Building a distributed data-stream platform for direct marketing
Architected an open-source streaming stack that powers real-time targeting and predictive modeling across marketing channels.
Streaming sources integrated
10+
Latency to action
< 5s
Tooling stack
WEKA, MOA, SAMOA, Spark
Overview
Marketing organizations wanted to operationalize CRISP-DM workflows on live data streams without proprietary lock-in.
Our researchers proposed a reference architecture that combines open-source tools across the modeling lifecycle.
Challenges
- Multi-source streams required scalable ingestion and feature engineering.
- Teams needed interoperability between batch analytics and online learning.
- Solutions had to be deployable on modest infrastructure budgets.
Approach
Modular streaming architecture
Integrated Storm/S4 stream processors with MOA and SAMOA for online learning and Spark for fast analytics.
CRISP-DM alignment
Mapped ingestion, preparation, modeling, and deployment steps to streaming-friendly components and governance.
Domain-specific feature design
Curated RFM and campaign attributes that generalize across direct marketing use cases.
Impact delivered
- Enabled real-time targeting and response prediction on continuous marketing data.
- Delivered a repeatable, open-source blueprint for scaling predictive marketing systems.
- Demonstrated interoperability between batch and streaming analytics for marketing teams.
Key lessons
- Streaming architectures succeed when they balance open tooling with operational rigor.
- CRISP-DM remains relevant when adapted thoughtfully to real-time contexts.
- Feature design for marketing streams should prioritize portability across campaigns.
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