Introduction
To improve AllTrails' trail difficulty ratings for accuracy and personalization, we need to delve deep into user behavior, data collection methods, and innovative technologies. I'll outline a comprehensive approach to enhance this critical feature, focusing on user needs and leveraging data-driven insights.
Step 1
Clarifying Questions (5 mins)
Why it matters: This helps us understand the scale of impact and prioritize features based on usage patterns. Expected answer: Around 35 million active users, with an average of 2-3 trail searches per month. Impact on approach: High user count would prioritize scalable solutions, while frequent usage suggests investing in personalization.
Why it matters: Identifies potential gaps in data collection and areas for improvement. Expected answer: Combination of user-submitted data, GPS tracking, and manual curation by staff. Impact on approach: Would focus on enhancing data accuracy and expanding data sources if current methods are limited.
Why it matters: Helps prioritize improvements based on user pain points and satisfaction levels. Expected answer: Mixed feedback, with 60% satisfaction rate and common complaints about inaccuracies for certain trail types. Impact on approach: Would target specific trail types or user segments with lower satisfaction for immediate improvements.
Why it matters: Identifies areas where we can further differentiate and innovate. Expected answer: More comprehensive coverage but less personalization compared to niche competitors. Impact on approach: Would focus on leveraging our extensive data for more personalized and accurate ratings.
Tip
At this point, I'd like to take a 1-minute break to organize my thoughts before diving into the next step.
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