How can ManoMano improve its mobile app search function to help customers find products faster?

Introduction

ManoMano's mobile app search function is a critical component for customer satisfaction and conversion rates. To improve it, we need to focus on speed, accuracy, and user experience. I'll analyze the current situation, identify key pain points, and propose data-driven solutions to help customers find products faster.

Step 1

Clarifying Questions

• Looking at ManoMano's position as a DIY and home improvement marketplace, I'm thinking about the diverse product catalog and user needs. Could you share insights on the current search behavior patterns and the most common types of queries users perform?

Why it matters: Determines if we should focus on category-based navigation, natural language processing, or visual search capabilities. Expected answer: Mix of specific product searches and category browsing, with a high percentage of long-tail queries. Impact on approach: Would prioritize advanced query understanding and category optimization.

• Considering the mobile app context, I'm curious about user engagement metrics. What's the current average session duration and search-to-purchase conversion rate on the app compared to the desktop version?

Why it matters: Helps identify if the main issue is findability or the overall mobile user experience. Expected answer: Mobile sessions are shorter with lower conversion rates compared to desktop. Impact on approach: Would focus on streamlining the mobile search experience and improving result relevance.

• Given the competitive landscape in e-commerce, I'm interested in understanding ManoMano's unique value proposition. What are the key differentiators that set ManoMano apart from general marketplaces like Amazon or specialized retailers?

Why it matters: Ensures that our search improvements align with and enhance ManoMano's core strengths. Expected answer: Specialized knowledge in DIY and home improvement, curated product selection, and expert advice. Impact on approach: Would incorporate expert content and specialized filtering options into search results.

• Considering the product improvement initiative, I'm wondering about the current tech stack and any constraints we might face. What search technology is currently in use, and are there any limitations in terms of infrastructure or data availability?

Why it matters: Determines the feasibility of potential solutions and helps prioritize improvements. Expected answer: Custom-built search engine with basic machine learning capabilities, limited by processing power for real-time personalization. Impact on approach: Would focus on optimizing existing capabilities while proposing scalable enhancements.