Industries everywhere are analyzing customer data to identify new opportunities to improve and predict customer engagement. Customer loyalty programs are no exception.

Still, a significant number of customer loyalty programs fall into the trap of using outdated historical modeling to predict future member behavior. This opens up the opportunity for program managers to try new predictive methods, such as advanced predictive analytics and even machine learning algorithms, to help derive more accurate estimations.  

In the race to improve member engagement, predictive analytics can transform seas of data into actionable insights that improve member profitability, personalize the customer experience, and predict future customer value.  

Improve profitability

It might sound like science fiction, but machine learning algorithms can unlock extremely valuable insights about your members.

In the age of big data, customer loyalty programs have a big opportunity: they generate vast amounts of customer data.

And this data can often lead to big profits, too. Recent research by McKinsey illustrates that companies that implement predictive analytics based off of customer data realize a 126% increase in profit margin.

Personalized customer experience

Predictive analytics can also help you personalize member rewards based on demographics, unique characteristics, and purchasing patterns. The more a loyalty program personalizes its member experience, the more likely members are to remain engaged.

Through the personalization of rewards based on member behavior, companies can ensure all types of program members are motivated to interact with the brand, without inundating customers with non-personalized, non-relevant offers.

For example, predictive analytics allows companies to identify which customers will be most receptive to new offers, and when.

In short, an effective loyalty program cultivates active and engaged participants through personalized rewards. This investment appears to be paying off: highly engaged loyalty program members have been shown to purchase more often and spend significantly more.

Forecast customer value

Not every loyalty program member is worth engaging with; some members participate just for sign-up bonuses or one-time promotional deals.

When developing a program strategy, predictive analytics can help you identify which members to focus on. In fact, machine learning algorithms are so precise, they can calculate and identify an individual’s customer lifetime value (CLV) in order to optimize your loyalty program liability.

Predictive analytic models can also identify which members hold the highest value and why. This allows you to segment and target high value members who will give you the greatest return for dollars spent in your loyalty program.

Predict the future with predictive analytics

Don’t get stuck in the past with unsophisticated modeling tools. Predictive analytics isn’t going away; it’s the future. Early adopters will benefit from understanding their customers faster and easier, resulting in the design of a superior customer experience.