Why Your Loyalty Program Needs an Actuary

Loyalty programs are designed for continuous participation from members.

 

But if you’re a loyalty program manager responsible for generating program ROI, it’s not enough to simply understand where you are — you need to know where you’re going.

 

Unless you have a crystal ball, however, you’re likely going to need some help. But where should you start?

 

The answer is predictive modeling. Predictive modeling, which uses statistics to predict future outcomes, helps program managers determine the value each loyalty program member will bring to a company. Unfortunately, accurately forecasting the right numbers is easier said than done.

 

That’s where actuaries come in.

 

While their title may not be the most glamorous, actuaries are the financial experts who can shed light on the future of your loyalty program.

 

How exactly? By forecasting and extrapolating off past and current data, often with great accuracy. In fact, employing actuarial insights is one of the best ways to prepare your program for long-term financial success.

 

Actuaries can help you answer important questions such as:

 

“How can I optimize member acquisition?”

“Who are my highest value members?”

“Is my program strategy driving incremental value?”

 

When you arm yourself with an actuary, you’re taking an essential step towards setting and achieving your loyalty program goals.

 

In this piece, we’ll explain:

  • What actuaries actually do
  • 9 reasons why actuarial insight is useful to your loyalty program
  • Why a data science team isn’t enough
  • What you should look for in a loyalty program actuary

 

Do you know where your program is heading and why? Let’s enter the world of actuarial insight.

 

What does an actuary actually do?

 

To those new to the world of actuarial consulting, actuaries are somewhat of an enigma.

 

The Society of Actuaries labels the profession as “Part super-hero. Part fortune-teller. Part trusted advisor.” While you probably won’t encounter an actuary with a cape, these professionals take on an essential role in accounting for loyalty programs: they manage loyalty program risk by planning for the future and protecting their organization against loss.

 

Actuaries are generally associated with the insurance industry

Most people associate actuaries with insurance, and they’re right: over 50% of actuaries are employed directly or indirectly by the insurance industry.

 

Insurance companies are in the business of predicting outcomes. These companies are required to reserve cash for the policies that they sell, and must set aside money for the claims they expect to eventually pay out on those policies. Given the range of insurance policies, this planning can take on many different forms.

 

For example, when an insurance company issues your car insurance policy, they have to estimate the probability of paying a claim, the size of that claim, and the timing of when it will occur.

 

You pay your policy annually. And maybe someday (hopefully not), you’ll need to draw on your insurance policy to cover the costs of an accident or damage to your vehicle. The insurance company has to ensure, on average, customers are paying more into the plan than the company is doling out. It’s how they stay in business while still providing uninterrupted coverage.

 

Another example is workers’ compensation insurance. Some claims may not be reported until many years later (such as a disease brought about by occupational conditions), but would require medical payments throughout the rest of a person’s life.

 

While auto insurance claims only involve predictions over the twelve months of the policy period, reserving for worker compensation requires the tools to predict expected claims payments decades into the future based on the information available today.

 

When it comes to your loyalty program, this is just the type of information you need.

 

Actuaries have the toolbox for making long-term predictions

Fortune-tellers have crystal balls; actuaries have numbers, data, and statistics. Using statistical analysis, they can evaluate the probability of future events occurring and plan accordingly.

 

In the context of loyalty programs, cash flow (amount, timing) is the primary metric examined. Other metrics can include:

  • Customer lifetime value (CLV)
  • Customer future value (CFV)
  • Customer potential value (CPV)
  • Ultimate redemption rate (URR)
  • Cost-per-point (CPP)
  • Breakage

 

Forecasting over the long term is required for insurance companies, and can be highly beneficial for loyalty programs. Let’s walk through the benefits to long-term forecasting with actuaries.

