Are you able to tell what numbers are important and why when you're measuring product success?
This product sense question is meant to gauge how well you can think about the data that explains the health of your product. By understanding the metrics that are essential for the success of your product, you are able to tell whether or not your product is sinking, swimming or gasping for air.
It is important to preface the answer to this question with a contextual explainer (or disclaimer):
"There's a set of numbers I look at to determine how well my product is doing. How I interpret those numbers depends on the goals of my product and the needs of our users. For instance, if I was a P.M at Hinge, and the mission truly is 'designed to be deleted', then churn is good [if they got partnered from Hinge]. If I am managing a money transfer product, I want people to spend as little time as possible on my product, so the smaller the session duration, the more successful we are- based on whether or not the user successfully completed the 'job-to-be-done'.
My numbers are always put in context.
Still, these are the KPIs/Metrics I would use to measure the success of my customer-facing product:
User Engagement: Metrics like daily active users (DAU), monthly active users (MAU), and session duration reflect user engagement. These metrics help in assessing the product's stickiness.
Conversion Rates: Tracking conversion rates at key points in the user journey, such as sign-ups or purchases, helps identify bottlenecks and areas for improvement.
Retention Rate: Measuring user retention over time (e.g., 7-day or 30-day retention) indicates whether users find long-term value in the product.
Net Promoter Score (NPS): NPS measures user satisfaction and their likelihood to recommend the product to others, providing insights into user sentiment.
Churn Rate: Tracking user churn rates helps identify issues that cause users to stop using the product.
Revenue Metrics: For revenue-generating products, metrics like average revenue per user (ARPU), customer lifetime value (CLTV), and conversion to paid users are essential.
Feedback Analysis: Qualitative user feedback and sentiment analysis provide context to quantitative metrics, helping identify why users are behaving a certain way.
Using these metrics, I create a data-driven product roadmap. I prioritize features and improvements based on the impact they have on these KPIs. For example, if the retention rate is low, I would prioritize features that enhance user onboarding or deliver more value over time; unless we're Hinge, of course"
Product sense questions allow you to showcase your brilliance when analysing products so you can either improve them or create new ones. Like any other skill, answering these questions is a muscle that can be developed from professional experience, and consistent practice of such interview questions.