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    Loyalty Reward Programs: Turning Analysis into Action

    If you're running, managing or overseeing a loyalty program, you're probably looking for an edge. Whether it's to become more profitable or to avoid high risk liability, you're likely seeking opportunities or avoiding mistakes through loyalty reward program analysis. While you're right to be analyzing your membership, you need to make sure you're doing it correctly.

    What are you analyzing?

    You might be looking at spending behavior and keeping track of your membership but you're likely not optimizing your analysis potential. In fact, it's almost impossible to — without help. However, with the assistance of predictive analytics, you'll be able to analyze and fully understand:

    Once you've enlisted predictive analytics — you're ready to put analysis into action. But there are a few things you'll need to remember:

    Breakage is Relative

    Breakage is the percentage of outstanding points that will ultimately go unredeemed. It's important to accurately assess your program's breakage, which is vital to its health. However, you should remember that breakage is relative; it should be considered with other metrics in mind.

    If your breakage is low (lots of points are being redeemed) but your predictive Customer Lifetime Value (the amount of money a customer has spent and will spend in the future) has increased, your program is becoming more profitable. If you look at breakage alone, those numbers will provide an inaccurate picture of your performance.

    This is why you should never present breakage to shareholders, stakeholders or anyone else, without including other metrics like CLV. With predictive analytics, you'll be able to see the whole picture.

    The 80/20 Rule

    With CLV in mind, you'll want to remember the 80/20 rule: 80% of your program's profit comes from 20% of its members. Moreover, this 20% won't always be classified as part of your top-tier membership. Identifying members with high CLV will be crucial to your loyalty program's health.

    However, identifying these members won't always be simple. As mentioned above, your program's members with the highest CLV may sometimes be located in lower tiers of your membership. Without predictive analytics they'll be difficult to identify, but with help, it will be a breeze.

    Incentivize the Right Members (Uplift)

    It's important to remember that your members are your assets and you should treat them as such. Once you've estimated each member's CLV, you'll want to take action to make them even more profitable — just as you would with any other 'assets' in your possession.

    That's why you should be incentivizing those that have the highest uplift, or likelihood to increase their future profit when given the right stimulus. For example, use a predictive platform (like KYROS) to identify these individual members, and use their spending behavior to deliver exactly what they want when they want it. This group of members gives you the most bang for your buck, helping you drive top line revenue and maximize ROI on your incentive strategy. By continually uplifting the right members, your program will only become more profitable in the future.

    The Action?

    So, here's your edge: predictive analytics. Your program will be healthier, more profitable and less opaque once you're analyzing its most important aspects in the right way. Not only will your program add more to the bottom line, but it will have a much happier membership as well. With predictive analytics you'll be able to turn analysis into action.

    To learn how to choose the right predictive software check out, What to Look For in a Loyalty Rewards Program Software.

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    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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