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How Dialog Axiata used Amazon SageMaker to scale ML models in production with AI Factory and reduced customer churn within 3 months
- Dialog Axiata implemented a churn prediction model to reduce customer churn based on demographic and network usage data of customers using Amazon SageMaker
- The model was trained using nearly 100 features across ten distinct areas to predict churn 45 days in advance
- The model uses two essential models - a CatBoost model and an ensemble model trained to capture additional insights and patterns
- Both the training and inference pipelines are run three times per month to maintain accurate predictions and reduce customer churn rates
- Features are sourced from Amazon SageMaker Feature Store, to accurately identify customers at a higher risk of churning
- The churn prediction solution implemented by Dialog Axiata resulted in a substantial reduction in month-over-month gross churn rates within a span of 5 months
- The AI Factory framework and SageMaker were instrumental in designing and deploying the end-to-end AI/ML system life cycle in the AWS cloud environment
- Dialog Axiata's MLOps process involves using SageMaker as their core ML platform to perform feature engineering, and train and deploy models in production
- Dialog Axiata witnessed remarkable business outcomes, showcasing the power of innovative solutions and the seamless integration of AI technologies
- The churn prediction model developed by Dialog Axiata underscores the crucial role of predictive analytics in staying ahead in the competitive telecom industry
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