Introduction
To enhance Delhivery's real-time tracking system for more accurate ETAs, we need to analyze the current system, identify pain points, and develop innovative solutions. I'll outline a comprehensive approach to address this challenge, focusing on user needs, technological improvements, and data-driven decision-making.
Step 1
Clarifying Questions (5 mins)
Why it matters: Determines the complexity of the ETA prediction problem and potential solutions. Expected answer: Millions of daily deliveries across diverse regions in India. Impact on approach: Would focus on scalable, AI-driven solutions that can handle high volumes and geographic variability.
Why it matters: Identifies potential gaps in data collection and opportunities for improvement. Expected answer: GPS data from delivery vehicles, historical delivery times, and basic route information. Impact on approach: Would explore integrating additional data sources like real-time traffic, weather conditions, and package-specific attributes.
Why it matters: Establishes a baseline and helps set realistic improvement goals. Expected answer: Current margin of error is +/- 2 hours, aiming to reduce it to +/- 30 minutes. Impact on approach: Would focus on incremental improvements and managing user expectations through transparent communication.
Why it matters: Helps identify areas for differentiation and prioritize improvements. Expected answer: On par with most competitors, but lacking some advanced features like live map tracking. Impact on approach: Would prioritize innovative features that set Delhivery apart while improving core accuracy.
Tip
Let's take a quick minute to organize our thoughts before moving on to user segmentation.
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