Imagine scrolling through a hundred menu items at a restaurant—what should you order? That’s where “Best Seller” and “Must Try” tags come in. These data-powered labels help you cut through the clutter and find meals that others love—and that you probably will too.
In this blog, we delve into the technical process behind these tags, highlighting how data-driven insights can enhance your dining experience.
Improve User Experience: Simplify menu navigation by prominently featuring top-performing items.
Increase Engagement: Drive higher-order conversions by recommending popular and high-quality dishes.
Boost Restaurant Sales: Enhance visibility for restaurants’ best items, rewarding quality and consistency.
Ensure Scalability: Design a robust system that can handle large datasets and integrate with diverse order sources.
Maintain Accuracy: Implement rigorous data cleaning and validation to ensure reliable tag assignments.
The Best Seller tag identifies menu items with the highest order volumes at each restaurant, reflecting their widespread popularity. The Must Try tag highlights menu items that combine strong sales with exceptional customer ratings, offering a curated selection of standout dishes. Some items earn both tags, marking them as crowd favorites with top-notch quality.
The system gathers data from customer orders over the past three months, focusing on:
To ensure accuracy, the data undergoes rigorous cleaning:
The tagging logic uses percentile analysis to identify top-performing items:
This approach ensures fairness across restaurants with varying order volumes.
Finally, we batch-update the MySQL database with tags using efficient tab-separated CSV streams.
Tags stored as JSON arrays in Menu_Items.tags : [“BESTSELLER”, “MUST_TRY”]
A simple yet robust function ensures:
Atomic updates per restaurant/item
Logging for rollback and tracking
Hybrid Tagging Workflow :
Every step is logged for transparency, capturing successes and errors with timestamps to ensure reliability.
| Component | Tools/Techniques |
|---|---|
| Data Extraction | SQLAlchemy, MySQL |
| Data Processing | Pandas (percentiles, grouping, cleaning) |
| Statistical Engine | Numpy-based percentile calculations |
| Database Updates | mysql.connector + CSV batch processing |
| Monitoring | Python logging with timestamps |
The menu tagging system transforms chaotic menus into curated culinary journeys. By combining statistical rigor with restaurant-specific adaptability, we ensure every traveler discovers unforgettable meals—whether it’s a bustling station’s best-selling “Special Veg Thali” or hidden gems like:
This isn’t just about tags; it’s about making India’s diverse food heritage discoverable, one railway journey at a time.
Want to read more about how we think and build at Ipsator?
