In our first year using InfiniteInsight® we realized a 300% uplift in targeting accuracy.
Senior Analytics Consultant
Tipp24 hits the jackpot with predictive analytics - quadruples performance of marketing campaigns with InfiniteInsight®
- Tipp24, one of Europe's leading lottery brokers, wanted to better understand the customer lifecycle in order to nurture high-value customers, increase up-sell and cross-sell and reduce churn.
- The absence of crucial information about customer behavior, such as whether they were increasing their use of Tipp24 products, decreasing their use, or dropping lottery products altogether, made it difficult to optimize marketing campaigns.
- The company sought an efficient, industrialized approach that could deliver predictive models across all Tipp24's marketing activities, including all the customer channels — direct mail, email and telephone.
- Traditional analytics solutions were difficult to scale.
- Standardized on InfiniteInsight® after comparing its performance to SAS and SPSS.
- Deployed first predictive model in days — compared to weeks with traditional analytics solutions.
- Scaled the solution across all Tipp24's marketing activities, including all the customer channels — direct mail, email and telephone.
- Assigned predictive scores inside the Oracle database that serves as Tipp24's data warehouse, dramatically decreasing the traditional amount of time to put a model into production.
- With the precision made possible by InfiniteInsight®, achieved marketing performance goals while cutting the size of the targeted audience for any individual campaign by 25 percent.
- With InfiniteInsight®, Tipp24 can recognize patterns of customer behavior and use that information to improve customer satisfaction.
- The company can now accurately determine which players are likely to be interested in weekly, monthly, or permanent tickets, and which Lotto players may be interested in playing Euro Millions or other lottery games.
- Tipp24 can also predict which active customers are at risk of becoming inactive and which inactive customers have a high likelihood of becoming active again.
- Relationships with existing high value customers are proactively managed, preventing churn, and the company can identify and cultivate relationships with new high value customers.
- 300% improvement in targeting accuracy.
- Reduced churn.
- Optimized customer lifecycle across multiple channels including telephone, direct mail and email.
- Increased overall customer lifetime value.
- Increased productivity of analytics team.
- Reduced the time it takes to build predictive models by 90%.
- Decreased the time to build and deploy predictive models from weeks to days.