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.

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.

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