How can LiveRamp improve its Identity Resolution capabilities to better handle cross-device matching?

Introduction

To improve LiveRamp's Identity Resolution capabilities for better cross-device matching, we need to analyze the current system, identify pain points, and develop innovative solutions. I'll approach this by examining user segments, analyzing pain points, generating solutions, and proposing metrics for success.

Step 1

Clarifying Questions

• Looking at LiveRamp's position in the ad tech ecosystem, I'm thinking about the scale of their current identity graph. Could you share some information about the current coverage and accuracy of LiveRamp's cross-device matching capabilities?

Why it matters: Determines the baseline for improvement and helps identify specific areas of focus. Expected answer: 70% coverage with 85% accuracy across major devices. Impact on approach: Would focus on expanding coverage to new device types or improving accuracy for existing matches.

• Considering the evolving privacy landscape, I'm curious about LiveRamp's current approach to user consent and data governance. How does LiveRamp currently handle user privacy and consent in its identity resolution process?

Why it matters: Ensures our improvements align with privacy regulations and user expectations. Expected answer: Consent management system in place, but challenges with global regulations. Impact on approach: Would prioritize privacy-preserving techniques and consent management improvements.

• Given the rapid pace of technological change, I'm wondering about LiveRamp's current tech stack for identity resolution. What are the primary technologies and algorithms currently used for cross-device matching?

Why it matters: Helps identify potential areas for technological upgrades or algorithm improvements. Expected answer: Probabilistic and deterministic matching, with some machine learning components. Impact on approach: Would explore advanced AI/ML techniques or novel data sources for matching.

• Thinking about LiveRamp's business model, I'm curious about the key performance indicators (KPIs) that drive the identity resolution product. What are the primary metrics LiveRamp uses to measure the success of its cross-device matching capabilities?

Why it matters: Ensures our improvements align with business goals and can be measured effectively. Expected answer: Match rate, accuracy, processing speed, and customer retention. Impact on approach: Would focus on solutions that directly impact these KPIs.