Data story behind Food delivery Apps

Since I use food delivery apps heavily. Both swiggy, ubereats. My views and reflections of data story/measures/ machine learning use cases from these applications

My observations based on application use. Ubereats highlights below activities based on historical data collected

• Previously ordered restaurants 
• Previously ordered items highlighted
• Review based listings 
• Projecting estimated delivery times

I personally face challenges while trying to shift to a low-calorie diet as recommendations are more tuned for past orders.

• Recommending a similar item every day from other restaurants based on historical data
• No option to set preferences for the coming week - Balanced diet customized to need /preferences based on user choices for a week 
• Fold quality issues exist no matter how good review ratings are

I have worked on real-time systems, reporting and moved to AI. Now we have all tools to query data in motion, historical data and future data forecast. This view provides a complete end to end perspective to understand data, numbers. Some of below metrics/ measure overlap across transactions/ historical data / ai

Key Metrics / Measures

• Average Order delivery time at different times (Morning / Lunch / Evening / Holidays / Weekends)
• Average Order Order pickup time at different times
• Order acceptance rate
• Clicks/ conversions 
• A/B experiment and conversions 
• Payments type vs orders
• Average menu browsing time
• Frequently searched items across days / restaurants / seasons
• Predict order delays using Traffic data
• Peak seller's 
• Top customers 
• Weekday trends
• Top trends based on seasonality 

Data science use cases

• Forecast on volumes of items based on historical data 
• OCR, Recommendation at User Level / Sold together items
• Deep learning for automated food classification, tagging
• Segmenting customers based on Age / Gender / Veg / Non-Veg / Cusine Choices and providing recommendations
• Forecast Order Volumes and assign Delivery partners based on Projected numbers to reduce other delays

Tech Talk - 

Everything that is measurable can be managed, monitored, improved. There have to be more quality aspects to be integrated as we risk ourselves trusting rating for better quality. Hope quality bar keeps improving and story evolves into another version customizing based on personal diet plans and choices. Happy finding the data story behind these food delivery businesses Apps !!!