 

9 reasons why actuarial insight is useful for loyalty programs

actuarial insight for loyalty programs

 

1. Helps predict loyalty program liability

When points are issued, companies must defer revenue for the eventual cost of redemption. This is required by the financial reporting standards ASC 606/IFRS 15. The challenge in estimating liability, however, lies in the number of variables associated with estimating redemption costs.

 

Actuaries can predict what percentage of points will ultimately be redeemed, when those redemptions will occur, and how much those redemptions will cost the company. After all, points can be redeemed many years (potentially decades) after they are earned. The best estimates require long-term forecasts.

 

Adding to this challenge is the reality that loyalty programs are dynamic, constantly evolving entities:

  • Members change their behavior frequently
  • Members are becoming increasingly sophisticated thanks to the availability of online resources on loyalty programs
  • Programs frequently offer deals and marketing campaigns
  • Programs occasionally change program structure – e.g., expiration rules, award charts, accrual charts, etc.

 

Liabilities can be very large, in the hundreds of millions or even billions of dollars – particularly among frequent flyer and hospitality programs. At this scale, CFOs and auditors require proper due diligence to validate liability estimates. Actuarial opinions provide formal documentation from a credentialed actuary stating their professional opinion on the booked program liability. This provides CFO, auditors, and other stakeholders proof of the due diligence.

 

2. Quantifies customer lifetime value (CLV)

While liability is important, the whole point of a loyalty program is to increase customer brand loyalty. (i.e., customers should keep coming back to your company to make purchases). The best way to quantify the value of this long-term loyalty is a metric known as customer lifetime value (CLV). CLV predicts the profits that a given member is expected to generate over his lifetime in the program.

As we’ve discussed in previous articles, CLV and its family of metrics are the most important KPIs to link program management to economic value creation. In fact, you can even use these metrics to proactively create economic value (you can review how to calculate customer lifetime value here).

Properly calculating CLV is critical to loyalty program profitability and requires the ability to predict over long horizons. As you’ll see, CLV and its many applications will unify the rest of this section.

 

3. Identifies high value members

Simply put, members with high CLV are likely to spend more money with your company. Targeting them with special offers will make them feel appreciated and inspire even more purchases, maximizing the likelihood of unlocking their expected future profits.

 

4. Drives incremental value by targeting high potential members

With just a small nudge, high potential members will likely increase their program participation, and, as a result, their CLV. A well-executed, targeted incentive strategy can grow the future economic value of your loyalty program.

 

5. Targets at-risks members with decreasing CLV

On the flip side, loyalty programs can use CLV as a defensive strategy. By identifying leading indicators of declining participation, programs can be designed to proactively prevent loss of economic value.

 

6. Provides enhanced customer service based on CLV

CLV quantifies the value of each customer. This indispensable information enables customer service agents to focus their efforts and spend more time and energy on resolving issues for those members most valuable to the organization. Note that this doesn’t mean that members with a low CLV don’t matter; rather, it focuses efforts on providing services that will keep your most loyal customers happy.

 

7. Optimizes new member acquisition based on value rather than cost

Well-run loyalty programs can become an appreciating asset for any company. Examining CLV as a growth value metric ensures that every dollar spent on acquisition is spent in a way that maximizes economic value, rather than minimizing cost.

 

8. Optimizes program design

Actuaries conduct scenario testing to forecast the impact of various program changes on CLV. By quantifying how these changes impact CLV, program managers can identify and implement the changes that drive the most growth. This ensures that program structure changes are focused on maximizing economic value.

 

9. Aligns marketing and finance around a common set of KPIs to measure economic value creation

Miscommunication and lack of alignment leads to lost time in business. Aligning KPIs across organizations prevents organizational conflict. Using the shared language of CLV, organizations can increase the speed by which they make decisions — a competitive advantage in today’s fast paced world.

 

Why a data science team isn’t enough

In today’s digital world, data analysis is a highly sought after skill. Wherever there’s a large amount of data, data scientists are needed to plug and chug through significant statistical analyses, searching for patterns and identifying solutions.

