Loyalty Program Usage

Predict which offers and program elements will encourage customers to use your loyalty program, boosting customer engagement and satisfaction.
Personalize redemption recommendations in loyalty schemes, resulting in increased consumer usage and engagement.


Loyalty programs are designed to improve customer engagement and reduce customer churn, but they are only effective when customers are actively participating. Choosing the best content and redemption offers for loyalty schemes gets program members more active and engaged, but it is difficult to know which activities will be effective.


With machine learning, companies personalize redemption recommendations in loyalty schemes, resulting in increased point redemptions, more fulfilling experiences, and a more active membership base. For example, models predict the types of people that are more likely to travel, the types of travel people are likely to undertake, the prices that travellers are willing to pay, the importance of accommodation relative to travel, and the importance of experience compared to travel, all of which allows travel companies to tailor offerings and loyalty programs for maximum engagement and use.

Why DataRobot

DataRobot’s automated machine learning platform rapidly builds accurate and agile models that predict members’ redemption preferences with just one click.