Problem / Pain

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.

What people say

"We want people within our program to be able to redeem points for great experiences, and to do that, we want to be able to better predict when is the best time for particular people to redeem points and what should they be redeeming them against. I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge."

Oliver Rees, GM of Torque Data at Virgin Australia