 

At first glance, data scientists share many skills with actuaries. Both require business intelligence and data analysis skills. And the end result of data analysis and actuarial insight is a best-fit solution to an ambiguous problem.

 

However, these professions are not interchangeable.

 

Data scientists lack domain knowledge

Data scientists know how to build models to make predictions, but not necessarily predictions over long horizons, which is where actuaries excel. Actuaries are also subject to rigorous formal training and credentialing in their domain of focus – whether that be insurance, finance, investments, etc.

 

We can’t overstate the value of domain knowledge.  Consider this example:

Suppose you need brain surgery. Are you going ask your family doctor to do the operation, or a trained brain surgeon? Both are very smart, both are trained in the medical profession, and both probably have the aptitude to do it. But only the brain surgeon has the training, experience, and specific domain knowledge required to do the job correctly.

 

Data scientists and actuaries are similar: both are smart, both are trained in applied statistics, and both have the aptitude to learn to predict over long horizons; but only actuaries have the explicit training, experience, and specific domain knowledge required to do the job right.

 

What makes an ideal loyalty program actuary?

Traditional actuarial models focus on making predictions in aggregate. But the value of CLV lies in its ability to make predictions at the individual member level.

 

Within actuarial consulting there is even more niche domain knowledge required to properly apply actuarial theory to loyalty programs. The most effective professionals combine predictive modeling with expert financial reporting knowledge.

 

Loyalty Program Actuary - Venn Diagram

 

Individual predictive modeling

Loyalty program actuaries need to know how to leverage today’s technology to build predictive models. Traditional actuarial methods were developed decades ago, well before the availability of today’s big data and advanced predictive modeling capabilities.

 

While the basic actuarial concepts underlying traditional methods remain applicable to today’s loyalty programs, the methods themselves need to be redesigned to leverage modern technology and allow for individual member-level predictions. Therefore, you need an actuary with strong predictive modeling skills and experience using modern technology to solve problems.

 

Financial reporting expertise

Given a loyalty program’s complex financial reporting requirements, you not only need an actuary with predictive modeling skills, but also one with a strong understanding of the business dynamics and financial reporting regulations surrounding loyalty programs.

 

ASC 606/IFRS 15 have introduced updated revenue recognition standards and a fresh focus on loyalty program accounting practices. Your actuary needs to know how to properly identify performance obligations when accounting for loyalty programs.


In summary, the ideal loyalty program actuary is adept at predicting behavior over the long term, can build predictive models at the individual member level, and has a nuanced understanding of business dynamics and the financial reporting environment. This combination enables actuaries and loyalty program leaders to translate predictions into real business insight and loyalty program value.

 

Optimize your program with the right actuary

loyalty program optimization

 

Loyalty programs generate more than a significant amount of data. Between engagement metrics, membership statistics and financial results, programs need someone with the skills to discern the meaning amidst all the noise. And while it’s easy enough to turn to any data scientist or actuary, to get the most actionable insight from your data, it’s best to trust those with deep domain expertise.

 

Imagine if each year at tax season, you hired an average accountant with limited personal tax experience. Would you really expect him or her to optimize your taxes and bring you the biggest refund?

 

The odds are that you would sleep better at night (and receive the biggest bang for your buck) if you hired a professional tax accountant with years of experience.

 

An ideal actuary can provide just as much peace of mind — and financial return — to your loyalty program. Select an expert who understands how to model your loyalty program liability, the nuances of customer lifetime value, and the fundamentals of optimizing program ROI.

 

Loyalty programs are one of the most effective ways of driving long-term customer value and ensuring the continued success of your business. When it comes to your program’s financial management, why cut corners?

 

 

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KYROS provides sophisticated predictive analytics solutions that help companies optimize the financial performance of their loyalty program. Need an experienced set of eyes? Contact us for a free consultation.

 

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.

 

 

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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.

 

 

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

 

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