Selling a loyalty program to a CFO might seem like a daunting task, but advanced valuation techniques can help assist in these investment cases.

Len Llaguno, a senior consultant at Willis Towers Watson, plans to talk about this challenging scenario during a session titled, “3 Ways to Convince Your CFO to Invest More in Loyalty,” at the 10th annual Loyalty Expo set for May 2-4, 2017, at the Caribe Royale Orlando in Orlando, FL.

Presented by Loyalty360 – Association for Customer Loyalty, Loyalty Expo has earned the reputation of being one of the industry’s premier events.

Llaguno offered Loyalty360 a sneak preview of his session at Loyalty Expo.

Your session is titled, “3 Ways to Convince Your CFO to Invest More in Loyalty.” What does it take to get buy-in from the CFO in your opinion?

Llaguno: Like any other kind of prospective company investment, 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. These days, loyalty programs can involve some big numbers either way: Unredeemed points, for example, represent a liability of billions of dollars in some programs.

I’ll be making the case for using advanced predictive analytics in programs because they not only help drive value, but they allow sponsors to make the more robust, accurate and reliable forecasts that boards increasingly need to see before signing off an investment, given the sums involved.

How does this represent a shift from how sponsors might already be using analytics?

Llaguno: Most companies crunch a lot of historical data. But, in my experience, the more sophisticated analytic tools are applied to marketing cases that explore short-term, discrete behaviors. These behaviors might include near-term lapse probabilities or conversion rates, or customer responses to customized communications and offers.

Fewer companies apply 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. For example, we often see companies estimating their program liabilities using very dated techniques – some that have been around for 40 years. We also see companies building annual and even three-to-five year financial forecasts for member revenue and costs, but these typically are based on aggregated data that throw little light on member behaviors that drive financial performance.

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

How do these valuation methods work?

Llaguno: Without getting too technical, newer generation analytics, more forensically and comprehensively, assess 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.

Can you put that in practical terms for loyalty program sponsors?

Llaguno: OK, let’s consider unredeemed points. Newer predictive analytics 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.

On the revenue side, more sophisticated financial models reveal a deeper level of behavioral insights that would allow a company to tailor a program to maximize long-term customer value. 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. By quantifying the increase in customer lifetime value from the offer, they could identify and target members where the lift in value far outweighs the cost of the bonus.

And from a longer-term strategy point-of-view, such applications help bridge the gap between marketers’ desires to grow engagement and finance’s concerns about impacts on income.

How could this play out in day-to-day program management?

Llaguno: One of the key things new generation predictive analytics enable sponsors to do is to easily explore what-if scenarios to assess the impact of potential program changes, both from a revenue and risk perspective. There are tools, including our own LoyaltyAdvisor, that are highly automated, so results are produced very quickly, giving sponsors the information they need to make fast and smart business decisions. 

What are the three main pieces of practical insight you’d like to have resonate from your session at Loyalty Expo?

Llaguno: Firstly, be ready, willing and able to talk the same language as your board and, particularly, the CFO. Secondly, there are tools out there that can help you do this, and you don’t have to become an actuary or a statistician overnight to make use of them. And thirdly, advanced analytics are just as much about driving revenue and customer lifetime value as they are about understanding cost and liabilities.

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