To help understand the challenges that loyalty programs face when trying to optimize the tradeoff between breakage and engagement, we spoke with Len Llaguno, Founder and CEO of SnapShotML, a firm providing a financial measurement for loyalty programs.
Len has spent nearly ten years helping many of the world’s largest loyalty programs manage program liabilities worth several billion dollars. Len works with finance and loyalty teams to address the challenge that breakage drives profit today, but hurts long-term engagement.
Let’s explore how Big Data can assist Loyalty and Finance teams with this challenge…
Big Data is a popular topic. How do you think being data-driven will benefit loyalty programs from a financial management perspective as we head into 2018?
- I think being data driven will become even more important for loyalty program financial management in 2018. As an industry, we lack a credible financial measurement framework. A classic example is the question, “how do we manage the engagement/breakage trade off?” That is, we know breakage drives profit today, but hurts engagement and long-term profit. We need a framework to help us answer these types of questions.
- Financial measurement will become more important in 2018 as we see the effects of new accounting standards for programs (ASC 606 in the US and IFRS 15 elsewhere). Liabilities will likely increase, particularly in the US where the accounting changes are more significant.
- These changes don’t affect the underlying economics of programs, but it will likely increase the difficulty for programs to communicate this amidst all the noise. The discussion between Chief Financial Officers and Loyalty Managers won’t be helped without the smart use of data. Some may have an urge to devalue programs, so it will be important to clearly quantify program economics and the long-term profit that would be lost if the program was devalued.
Briefly describe the role big data and analytics plays in managing a loyalty program (strategy/financials etc)?
- Many companies are already doing all kinds of marketing analytics. They are using data and data science to better understand the wants and needs of their members to build a better customer experience. This use case will only continue to grow.
