For EDO's outcome measurement platform, how should we weigh adding new data sources against keeping the interface simple and user-friendly?

Introduction

The trade-off between adding new data sources and maintaining a simple, user-friendly interface for EDO's outcome measurement platform is a critical product decision. This scenario involves balancing the potential value of enhanced data insights against the risk of overwhelming users with complexity. I'll analyze this trade-off by examining the product context, identifying key metrics, designing experiments, and providing a structured decision framework.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming EDO's platform primarily serves advertisers and media companies. Could you confirm the main user segments and their typical use cases for the platform?

Why it matters: Helps tailor the solution to specific user needs Expected answer: Advertisers and media companies are primary users, focusing on campaign performance measurement Impact on approach: Would influence the types of data sources and interface complexity appropriate for each segment

• Business Context: Based on the emphasis on new data sources, I'm thinking this might be driven by competitive pressure or user requests. How does this initiative align with our current strategic priorities and revenue model?

Why it matters: Ensures the solution supports overall business objectives Expected answer: High priority, aimed at increasing platform value and potentially enabling premium pricing Impact on approach: Would justify more resources for data integration and user research

• User Impact: I'm considering the varying technical expertise of our users. Can you share insights on the data literacy and analytical capabilities across our user base?

Why it matters: Determines the appropriate level of complexity for the interface and data presentation Expected answer: Mix of highly technical analysts and less data-savvy decision-makers Impact on approach: Would necessitate a tiered interface design with different levels of complexity

• Technical Feasibility: Given the mention of new data sources, I'm curious about our current data architecture. How flexible is our system for integrating and processing new types of data?

Why it matters: Influences the feasibility and timeline for adding new data sources Expected answer: Moderately flexible, but some legacy systems may require updates Impact on approach: Might need to factor in additional development time or phased rollout of new data sources

• Resource Allocation: Considering the potential scope of this project, I'm wondering about our team's capacity. What resources (engineering, design, data science) are available for this initiative?

Why it matters: Helps determine the realistic scope and timeline for implementation Expected answer: Limited additional resources available, need to prioritize efforts Impact on approach: Would require careful prioritization of new data sources and interface improvements