Engineer Predictive Analytics (ML) Capability Proof of Value (POV)
Objective
Engineer a Machine Learning system that seamlessly ingests and processes event stream data from Adobe Analytics platform into a purpose-built data lake architecture. Develop the ability to perform advanced analytics and machine learning algorithms to generate comprehensive predictive models focused on three key business outcomes: accurate lead scoring to identify high-potential prospects, intelligent next-best-conversation predictions to optimise customer interactions, and proactive churn risk alerts to enable timely intervention strategies.
Approach
- Stakeholder management and consensus for solutions
- Enable capability and build solutions
- Test and validate solutions
- Run continuous improvement workflow
- Provide ongoing solution support
- Run business review/return on investment sessions
Outcome
The project delivered a solution that ingested and processed high-volume data streams from Adobe Analytics into AWS, where the data was transformed to enable ML model training and execution. This enabled the organisation to explore predictive analytics’ potential value and provided tangible results for calculating return on investment.
- Achieved high accuracy in lead scoring predictions, resulting in a 12% improvement in sales conversion rates
- Implemented next-best-conversation model with high confidence, leading to a 25% increase in customer engagement metrics
- Deployed churn prediction system that identified at-risk customers, enabling proactive retention strategies that reduced churn by 30%
Technical Achievements:
- Engineered scalable data pipeline processing 1M+ daily events from Adobe Analytics
- Built efficient data lake architecture on AWS with automated ETL workflows
- Implemented model scoring with fast response times
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