How should MNTN balance increasing ad frequency in its Performance TV platform against potential viewer fatigue?

Introduction

Balancing ad frequency and viewer fatigue in MNTN's Performance TV platform is a critical trade-off that directly impacts our revenue and user experience. This scenario involves weighing the potential for increased ad revenue against the risk of diminishing returns and user dissatisfaction. I'll approach this by analyzing the key factors, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Based on our current market position, I'm thinking this trade-off might be driven by revenue pressure. Could you share more about our current business context and why we're considering increasing ad frequency now?

Why it matters: Helps understand the urgency and business drivers behind this decision. Expected answer: Pressure to increase revenue in a competitive market. Impact on approach: Would influence how aggressively we might push for increased frequency.

• Considering user behavior, I'm assuming we have data on current ad engagement rates. Can you provide insights into how users typically interact with ads on our platform?

Why it matters: Establishes a baseline for user behavior and ad effectiveness. Expected answer: Varied engagement rates across different user segments. Impact on approach: Would help tailor our experiment design and metric selection.

• From a technical standpoint, I'm curious about our current ad serving capabilities. Do we have the infrastructure to support more granular frequency control and personalization?

Why it matters: Determines the feasibility of implementing more sophisticated ad frequency strategies. Expected answer: Some capabilities exist, but may require additional development. Impact on approach: Would influence the complexity of solutions we can consider.

• Regarding our user base, I'm thinking different segments might have varying tolerance for ad frequency. Can you share any insights into our user segmentation and their ad sensitivity?

Why it matters: Allows for a more nuanced approach to balancing ad frequency across different user groups. Expected answer: We have some segmentation data, but it's not comprehensive. Impact on approach: Would guide how we design our experiments and analyze results.