Payback time: What your CFO wants to know about your loyalty rewards program

Advanced analytics techniques are enhancing loyalty program investment cases and helping to more consistently blend financial and marketing objectives.

Most customers love being rewarded for brand loyalty, and businesses love loyal customers. It’s a simple formula that has seen loyalty programs multiply and expand across a whole range of industries over a period of many years.

Investments in loyalty programs can therefore now generate some big numbers – unredeemed points, for example, represent a liability of billions of dollars in some programs. And like any other kind of prospective financial commitment, CFOs are interested in the balance between risk and reward. For loyalty programs, that balance largely depends on predicting member revenue, how members earn and redeem points, the role of affinity partners and suchlike. The methods for doing these things are moving on.

A shift in emphasis

Most loyalty programs already crunch a lot of historical data. But, typically, sponsors have reserved more sophisticated analytic tools for marketing cases that explore short-term, discrete behaviors, such as near-term lapse probabilities or conversion rates.

Fewer companies, in my experience, have applied the same analytic rigor to financial analytics, such as estimating the program liability and performing short- and long-term financial projections of member revenue and cost. Many annual and even three-to-five year financial forecasts for member revenue and costs, for example, are typically based on aggregated data that throw little light on member behaviors that drive financial performance.

By contrast, modern predictive modeling and data science techniques can help companies understand better how member’s behavior today drives long-term financial value. With this knowledge, financial analytics becomes a super-charged tool to both monitor and maximize financial performance.

How do these techniques differ?

These newer generation analytics work by more forensically and comprehensively assessing the cause and effect of decisions than aggregated historical data ever could. So, if you had 20 pieces of distinct information about a customer or set of customers, you could look at the impact of every combination of those items.  And with things like big data and machine learning techniques increasingly coming on stream, the type and speed of analyses are moving on rapidly.

In the case of unredeemed points,  newer predictive analytics can add a level of sophistication to customer financial models, shedding light on considerations such as when points are redeemed and what factors increase or decrease redemptions. This deeper understanding of the mechanics gives a more solid base for liability estimates and the financial reporting decisions based upon them, not to mention a means for more proactively influencing redemption.

Equally on the revenue side, more sophisticated financial models can reveal a deeper level of behavioral insights that would allow a company to tailor a program to maximize long-term customer value. One of the key things new generation predictive analytics enable sponsors to do is to easily explore what-if scenarios. For example, a sponsor could test the financial impact of possible program changes on performance, including revisions to expiration/forfeiture rules. They might also explore how to drive more value from bonus offers. The availability of off-the-shelf software tools, including our own LoyaltyAdvisor, means this process can be highly automated, so results are produced very quickly, giving sponsors the information they need to make fast and smart business decisions.

Another longer-term, strategic benefit is that the confluence of revenue and cost applications made more readily possible by newer generation analytics helps bridge the potential gap between marketers’ desires to grow engagement and finance’s concerns about cost and liability impacts. And, with that, program leaders may find themselves able to present investments more fluently in the language of sometimes skeptical CFOs.


Related article and video: The cost and value of loyalty


Len Llaguno leads the loyalty analytics team at Willis Towers Watson. Len has worked on a diverse range of insurance and risk management assignments for personal and commercial insurers as well as corporate clients. Len’s background includes traditional reserve, ratemaking and funding studies as well as other quantitative and qualitative analyses to help clients manage and leverage risk.

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