Here's how you could approach analyzing the increase in order cancellations as a data scientist at Uber Eats:
1. Clarifying Questions:
What is the timeframe over which the 15% increase in order cancellations occurred?
Is this increase consistent across all markets or specific to certain regions?
Have there been any recent changes or updates to the Uber Eats app or platform?
Are there any specific types of orders (e.g., time-sensitive, large orders) that are experiencing higher cancellations?
2. Product Description and Objective:
Uber Eats is a food delivery platform that connects users with restaurants for meal delivery. The objective is to identify the root causes of the 15% increase in order cancellations and implement solutions to reduce cancellations while improving user satisfaction.
3. Hypotheses:
Restaurant Delays: Restaurants are experiencing longer preparation times, leading to increased cancellations.
App Performance: Technical issues or app crashes are causing users to cancel orders.
Delivery Time Accuracy: Estimated delivery times are inaccurate, leading to user frustration and cancellations.
Order Customization Complexity: Users might be finding it difficult to customize their orders, leading to cancellations.
Delivery Partner Availability: A shortage of available delivery partners is causing delays and cancellations.
Pricing Changes: Recent changes in pricing or delivery fees might be discouraging users from completing orders.
Competition: Increased competition from other food delivery platforms could be impacting order volumes and cancellations.
4. Operationalizing Hypotheses:
1. Restaurant Delays:
Analyze data on average restaurant preparation times.
Visualize preparation time distribution and identify outliers.
2. App Performance:
Monitor app performance metrics (crashes, load times).
Correlate cancellations with app performance incidents.
3. Delivery Time Accuracy:
Compare estimated vs. actual delivery times for cancelled orders.
Plot delivery time discrepancies over time.
4. Order Customization Complexity:
Gather user feedback on order customization process.
Analyze user interactions with customization options.
5. Delivery Partner Availability:
Track delivery partner availability during peak hours.
Plot cancellations against available delivery partners.
6. Pricing Changes:
Compare cancellation rates before and after pricing changes.
Segment cancellations based on order value.
7. Competition:
Monitor changes in market share and user engagement for competing platforms.
Analyze the impact of competitors' promotions or features.
5. Conclusion and Recommendation:
Based on the analysis, the top hypotheses to investigate further are:
Restaurant Delays
App Performance
Delivery Time Accuracy
Further investigations could involve collaborating with restaurant partners to improve efficiency, conducting app performance optimizations, and refining the estimation algorithm for delivery times. Regular monitoring and iterative improvements should be implemented to address these issues and reduce order cancellations, thereby enhancing the overall user experience on Uber Eats.