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    Loyalty Program Finance: The Struggle for Progress

    In my previous articles, we talked about some of the struggles loyalty marketers and accountants face when booking loyalty program liabilities.

    Now we turn our attention to loyalty finance professionals, who face significant pressure to ensure accurate liability and solid loyalty program ROI.

    Unfortunately, there are not many resources for finance professionals that support loyalty programs. This makes a loyalty finance professional’s struggle for progress very challenging.

    In this article, we’ll look closer at the progress loyalty finance professionals aim for, along with the obstacles often face.



    The progress loyalty finance professionals desire

    Like most people in finance, a loyalty finance professional strives for stable and predictable financial results. This means that they need to accurately forecast the loyalty program’s financial performance.

    These predictions include how the loyalty program liability will emerge, the cash flow and revenue recognition from the redemption of points, member acquisition costs, the profit contribution from member spend and more.

    Additionally, forecasts must be made over both the short and long term, performance against forecasts must be monitored, and any variance explained.

    The more strategic loyalty finance professional will also think about optimizing the financial value of the loyalty program, along with the most efficient way to do so — that is, they want to participate in discussions about how to optimize customer lifetime value and incremental lift.



    The challenges faced

    Loyalty finance professionals face a number of obstacles to predicting accurate liability and realizing positive loyalty program ROI. In this section, we’ll explore the hurdles many loyalty program financial professionals face, including:

    • Demands by Wall Street for accurate financial forecasts
    • Evolving loyalty program marketing strategies
    • Disconnect between cost and benefit realization
    • Technical challenges of building and implementing predictive models

    Challenge 1: Wall Street demands accurate financial forecasts

    One of the biggest challenges finance professionals face is the pressure from Wall Street to meet expected financial results. This puts enormous importance on managing expectations. Furthermore, this issue is amplified because of the leveraged financial risk associated with loyalty program liability.

    It’s common for liabilities to reach hundreds of millions — or even several billion — dollars. At this scale, even a small percentage change in liability will have a leveraged effect and can drive a large variance in financial results.

    Challenge 2: Evolving loyalty program marketing strategies

    In order to drive the most revenue, marketing is constantly trying to implement programs and campaigns that drive high engagement. If successful, these programs will impact member behavior, affecting point earning, point redemptions and future spending.

    They will also directly impact financial results. The constant change and fluidity of program management decisions make it very challenging for loyalty finance professionals to translate marketing strategies into an accurate prediction of financial performance.

    Challenge 3: Disconnect between cost and benefit realization

    The nature of accounting rules adds another layer of complexity. Campaigns to drive an increase in engagement will also drive immediate cost increases, since an increase in point earning or expected future redemption behavior will hit financial statements in the form of a change in program liability.

    Unfortunately, expected growth in the future profit contribution from your members is not an asset you can recognize on the balance sheet. The short term implication is that you recognize all the costs and none of the benefits. This means smaller profit in the short term. Untangling the impact of this timing on financial statements can be tough.

    Challenge 4: Technical challenges of building and implementing predictive models

    The loyalty finance professional is asked to predict customer lifetime value, incremental lift and how financial performance will emerge and impact financial statements. Needless to say, these are complex modeling exercises to tackle!

    Most organizations struggle with predictive models, mainly because trying to make predictions over the long term is difficult. So, they default to predicting behavior over shorter periods. For loyalty finance professionals interested in optimizing the financial value of the program, the “short horizon” limits the set of acceptable opportunities to only those that generate return quickly. These are likely few and far between.

    It’s important to understand that there are more opportunities to generate return over the long term. Unfortunately, most professionals don’t yet have the ability to evaluate them.

    Finally, there remains the struggle to have fresh and relevant information to support the ongoing business. Operationalizing models to continually score new data as it emerges is critical for loyalty finance, so all decisions can be based on the latest, most relevant sets of data. This is possible, but it comes with several implementation and management challenges, such as: securing the right IT, data science, data security and developer resources to build a sophisticated ETL pipeline; managing this cross-departmental team; and maintaining this infrastructure and complex set of models as business needs and data evolves.


    How to make progress in loyalty finance

    Regardless of whether you’re trying to translate marketing strategies into financial implications, set expectations for the emergence of financial statements, or uncover ways to optimize the economic value of the program, all of these needs have a common thread: the ability to predict member behavior over short and long horizons.

    The right kind of predictive analytics tools make this easy.

    They seamlessly incorporate ever-evolving marketing campaigns into your predictions, allowing you to test specific scenarios and vet the financial implications of these strategies, so you can feel confident that you are developing smart marketing and financial strategies.

    They allow you to evaluate the tradeoff between short vs. long-term decision making, giving leadership the information required to disarm short term pressures.

    They show an accurate estimate of how financial statements are going to emerge over the next 12 to 24 months, continually updating so you can monitor and mine the data.

    Ongoing management of expectations about financial results will become easier. Telling the story behind the variances will become easier.

    Best of all, you’ll have the ability to uncover the behaviors that drive financial performance. And you’ll be armed with the knowledge required to help your organization optimize the financial health of its loyalty program.

    Suddenly, financially planning and expectation-setting becomes a whole lot easier.


    For more information on loyalty program liabilities, check out What is loyalty program liability? and The 2nd Question to Answer if You Manage Loyalty Program Finances.




    Len Llaguno

    Founder and managing partner of KYROS Insights. I'm an analytics nerd and recovering actuary. I use machine learning to help loyalty programs predict member behavior so they can identify their future best customers, and recognize and reward them today.


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