How can we enhance Trouva's search filters to help customers find unique items more easily?

Introduction

Enhancing Trouva's search filters to help customers find unique items more easily is a critical challenge that directly impacts user satisfaction and conversion rates. As we dive into this product improvement case, I'll focus on understanding the current user experience, identifying pain points, and proposing innovative solutions that align with Trouva's mission of connecting customers with unique, independent boutiques.

Let's begin by clarifying some key aspects of the problem.

Step 1

Clarifying Questions (5 mins)

• Looking at Trouva's position in the market, I'm thinking about the balance between curation and scale. Could you help me understand how Trouva currently approaches the tension between offering a wide variety of items and maintaining a curated, boutique feel?

Why it matters: This will inform our approach to filter design, balancing discoverability with the uniqueness that sets Trouva apart. Expected answer: Trouva leans towards curation but struggles to showcase its full range effectively. Impact on approach: If heavily curated, we might focus on storytelling in filters; if broader, we'd emphasize granular categorization.

• Considering Trouva's unique value proposition of connecting customers with independent boutiques, I'm curious about user behavior. What percentage of users browse by boutique versus searching for specific items?

Why it matters: This will help us determine whether to prioritize boutique-centric filters or item-specific attributes. Expected answer: 70% search for items, 30% browse by boutique. Impact on approach: A higher percentage of item searches would lead us to focus on item attributes and styles in our filter improvements.

• Given the focus on unique items, I'm wondering about the current search and filter usage patterns. What are the top 3-5 filters users currently engage with, and how does this align with our understanding of what makes an item "unique" on Trouva?

Why it matters: This will help us identify gaps between user behavior and Trouva's unique selling points. Expected answer: Top filters are price, category, and color, but these don't necessarily capture uniqueness. Impact on approach: We might need to introduce new filters that better reflect the unique aspects of Trouva's inventory.

• Thinking about Trouva's growth stage and competitive landscape, I'm curious about our key performance indicators. What metrics are we currently using to measure the success of our search and filter functionality, and how do they tie into broader business goals?

Why it matters: This will help us align our solution with Trouva's strategic objectives and measure impact effectively. Expected answer: Key metrics include conversion rate, time to purchase, and average order value. Impact on approach: We'd focus on solutions that directly impact these metrics while also considering qualitative measures of user satisfaction.