Category Archives: Loyalty Program Strategy

How to Prove the Value of Your Loyalty Program

Loyalty programs are far from a new concept. If we dig deep enough inside our wallet, most of us will likely find an old, tattered punch card … just in case an opportunity presents itself to finally cash in for that free sandwich.

 

While many of these programs may look different today — think instant digital redemptions — the original loyalty program value proposition remains: in exchange for repeat business with a brand, loyal customers are given opportunities to earn rewards. Customers tend to recognize the value of these rewards, which helps explain why more than half say they will join a loyalty or VIP program.

 

Most business leaders also understand the value of loyalty programs, at least in theory. Nearly anyone can tell you that it costs more to acquire a new customer — five to 25 times more, depending on who you ask — than to keep an existing one, and loyal customers tend to spend more with a brand than new customers. In other words, building a loyal customer base should have a significant impact on a firm’s bottom line and stock price over time.

 

In practice, however, many loyalty finance and marketing professionals have difficulty quantifying this impact, which makes it hard to prove that a loyalty program is indeed creating value. Fortunately, most companies already have the necessary data to measure loyalty program value with more precision. When the data is used more effectively, it becomes possible to communicate program value to your company’s stakeholders in a language everyone understands.  

 

For the finance team

 

In any company, the finance team is responsible for measuring financial performance and identifying ways to maximize firm value through financial planning and investment decisions. Since loyalty programs directly influence company value, finance teams can benefit from concrete data and methods that allow them to make better decisions.

 

One of the most common methods for quantifying firm value is the discounted cash flow (DCF) approach, which says that a company’s value is the present value of its expected future cash flows. Of course, the more predictable a firm’s future cash flows, the more credible the present value calculation.

 

Some business models have more predictable cash flows than others (subscription-based models in which customers periodically pay a predetermined fee is a prime example). Businesses with active, healthy loyalty programs also provide a wealth of insight into a company’s future cash flow. Why? Because most of their future cash flow comes from current program members.

 

Taking a step back for a moment, let’s see why the relationship between loyalty program member behavior and a company’s future cash flow holds true. You can apply a DCF-like model to measure the value of a loyalty program by estimating customer future value (CFV), which is the present value of estimated future free cash flow produced by each program member — more precisely, expected future revenues less expected future costs. Summing CFV across all loyalty program members gives you the present value of your program members, also known as member equity.

 

The Pareto Principle states that on average, roughly 20% of a business’s customers account for 80% of its revenues. While the actual breakdown varies depending on your business model, your most loyal customers always account for most of your future free cash flow.

 

Therefore, member equity is a good proxy for a large share of the value of your entire organization. And, if customer behavior influences member equity, you can increase organizational value by focusing on the relatively small segment of customers that drive company profits. This notion supports the use of customer-based corporate valuation (CBCV) models.  

 

The underlying concept of the CBCV model is that unit economics is the number one determinant of lasting business success. Put differently, if customer retention and variable profitability are strong while customer acquisition costs remain low, a business is more likely to succeed long-term than it would with operational efficiencies alone. If you understand customer-level profitability dynamics, you can increase company value by investing in acquiring profitable customers and marketing strategies that influence their behavior.  

 

The meal-kit delivery service Blue Apron is a great real-world example of how CBCV models can be used to understand and influence firm value. Although the idea of food delivered directly to your doorstep resonates with many consumers, subscription-based models like Blue Apron have not been as financially successful as food delivery services such as UberEats and GrubHub.

 

By digging deeper into the behavior of Blue Apron’s customer base, company management was able to see that their low customer retention rate combined with rising customer acquisition costs was eroding member equity. Unfortunately, this realization was ill-timed, as Blue Apron’s stock price had already fallen nearly 90 percent since its IPO in mid-2017 — an outcome that Dan McCarthy, a leading CBCV researcher, had predicted using his models. Nevertheless, these insights enabled Blue Apron to redirect firm resources to the portion of customers that drive value.

 

Since loyalty programs provide rich, member-specific transactional data, CBCV models are powerful tools for analyzing trends in customer behavior and diagnosing issues that limit profitability. This type of data analysis allows finance teams to show the loyalty program’s direct impact on organizational value and company stock price.

 

For marketers

 

Since marketing teams are responsible for building and maintaining their company’s customer relationships, they typically lead both strategic and tactical loyalty program initiatives. One of the biggest mistakes companies make is focusing on the near-term cost of such initiatives rather than the long-term value they create. Over time, incremental value can be created even if costs rise. Companies that can quantify this cost-benefit trade-off can more readily determine whether their marketing efforts are successful.

