Case Study 1: Uber Ratings – Trust Built on Data
Context:
Uber’s success relies heavily on user ratings, which help the platform maintain quality control and customer satisfaction. Every rider and driver gets rated after each trip—this results in millions of data points daily.
Statistical Applications:
- Descriptive Statistics: Mean rating per driver, most frequent scores
- Confidence Intervals: Estimate the average rating of a driver population based on a sample (e.g., drivers in Mumbai with 500+ trips)
- Hypothesis Testing: Is there a significant difference in average ratings between day and night rides?
- Z-score Analysis: Identify outliers (e.g., drivers consistently below 4.0)
Outcome:
Uber uses these insights to:
- Trigger alerts for retraining or warnings
- Reward top-performing drivers
- Improve matching algorithms to ensure better ride experiences
Case Study 2: Food Delivery Apps – Improving Delivery Times
Context:
Apps like Zomato and Swiggy track delivery time data in real-time to ensure prompt service and customer satisfaction. Each delivery adds to a growing pool of data, used for both analysis and operational improvement.
Statistical Applications:
- Inferential Statistics: Estimate average delivery times across cities
- Central Limit Theorem: Justifies that averages from large random samples follow a normal distribution, enabling use of t-tests and z-scores
- T-test: Test if a new routing algorithm significantly reduces delivery time
- Power Analysis: Plan how many deliveries to test before rolling out a new logistics model
- Regression & Correlation: Predict delivery time based on distance, traffic conditions, and restaurant type
Outcome:
- Optimized delivery routes and zones
- Dynamic ETAs shown in apps
- Better resource allocation (e.g., delivery partners per region)
