What factors are contributing to the 50% decrease in ad impressions on Outcome Health's waiting room TV screens since the latest content update?

Introduction

The recent 50% decrease in ad impressions on Outcome Health's waiting room TV screens following the latest content update is a critical issue that demands immediate attention. This significant drop in a key performance metric could have far-reaching implications for our business model, client relationships, and overall product strategy. To address this complex problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking the content update might be directly related to the decrease. Could you provide more details about the nature of this update?

Why it matters: Understanding the specifics of the update helps pinpoint potential issues. Expected answer: Information about content types, formats, or scheduling changes. Impact on approach: Different types of changes would lead to different hypotheses.

• Given the scale of the decrease, I'm wondering about the timeframe. How quickly did this 50% drop occur after the update?

Why it matters: The speed of the decline could indicate whether it's a technical issue or a gradual user behavior shift. Expected answer: A timeframe ranging from immediate to several weeks. Impact on approach: A sudden drop might suggest a technical issue, while a gradual decline could point to user engagement problems.

• Considering the complexity of ad systems, has there been any change in how we're measuring or defining ad impressions?

Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: Confirmation of consistent measurement methods or details of any changes. Impact on approach: If measurement changed, we'd need to recalibrate our analysis based on the new definition.

• I'm curious about the scope of this issue. Is the 50% decrease consistent across all locations and demographics, or are there variations?

Why it matters: Helps identify if the problem is universal or specific to certain segments. Expected answer: Information on any patterns or variations in the decrease. Impact on approach: Segmented data would lead to more targeted hypotheses and solutions.