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Product Management Trade-Off Question: Instagram Explore feed categories versus recommendation accuracy dilemma

Asked at Meta

15 mins

The Instagram Explore team is discussing: should we implement more content categories or improve existing category accuracy?

Product Trade-Off Hard Member-only
Strategic Decision Making Data Analysis User Experience Design Social Media Content Platforms Digital Advertising
Social Media Product Strategy User Engagement Content Discovery Trade-Off Analysis

Introduction

The Instagram Explore team is facing a critical decision: should we implement more content categories or improve existing category accuracy? This trade-off involves balancing the breadth of content discovery against the precision of recommendations. I'll analyze this scenario using a structured approach, considering user experience, business impact, and technical feasibility.

Analysis Approach

I'd like to outline my approach to ensure we're aligned on the key areas I'll be covering in my analysis.

Step 1

Clarifying Questions (3 minutes)

  • Context: I'm assuming this is a strategic decision for the Explore feed. Is this correct, or are we considering other areas of Instagram as well?

Why it matters: Defines the scope of our analysis and potential impact. Expected answer: Focus is on Explore feed. Impact: Narrower scope allows for more targeted solutions.

  • Business Context: How does Explore currently contribute to our key revenue streams and user engagement metrics?

Why it matters: Helps prioritize the trade-off against business objectives. Expected answer: Significant contributor to ad revenue and time spent in-app. Impact: Higher priority if it's a major revenue driver.

  • User Impact: What user segments are we most concerned about for this decision?

Why it matters: Different user groups may have varying needs and behaviors. Expected answer: Focus on high-value creators and power users. Impact: May lean towards accuracy for retention of key users.

  • Technical Feasibility: What are our current limitations in improving category accuracy?

Why it matters: Determines if accuracy improvements are realistic. Expected answer: Challenges with AI model refinement and data quality. Impact: If significant barriers exist, may favor new categories.

  • Resource Allocation: Do we have the team capacity to pursue both options simultaneously, even at a smaller scale?

Why it matters: Influences whether this is a true either/or decision. Expected answer: Limited resources require focusing on one option. Impact: If resources allow, could consider a hybrid approach.

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