Loyalty Program Usage

Marketing Marketing / Sales Churn/Retention Decrease Costs Increase Revenue
Personalize redemption recommendations in loyalty programs, resulting in increased consumer usage and engagement.
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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.

Solution

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

The DataRobot AI Platform rapidly builds accurate and agile models that predict members’ redemption preferences with just one click.

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