How might MNTN enhance its cross-device attribution capabilities to provide more actionable insights for advertisers?

Introduction

To enhance MNTN's cross-device attribution capabilities and provide more actionable insights for advertisers, we need to dive deep into the current product landscape, user behavior, and market dynamics. I'll structure my approach by first clarifying key aspects of the problem, then analyzing user segments and pain points, generating solutions, and finally evaluating and prioritizing these solutions with appropriate metrics.

Step 1

Clarifying Questions

• Looking at the product context, I'm thinking MNTN might be facing challenges with data fragmentation across devices. Could you help me understand the current state of MNTN's cross-device tracking capabilities and what specific limitations we're trying to overcome?

Why it matters: Determines the baseline for improvement and identifies key areas of focus. Expected answer: Limited ability to track users across mobile and desktop, with challenges in connecting TV ad exposure to digital actions. Impact on approach: Would focus on integrating TV and digital data streams, potentially exploring partnerships with identity resolution providers.

• Considering user behavior, I'm curious about the typical customer journey across devices for MNTN advertisers. Can you share insights on how users typically interact with ads across TV, mobile, and desktop platforms?

Why it matters: Helps identify critical touchpoints and potential attribution gaps. Expected answer: Users often see TV ads, then research on mobile, but convert on desktop. Impact on approach: Would prioritize solutions that can connect these disparate touchpoints and provide a holistic view of the customer journey.

• Examining the competitive landscape, I'm wondering about the current market standards for cross-device attribution. What are the leading solutions offering, and where does MNTN currently stand in comparison?

Why it matters: Helps benchmark our solution and identify opportunities for differentiation. Expected answer: Competitors are using probabilistic and deterministic matching, with some leveraging AI for predictive modeling. Impact on approach: Would focus on areas where MNTN can leapfrog competitors, potentially through advanced AI implementation or unique data partnerships.

• Considering company alignment, I'm interested in understanding how this initiative fits into MNTN's broader strategy. What are the key business objectives driving this focus on cross-device attribution?

Why it matters: Ensures our solution aligns with overall company goals and prioritizes the right outcomes. Expected answer: Increasing advertiser ROI, reducing churn, and expanding into new market segments. Impact on approach: Would tailor solutions to directly impact these business objectives, potentially prioritizing features that demonstrate clear ROI improvements.