast, it’s relatively easy to call up an old friend to ask for advice or input on solving a problem. They are significantly easier to reach on the phone or meet in person.

 

Customer retention can be viewed the same way. It’s easier to contact an existing customer than reach a new one. Your existing customers have already supported your business in the past, and you have a better understanding of what brings them value and makes them happy as consumers.

 

The broader you grow your customer base, the better opportunity for future revenue growth.  A successful loyalty program that fosters a positive customer experience will lead to higher customer retention rates.

 

Ultimate redemption rate

ultimate redemption rate

 

While you may be increasing sign-up rates, are customers actually enjoying your program?

 

Coupons and rewards drive people to brands; calculating how often customers use your program’s rewards can provide insight into if they are actually taking advantage of your loyalty program. Incentivizing customer interactions will help increase the redemption rate.

 

Ultimate Redemption Rate = [Total loyalty points spent to date + Predicted future points that will be redeemed] / Number of points issued to date

 

It’s important to remember that customers are engaging not just with the benefits of your program, but with your brand. Active customers are a positive sign that you have designed a compelling incentive program. This also increases the likelihood of repeat customers. A successful loyalty program will have a strong ultimate redemption rate.

 

Note that a strong ultimate redemption rate is a positive sign of engagement, but it also means an increased cost to fulfill redemptions. The CLV metric discussed previously is critical to put the redemption cost in the appropriate context. If CLV is large and increasing, then the redemption cost is justified.

 

Loyalty program ROI

 

Ultimately, this leads us to measuring loyalty program return on investment (ROI).  Your goal is to improve profitability through improving the customer experience, and loyalty program ROI measures the profitable return on your customer loyalty program investment.

 

Loyalty Program ROI = Value Generated / Investment

 

Value can result from:

  • Sales
  • Customer retention
  • Referrals

Investments can include:

  • Marketing costs
  • Operational costs
  • Technology costs
  • Cost of rewards redemptions

 

Unfortunately, there’s no way to exactly measure the value generated. The most direct way to capture it is the change in customer lifetime value over time.

 

However, as you extend your loyalty program, whether the incentive is free reward points or travel miles, you’re also creating a loyalty program liability.  At some point in the future, a customer may cash in on those free perks and rewards, and if not properly accounted for, these redemptions can wreck financial havoc on your organization.

 

A clear quantification of loyalty program ROI will help convince your most cost conscious and risk averse colleagues to continue to invest in the program.

 

Many industries invest heavily in loyalty programs – airlines, credit cards, banks, and investment brokerages. All are very efficient and very competitive markets. Customer loyalty programs represent one key avenue to gain an edge on the competition

 

What gets measured, gets managed

Ideally, acquiring new customers and converting them into loyal customers will lead to an increase in profitability for your organization. A successful loyalty program will improve your customer retention, engagement, and average customer value.  

 

Measuring the success of these parameters doesn’t need to be an arbitrary or abstract process.

 

Apply quantitative financial metrics to assess your program’s performance. Identify where you stand now, track changes over time, and compare your metrics against industry benchmarks.

 

By designing a loyalty program strategy that focuses on these metrics, you’ll be well on your way to improving your customer’s experience and your company’s bottom line.

 

Where does your program stand?

 

 

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

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.

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.

 

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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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