Reducing Order Errors

Approach to Designing a System to Reduce Order Errors

1. Problem Understanding and Framing

• Objective: Develop a machine learning model to minimize incorrect or missing orders on the DoorDash platform.
• Types of Errors:
  • Incorrect orders (wrong items, wrong address, wrong time)
  • Missing orders (orders that are not fulfilled)
• Stakeholders: Customers, Restaurants, DoorDash Operations
• Key Metrics: Customer satisfaction, reduction in customer complaints, improvement in order accuracy, reduction in customer churn

2. Data Collection and Exploration

• User Data: Order history, payment methods, addresses, user interactions
• Restaurant Data: Menu items, operating hours, location, historical order accuracy
• Contextual Data: Time of order, device used, location
• Labeling:
  • 1: Wrong or missing orders
  • 0: Correct orders

3. Feature Engineering

• Categorical Features:
  • User demographics, restaurant type
• Continuous Features:
  • Order value, frequency of orders
• Derived Features:
  • Time since last order, number of items in cart, anomalies in order patterns

4. Model Selection and Training

• Classification Models:
  • Random Forest: Good for handling class imbalance and feature selection
  • XGBoost: Effective for large datasets with gradient boosting
  • Neural Networks: Useful if the dataset is extensive and requires complex pattern recognition
• Training Strategy:
  • Train/validation/test split
  • Hyperparameter optimization (HPO)
  • Cross-validation

5. Model Evaluation

• Metrics:
  • Recall: Proportion of true anomalies detected
  • Precision: Proportion of identified anomalies that are true anomalies
  • F1 Score: Balances recall and precision to provide an overall performance measure
  • AUC-ROC: To determine the optimal threshold for anomaly detection

6. Deployment and Monitoring

• Real-time Anomaly Detection:
  • Implement a system to flag potential incorrect orders during the checkout process
  • Provide warnings or confirmation prompts for unusual orders
• Feedback Loop:
  • Collect data on flagged orders and user interactions to continuously improve model accuracy
• A/B Testing:
  • Gradual deployment to assess impact on order accuracy and customer satisfaction

7. Additional Considerations

• Model Interpretability:
  • Ensure stakeholders understand the model's decision-making process
• Operationalization:
  • Integrate with existing DoorDash systems for seamless real-time intervention
• Customer Experience:
  • Balance between preventing errors and maintaining a smooth user experience

This approach ensures a comprehensive strategy to tackle the issue of order errors, leveraging data-driven insights and machine learning to enhance the reliability and efficiency of the platform.