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Executive Summary

This comprehensive workflow plan outlines the implementation of a machine learning solution for customer churn prediction at TechCorp. The project will leverage existing data infrastructure with Azure ML and Python-based models to create an automated prediction system that integrates with current CRM. The 4-month timeline encompasses data preparation, model development, testing, and deployment with allocated resources distributed efficiently across the cross-functional team.

Project Timeline

Phase 1: Data Preparation (3 weeks)

Data collection and cleaning from CRM systems
Feature engineering and dataset finalization
Deliverable: Processed dataset ready for model development

Phase 2: Model Development (5 weeks)

Algorithm selection and baseline model creation
Model optimization and hyperparameter tuning
Deliverable: Trained model with >85% prediction accuracy

Phase 3: Testing & Integration (4 weeks)

Model validation with historical data
API development for CRM integration
Deliverable: Functional API with documentation

Phase 4: Deployment & Monitoring (4 weeks)

Production deployment and system integration
Training for end-users and documentation creation
Deliverable: Fully operational prediction system

AI Tool Recommendations

Data Processing

Azure Data Factory for ETL, Python with Pandas for data cleaning

Model Development

Azure Machine Learning, scikit-learn, TensorFlow for algorithm implementation

Integration & Deployment

Azure Functions for API, Power BI for visualization, GitHub Actions for CI/CD

Alternatives

AWS SageMaker, Google Cloud ML Engine, or on-premises deployment with Docker containers

Resource Allocation

Team Assignments

2 Data Scientists (80% time allocation)
1 ML Engineer (100% time allocation)
1 DevOps Specialist (40% time allocation)
1 Business Analyst (50% time allocation)

Budget Breakdown

Personnel: $120,000 (70%)
Infrastructure & Tools: $35,000 (20%)
Training & Documentation: $8,500 (5%)
Contingency: $8,500 (5%)

Risk Assessment

Data Quality Issues

Mitigation: Early data audit and cleansing process with domain experts

Model Performance Below Target

Mitigation: Prepare ensemble approaches and feature engineering alternatives

Integration Delays

Mitigation: Early API prototyping and progressive testing schedule

Priority Matrix

High Priority

Data quality validation
Core model accuracy
API security implementation

Medium Priority

Dashboard visualization
Model explainability features
Documentation completeness

Quick Wins

Basic prediction API prototype
Sample dashboard for stakeholders

Success Metrics

Quantitative Indicators

Prediction accuracy >85%
False positive rate <10%
System response time <2 seconds
Successful identification of 90% of actual churning customers

Quality Checkpoints

Weekly code reviews and testing reports
Bi-weekly stakeholder demos
Monthly model performance evaluations

Next Steps

Assemble project team and conduct kickoff meeting
Set up development environment and access permissions
Begin data audit and collection process
Establish weekly progress reporting cadence
Finalize detailed technical specifications document

Project commencement date: October 15, 2023

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