Prior Situation / Scenario:
- Business Problem: increasing number of subscribers with involuntary churn.
- Over 50% of machine learning projects are never put into production due to labor intensive and inconsistent workflows.
- Manual ML model improvement in response to performance deterioration.
- Lack of scalability and high impact of staff turnover due to key-person dependency.
- Estimate daily propensity to involuntary churn (score/prediction) for each customer to improve Retention & Collection campaigns.
- Manage the lifecycle of the ML solution.
- Standardize operationalization.
- Enable or disable functionality remotely without deploying code.
- Automatic and self-healing ML model (retraining).
- Misuse of talent: high amount of manual task and low business impactful tasks.
Strata Solution/ Key Enablers:
- High-performance leading-edge ML algorithm portfolio
- ML Solution as a Product
- Deployed in customer cloud account
- ML Model Development and Operationalization Manual
- Serverless and Event Driven System
- Agile development: MVP + incremental functionalities
- High scalability and high availability architecture
- Delivered as a product with capabilities to: Data Process, Prediction, Monitoring, Automatic Retraining (model optimization).
- Time to market: one click deployment into production.
- Agile improvements: granular releases into production (MVP)
- Standardized, easy control, operation and maintenance of ML Solution
- 10% increase in Retention & Collection campaigns.
- 90% reduction in operational manual tasks and unintentional errors.
- 70% less effort in later releases (new markets) due to replicable and scalable architecture.
- 100% focus of the analytical team on model performance development and improvement (effective use of talent).
- 95% reduction in time to deployment.