In what ways can we improve the accuracy and relevance of Chegg's textbook search function?

Introduction

To improve the accuracy and relevance of Chegg's textbook search function, we need to analyze user behavior, identify pain points, and develop targeted solutions. I'll outline a comprehensive approach to enhance this critical feature, focusing on user experience and search efficiency.

Step 1

Clarifying Questions (5 mins)

• Looking at Chegg's position in the education technology market, I'm thinking about the diversity of our user base. Could you provide insights into the primary user segments we're targeting with this search function improvement? Are we focusing on college students, high school students, or a broader range of learners?

Why it matters: Determines the scope and specificity of our search optimization efforts. Expected answer: Primarily college students, with a growing high school segment. Impact on approach: Would tailor search algorithms and filters to accommodate different educational levels and textbook types.

• Considering the evolving nature of educational content, I'm curious about the types of materials our search function currently covers. Beyond traditional textbooks, does it include digital materials, open educational resources, or other forms of academic content?

Why it matters: Influences the breadth and depth of our search indexing and categorization. Expected answer: Currently focused on traditional textbooks with plans to expand to digital resources. Impact on approach: Would prioritize improving traditional textbook search while laying groundwork for future content expansion.

• Given the competitive landscape in edtech, I'm interested in understanding our current market position. How does our textbook search function currently compare to our main competitors in terms of accuracy and user satisfaction?

Why it matters: Helps identify specific areas for improvement and competitive differentiation. Expected answer: We're on par with major competitors but lagging in certain niche subject areas. Impact on approach: Would focus on enhancing search capabilities in underperforming subject areas and identifying unique features to stand out.

• Considering the importance of data in search optimization, I'm wondering about our current data collection and analysis capabilities. What types of user behavior data do we currently track related to search interactions, and how are we leveraging this for improvements?

Why it matters: Determines the depth of data-driven decisions we can make in our improvement strategy. Expected answer: We track basic metrics like search queries and click-through rates, but lack detailed user journey analysis. Impact on approach: Would prioritize enhancing data collection and analysis capabilities to inform more nuanced search improvements.