As PM for Google Web Answers, I'd prioritize metrics that directly reflect user success and platform health.
First, I'd clarify the product's vision: to be the most reliable and comprehensive source for quick, accurate answers to user queries. Over the next 4-5 years, this means expanding coverage, improving answer quality, and fostering a vibrant community of contributors.
Key user personas include:
- Information Seekers: Users looking for quick, factual answers. Their success is measured by Answer Findability (e.g., search result click-through rate to answers) and Answer Satisfaction (e.g., user ratings, time spent on page, subsequent searches for clarification).
- Contributors/Experts: Users who provide answers. Their success is tied to Contribution Volume (number of answers submitted), Contribution Quality (expert review scores, user upvotes), and Engagement (repeat contributions, community interaction).
Metrics would be derived from these personas and actions:
- Core User Metrics:
- Answered Query Rate: Percentage of queries with a satisfactory answer.
- Time to Answer: Average time for a query to receive a high-quality answer.
- User Satisfaction Score (USAT): Direct user feedback on answer helpfulness.
- Search Abandonment Rate: Percentage of users who leave Google without finding an answer.
- Contributor Metrics:
- New Contributor Acquisition Rate: How many new users are joining to answer questions.
- Active Contributor Rate: Percentage of contributors actively answering questions.
- Answer Acceptance Rate: Percentage of submitted answers that are marked as helpful or accepted.
- Expertise Signal: Metrics indicating the quality and reliability of answers from specific contributors.
I would also monitor negative recursion by looking for metrics like:
- Answer Spam/Low-Quality Content Rate: To ensure the platform isn't flooded with unhelpful or malicious content.
- Gaming of Metrics: Identifying patterns where contributors might be artificially inflating their scores without providing genuine value.
- User Frustration Signals: Sudden increases in negative feedback or abandonment rates after a change, indicating unintended consequences.
Google