For Coveo's commerce search solution, should we emphasize faster query response times or more comprehensive product attribute filtering options?

Introduction

For Coveo's commerce search solution, we're facing a critical trade-off between faster query response times and more comprehensive product attribute filtering options. This decision will significantly impact user experience, conversion rates, and overall platform performance. I'll analyze this trade-off by examining key metrics, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming this is for Coveo's existing e-commerce search solution. Could you confirm if this is for a new product launch or an update to our current offering?

Why it matters: Helps determine if we're optimizing existing infrastructure or building from scratch. Expected answer: Update to current offering. Impact: Influences whether we prioritize quick wins or long-term architectural changes.

• Business Context: Based on our revenue model, I'm thinking this decision could impact our pricing tiers. How does this align with our current monetization strategy?

Why it matters: Helps prioritize features based on revenue potential. Expected answer: Higher-tier customers demand more advanced filtering. Impact: May lead to emphasizing filtering options for premium tiers.

• User Impact: Considering user behavior, I'm curious about our current search abandonment rates. Do we have data on why users typically exit the search process?

Why it matters: Identifies pain points in the current user experience. Expected answer: High abandonment due to slow load times or inability to find specific products. Impact: Could prioritize speed if abandonment is due to slow responses.

• Technical Feasibility: I'm thinking about our current infrastructure. What's our current average query response time, and what's the technical limit for adding more filters without impacting performance?

Why it matters: Determines the realistic scope of improvements. Expected answer: Current average is 500ms, adding filters increases this by 50-100ms each. Impact: Helps set realistic goals for balancing speed and filtering capabilities.

• Resource Allocation: Considering our team structure, I'm wondering about our capacity for parallel development. Can we work on both speed optimization and filter enhancements simultaneously?

Why it matters: Influences project timeline and resource allocation. Expected answer: Limited resources, need to prioritize one aspect. Impact: May lead to a phased approach rather than tackling both simultaneously.