 

To measure the economic value of a proposed marketing initiative, you’ll need an analysis method that clearly shows the trade-off between costs and anticipated benefits — examples of which can be seen below. It’s important to note that the hypothetical loyalty program in these exhibits is relatively small, consisting of only 100,000 members. For larger programs that have millions of members, the resulting member equity projections can be on the order of several billion dollars.   

 

 

 

Like customer-based corporate valuation models, the exhibits above show the relationship between customer future value, member equity, and return on member equity (RME) — the change in customer lifetime value over time. The goal of any marketing team should be to invest in projects that are expected to generate the highest return on member equity. By breaking RME down between current program members and new members, it’s much easier to see whether marketing is productively spending customer acquisition dollars and changing member behavior to create value for the company.

 

While ideally you want to see positive RME for both current and new members, detecting negative RME early can still be beneficial, as it signals a need for intervention. If marketing teams can clearly see that their efforts are diminishing company value, they can then redirect these efforts to projects that are more profitable.

 

 

With the right set of tools, you can track member equity over time, holding the group of included members constant so that the data isn’t distorted by new member acquisitions. An increase in member equity as indicated by an upward sloping line means your RME is positive, and marketing is increasing loyalty program member value above expectations. The ability to visually demonstrate a successful long-term track record of marketing decisions that add to the economic value of the company can be very meaningful when presenting future initiatives to various stakeholders, including the shareholders of the organization.

 

Additionally, companies that can dig further into the components of the change in member equity over time gain a better understanding of changes in future revenue compared to changes in future cost. By seeing the cost-benefit trade-off, marketing can focus on maximizing loyalty program value instead of minimizing cost. This may lead to the execution of profitable projects that marketing would have otherwise avoided.

 

 

Finally, companies can measure the expected impact of their investments in acquiring new customers to develop strategies that focus on acquiring members through the channels with the highest value rather than the lowest cost. The KYROS Dashboard allows for robust customer and member equity analysis so marketing teams can validate the economic value of their efforts.

 

For loyalty program managers

 

Program managers are held accountable for increasing member equity, which some companies refer to as driving incremental value. While there are various methods you can use to measure incremental value, commonly used approaches such as look-alike analysis have shortfalls that prevent you from showing a true picture of loyalty program value.

 

A look-alike analysis splits customers into two groups — those who join the company’s loyalty program and those who do not — and then tracks the differences in each group’s behavior over time. These differences are considered the incremental value. Unfortunately, there are two major shortfalls to this approach:

  1. For most programs, customers who aren’t enrolled simply cannot have their behavior monitored. 
  2. It is accompanied by self-selection bias — that is, customers who choose to join are inherently more likely to continue spending with a company than those who do not.

 

Since these shortfalls tend to result in unrealistic estimates of the program’s incremental value, companies need a better way to measure the effectiveness of program management. A different — and arguably more accurate — way to measure incremental value is by calculating return on member equity (RME). RME is similar to look-alike analysis in that it compares member behavior to a benchmark. However, the benchmark in this case is status quo member behavior — in other words, expected behavior if members continue to follow their current behavioral trend.

 

Status quo behavior tends to be more relevant and easier to track than the behavior of customers who choose not to join the company’s loyalty program, making it a more informative baseline. Under this framework, program managers can present more compelling proof of the economic value their loyalty program creates for the organization.    

 

The bottom line

Loyalty programs are complicated engines with many moving parts that can drive company growth significantly if managed properly. A well-run loyalty program can dramatically increase the economic value of an organization as continuous investment in the right customer acquisition channels and marketing strategies drives profitability. However, to remain viable, each of the teams responsible for the program’s success—finance, marketing, and program management — must convincingly convey their value-add to senior executives, as well as the company’s shareholders.

 

You can only prove the value of a loyalty program if you have reliable data and the right measurement tools. Luckily, most companies already have the data they need, it simply needs to be leveraged more effectively. By employing the methods we’ve described above, you can not only measure your loyalty program’s value with more precision, you can use the insights you gain to make better decisions moving forward.

 

 

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

 

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.

 

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.

 

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?

 

 

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

 

Customer loyalty, predicted

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Customer loyalty, predicted

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Customer loyalty, predicted

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Customer loyalty, predicted

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