ML Marketing Platform
- Customer
- ALTEL/TELE2
- Project manager on the customer side
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- IT Provider
- ALTEL/TELE2
- Year of project completion
- 2024
- Project timeline
- January, 2024 - November, 2024
- Project scope
- 23900 man-hours
- Goals
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The primary goal was to develop an advanced ML Marketing Platform capable of delivering hyper-personalized customer experiences for a base of 9 million subscribers. The project aimed to overcome the limitations of traditional manual personalization methods by implementing AI-driven solutions for automated, scalable customer engagement. Key objectives included reducing churn, increasing conversion rates, maximizing revenue through personalized recommendations, and achieving 8% of core revenue from Customer Value Management (CVM) by 2026. The platform was designed to unify multiple communication channels and optimize customer interactions through data-driven decision-making.
- Project Results
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The implementation delivered significant quantitative and qualitative improvements:
Quantitative Achievements:
• 10-15% reduction in customer churn
• 10-15% increase in conversion rates
• 5-7% growth in revenue per customer
• Generated over $500,000 in advertising B2B service salesQualitative Improvements:
• Enhanced customer satisfaction and loyalty metrics
• Improved data reliability through advanced analytics
• Optimized marketing efficiency and resource allocation
• Established scalable infrastructure for future growth
• Fostered an innovation-driven culture within the organization
The uniqueness of the project
The project's uniqueness lies in its comprehensive integration of cutting-edge AI/ML technologies to create a holistic customer engagement solution. Unlike traditional marketing platforms, it combines reinforcement learning, neural networks, and Large Language Models (LLMs) to deliver truly personalized experiences at scale. The platform stands out through its ability to understand customer interests at a profound level, going beyond conventional behavioral analysis. Its dynamic nature ensures continuous learning and adaptation to changing customer preferences, while causal inference capabilities provide accurate impact analysis for business metrics. The platform's unified approach to managing multiple communication channels while maintaining consistency in customer experience sets new standards in marketing automation.- Used software
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Development and ML Infrastructure:
- Python-based ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Jupyter Notebooks for research and model development
- Custom-built neural network architectures
- ML training infrastructure
- Open Source LLM deployment infrastructure
Data Storage and Processing:
- Apache Kafka for real-time data streaming
- Oracle and PostgreSQL for transactional data
- Clickhouse for analytical data processing
- Qdrant is an Vector Database
DevOps and Deployment:
- Kubernetes clusters for container orchestration
- Docker containers for microservices
- GitLab CI/CD pipelines
- Terraform for infrastructure as code
- Grafana for monitoring
Analytics and Reporting:
- Apache Airflow for ETL workflows
- Custom BI dashboards
- Tableau for visualization
Development Environment:
- Git for version control
- JIRA for project management
- Confluence for documentation
- Development, staging, and production environments
- Code quality analysis tools (SonarQube)
- Difficulty of implementation
- The project faced several significant challenges during implementation. The primary challenge was managing the scale and complexity of personalizing experiences for 9 million subscribers while processing massive amounts of data in real-time. Integration of multiple ML models and ensuring their seamless cooperation presented substantial technical difficulties. The team had to overcome challenges in maintaining consistency across various communication channels while ensuring real-time personalization. Additional complexity came from implementing causal inference mechanisms for accurate impact analysis and developing dynamic model updates to reflect changing customer trends. The project required significant organizational changes, including the transition from manual to automated, data-driven decision-making processes and the adoption of new AI technologies.
- Project Description
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The ML Marketing Platform represents a revolutionary approach to customer engagement, designed to handle the complexity of serving 9 million subscribers with personalized experiences. The platform's architecture consists of five key modules:
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Offer Selection Model: Utilizes advanced ML algorithms to match customers with optimal offers based on their individual preferences and behaviors.
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Analytical Module: Employs causal inference and deep analytics to provide actionable insights and measure campaign effectiveness.
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User Reporting: Delivers comprehensive visibility into customer interactions and campaign performance metrics.
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Campaign Management Module: Orchestrates marketing campaigns across multiple channels while maintaining personalization at scale.
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Audience Distribution Module: Optimizes the delivery of messages across various communication channels (SMS, push, email, in-app).
The platform leverages LLMs for deep customer understanding, creating detailed interest profiles that go beyond traditional demographic and behavioral data. It employs reinforcement learning to continuously optimize customer interactions and neural networks to process complex patterns in customer behavior.
The system's dynamic nature allows it to adapt to changing customer preferences in real-time, ensuring relevance and timeliness in all communications. The platform's sophisticated AI-driven architecture enables it to process massive amounts of data while maintaining the personal touch in customer interactions.
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- Project geography
- The ML Marketing Platform operates across the entire territory of Kazakhstan, serving subscribers in all regions of the country.
- Additional presentations:
- ML_Marketint_Platform_ALTEL_TELE2.pdf