For Branch's People-Based Attribution, should we emphasize improving cross-device matching accuracy, which may require more user data collection, or prioritize user privacy by limiting data gathering?

Introduction

The trade-off we're examining today is between improving cross-device matching accuracy for Branch's People-Based Attribution and prioritizing user privacy by limiting data gathering. This scenario touches on the core tension between enhancing product functionality and respecting user data rights. I'll analyze this trade-off by exploring its implications for Branch's business, users, and the broader tech landscape.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking about Branch's current market position. Could you share how our People-Based Attribution compares to competitors in terms of accuracy and privacy features?

Why it matters: Helps understand competitive pressures and market expectations. Expected answer: We're mid-tier in accuracy, strong in privacy. Impact: If leading in privacy, might lean towards maintaining that advantage.

• Business Context: Based on our revenue model, I assume attribution accuracy directly impacts our value proposition. How significant is the revenue impact of a 1% improvement in cross-device matching?

Why it matters: Quantifies the business case for improved accuracy. Expected answer: 1% improvement translates to $X million in revenue. Impact: High revenue impact would justify more aggressive data collection.

• User Impact: Considering user segments, are there particular groups more sensitive to privacy concerns or more valuable for cross-device matching?

Why it matters: Helps tailor our approach to different user groups. Expected answer: High-value users are more privacy-conscious. Impact: Might lead to a segmented approach balancing privacy and accuracy.

• Technical: Regarding feasibility, what additional data points would significantly improve our cross-device matching, and how complex would implementation be?

Why it matters: Assesses the technical effort required for improvement. Expected answer: Device fingerprinting could help, but it's complex to implement. Impact: High complexity might favor privacy-focused approach in short term.

• Timeline: Given current market trends, how urgent is this decision? Are there upcoming regulations or competitor moves we need to consider?

Why it matters: Helps prioritize this decision against other initiatives. Expected answer: GDPR-like regulations expected in key markets within 18 months. Impact: Might push us towards a more privacy-centric approach proactively.