Category Archives: Loyalty Marketing

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 Convince Your CFO to Invest in a Customer Loyalty Program

Marketers are great at finding ways to drive customer engagement. They know how to use quantitative and qualitative data to uncover insights that drive desired behaviors. They empathize with the voice of the customer and are constantly thinking about optimizing the customer experience. Best of all, they use these skills to create effective campaigns and initiatives that drive customer engagement, build brands and grow businesses.

Ultimately, marketers hope that their efforts will not only enable a better customer experience, but help their company realize significant financial gains. One of the most effective ways they do this is through a customer loyalty program.


The struggle to achieve progress

Yet, the number one challenge I hear from marketers is how difficult it is to convince their CFO to invest in customer loyalty program initiatives. Finance usually asks very tough questions about these initiatives — questions that many marketers struggle to answer convincingly. Three of the most common questions include:

  • What is the incremental lift?
  • How big is the impact on customer lifetime value (CLV)?
  • What will the impact be on short and long term financial statements?

However challenging these questions may be, they provide CFOs and other members of the finance team with critical information about the cost — and risk — associated with an ambitious marketing plan. So, how can you prepare yourself to answer these questions and see your customer loyalty program through? We take a closer look at the reasoning behind the questions.


Question #1: What is incremental lift?

Convincing a CFO to invest in customer loyalty starts with proving the incremental lift. That is, the CFO wants to know if the campaign will drive extra profit above and beyond the status quo without the campaign.

If this incremental lift is sufficiently large, then the company should invest.

In a perfect world, measuring incremental lift would be easy. You’d set up two identical scenarios. In one scenario you’d run the campaign, and in the other you’d keep the status quo (i.e., without the campaign). You’d then observe how each behaves over the long term.

The difference in profit between these two scenarios is your incremental lift. Quantifying the incremental profit over the long haul is essential when it comes to capturing the campaign’s total impact.

But convincing the CFO to invest in loyalty isn’t easy. And unfortunately, one can’t measure incremental lift with complete precision.

Predictive models can help measure incremental lift in a reasonably accurate and defensible way, however, and include some of the best tools available to help marketers get loyalty program buy in from key stakeholders.


Question #2: What is the impact on customer lifetime value?

In addition to knowing the incremental lift, convincing a CFO to invest in a loyalty program requires you to understand its impact on customer lifetime value.

Finance 101 teaches us that the value of a company is more or less equal to the sum of the stream of future profit from all its customers — and CLV is a critical metric that captures this information.

It stands to reason that any marketing campaign or strategy that improves customer lifetime value is a sound financial decision. While this may be true, it isn’t necessarily the rule, since companies still need to manage financial statements.

The difference between costs and revenue can further complicate the decision.

The challenge here is that determining CLV requires the ability to predict customer behavior over both the short and long term. This is very hard to do. Most companies, therefore, limit themselves to predictions over shorter time frames, such as a few months.

In doing so, these businesses limit the opportunities to see returns.

If companies were to see the bigger picture, they would find that there are numerous opportunities to see returns over the long term. Marketers can benefit greatly if you have a believable model that can make long term predictions. This makes convincing the CFO to invest in loyalty a little bit easier.


Question #3: What is the impact on short and long term financial statements?

There are also some practical considerations beyond customer lifetime value and incremental lift that are important when it comes to convincing the CFO to invest in loyalty.

All companies need to manage their financial statements, particularly publicly traded corporations. This is a marketplace reality that isn’t going to change any time soon — and it often creates short term financial pressure on companies that are expected to meet Wall Street’s numbers.

This makes it critical to set correct financial expectations.

The financial risk associated with loyalty programs is often underappreciated. Most people don’t realize that loyalty program liability is often one of the largest on the balance sheet.

It’s common for these liabilities to run anywhere from hundreds of millions of dollars to several billion dollars. Furthermore, even the smallest change in liability has the ability to cause significant financial impact.

So despite the best intentions, new marketing strategies, campaigns and initiatives make it difficult for the finance team, as these all include levels of uncertainty. This affects liability and expected future revenue, and makes it challenging for finance to set the right financial expectations.

To get buy in from your finance team, you need to give them confidence about the short and long term implications of your marketing initiatives on their financial statements.

They need to understand the methods used to make and monitor underlying assumptions as new data emerges to help them manage the expectations of their stakeholders. This is the key to convincing your CFO to invest in loyalty.


How to convince your CFO to invest in loyalty

Whether you’re trying to measure incremental lift, customer lifetime value or the impact of your loyalty program on financial statements, having data that predicts member behavior over time will be crucial to getting support from your finance team — including the CFO.

Predicting such behavior isn’t an easy task. But there are tools out there that can help. Predictive modeling solutions allow you to quickly answer finances’ questions, and have the data and key metrics you need on hand to prove the true value of your loyalty program — for customers and your company’s bottom line.



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


And to learn more about loyalty program liability, don’t forget to check out:

What is Loyalty Program Liability?

The 1st Question to Answer if You Manage Loyalty Program Finances

The 2nd Question to Answer if You Manage Loyalty Program Finances

Accounting for Loyalty Programs: The Challenges & Opportunities

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Customer loyalty, predicted

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