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Customer Loyalty is a key ingredient to success for businesses around the world. Implementing programs that reward returning customers is a constant risk-reward balancing act that can be hard to get right — especially without a foundational understanding of the customer loyalty analytics that help you measure and optimize your loyalty programs and strategies.
Here are 4 things you absolutely need to know about customer loyalty analytics
An 'attitudinally' loyal customer is a fan, promoter or advocate for a company that may or may not buy from the company regularly. These folks will wear your logo, recommend your brand and have a distinctly positive impression of your services.
To put it simply, a 'behaviorally' loyal person buys from the same vendor regardless of their adoration of the vendor's brand. For example: You may live in an area with a single telecom provider. Their rates are too high and you can't stand their customer service, but you renew your contract annually because they're your only option to keep your internet service.
The most common tactic for measuring attitudinal loyalty among consumers is NPS scoring. A simple scale that measures a customer's likelihood to promote your company to others, an NPS score provides a solid baseline of customer satisfaction and brand appreciation — especially when averaged across a large set of customers.
Two views can be taken when measuring behavior loyalty: a historic approach that examines metrics such as:
Since a loyalty program's goal is to change behavior and make members more loyal over time, the performance of a program can be measured by an upward trend in these indicators.
Reporting on historical trends is relatively easy, but here's where the approach falls short: it tells you what people did rather than what they're going to do. Historical views into your program offer little strategic insight into how you can drive future value and increase the effectiveness of your program going forward.
But what about a predictive approach to loyalty analytics?
Making data-driven predictions about the expected future profit that a loyalty program member will generate helps companies to predict 'customer lifetime values' — or CLVs — a key building block in any predictive loyalty analytics strategy.
This prescriptive view gives you a mechanism to anticipate future behaviors and value of your program members and makes your customer loyalty analytics strategy (and its impact on your bottom line) more robust in the long run.
Each and every customer contributes to the growth and success of your business. But from a loyalty program perspective, they're not all of equal importance.
Some customers in your program are far more valuable than others — in fact, it's typical for around 20% of members to drive up to 80% of future profits. Customer loyalty analytics help you identify this key minority, allowing you to take actions today that maximize the likelihood that you'll unlock their value.
Analytics also enable you to go beyond the current high-value members and identify those with the potential for future increases in customer value. Spotting these members early on enables you to plan your marketing budget more effectively to drive up program ROI.
Once these key members have been identified, the way you target members also becomes informed by your loyalty analytics. What elements of the rewards program are they most attracted to? What discounts, services or options are they lost likely to utilize? A well-built customer loyalty analytics tool will guide targeting efforts in the right direction.
Loyalty Program experts like David Slavick will tell you that the true value of predictive loyalty analytics comes when you deploy these models at scale. Some examples include the automated targeting of:
Getting the operations in place to keep prediction models up to date can be a challenge, even for folks who specialize in loyalty program finance.
That's where KYROS can help. We strengthen your loyalty program finance and accounting teams by analyzing loyalty program data, building a predictive model and providing a user-friendly dashboard with ongoing reporting capabilities.
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.