Introduction
To optimize Wolt's in-app search functionality for faster item discovery, we need to analyze user behavior, identify pain points, and implement targeted improvements. I'll approach this by examining user segments, analyzing the current search experience, and proposing data-driven solutions. Let's dive in.
Step 1
Clarifying Questions (5 mins)
Why it matters: Helps focus our optimization efforts on high-impact areas Expected answer: Restaurant discovery, specific dish searches, and dietary preference filtering Impact on approach: Would prioritize improvements in these key areas
Why it matters: Identifies opportunities for competitive advantage Expected answer: Strong in certain markets, but search lags behind some global competitors Impact on approach: Would focus on innovative features to leapfrog competition
Why it matters: Determines the potential for AI-driven improvements Expected answer: Basic personalization based on order history, but room for improvement Impact on approach: Would explore advanced machine learning techniques for search optimization
Why it matters: Ensures our solution is scalable across diverse markets Expected answer: Significant variations in search behavior across regions Impact on approach: Would incorporate flexibility for local customization in our solution
At this point, you can ask interviewer to take a 1-minute break to organize your thoughts before diving into the next step.
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