Introduction
To improve Gett's ride-tracking feature for more accurate ETAs, we need to analyze the current system, identify pain points, and develop innovative solutions. I'll approach this by examining user segments, analyzing pain points, generating solutions, and proposing metrics for success. Let's dive in.
Step 1
Clarifying Questions
Why it matters: This helps understand the volume of data available for improving ETA accuracy. Expected answer: Around 100,000 rides daily across major cities. Impact on approach: Higher volume suggests we can leverage big data and machine learning for improvements.
Why it matters: Establishes a baseline for improvement and helps prioritize efforts. Expected answer: 70-80% of rides arrive within 2 minutes of the estimated time. Impact on approach: Lower accuracy might require fundamental changes to the prediction algorithm.
Why it matters: Identifies potential areas for data enrichment to improve accuracy. Expected answer: GPS, historical traffic data, and basic driver behavior metrics. Impact on approach: Limited data sources would suggest exploring additional inputs for more accurate predictions.
Why it matters: Helps focus improvements on the most impactful areas for user satisfaction. Expected answer: ETAs often underestimate actual arrival times, especially during peak hours. Impact on approach: Would prioritize solutions that address peak hour accuracy and manage user expectations.
Now that we've gathered some crucial information, let's take a minute to organize our thoughts before moving on to user segmentation.
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