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Optimove Builds Real-time Customer Data Platform with Always-Up-To-Date Customer Views using Confluent Cloud
Confluent Cloud is the core infrastructure that is enabling us to realize our vision of having a single, always-up-to-date view of customer data and our vision of real-time self-optimizing journeys, which will maximize uplift by identifying the optimal campaign for each customer.
Challenge
Expand the real-time customer data platform capabilities of an industry-leading Relationship Marketing Hub
Solution
Use real-time event streaming with Confluent Cloud and Apache Kafka to maintain always-up-to-date views of customer data and support machine learning algorithms that maximize campaign effectiveness
Results
- New business opportunities opened
- Stress on external IT systems cut by 90%
- Administrative overhead reduced by one FTE
Optimove, the Science-First Relationship Marketing Hub, helps customer-centric businesses drive growth using its real-time customer data platform (CDP) that ingests customer data from multiple sources and transforms it autonomously into actionable insights at scale. Optimove combines predictive customer analytics, AI optimization technologies, and a multichannel campaign orchestration engine, enabling marketers to consistently target customers with the right message at the right time via the most effective channels.
With the recent acquisition of Axonite, Optimove has expanded the real-time data capabilities of its platform with an event streaming framework based on Confluent Cloud and Apache Kafka. This event-driven architecture is enabling Optimove to offer its clients real-time customer 360 data synchronization between the Optimove platform and customer systems, real-time self-optimizing journeys that take into account the predicted uplift of various campaigns for each customer, and event-based triggers that activate campaigns based on external events, such as a sold-out product being back in stock.
More Customer Stories
Confluent Cloud to save time and money by reducing operational overhead and allowing for real-time processing and easy scalability of event data.
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