💡Introduction - Understanding Product Analytics

Analyzing product usage especially in the context of an app or website gives us the advantage of understanding how users interact with different features, pages, and journeys. This insight is crucial for product teams to iterate intelligently, prioritize improvements, and drive overall user satisfaction and retention.

Product analytics helps answer key questions such as:

• Which features are being used the most?
• Where do users drop off?
• How quickly do new users adopt key features?
• What user behaviors correlate with long-term engagement?

📈 Digging Into Feature Adoption: What We Want to Know

In this project, I focused on feature adoption analysis, a vital component of product analytics that measures how users discover and begin to use a specific feature over time. While tools like Amplitude, Heap, and PostHog typically provide such event data through built-in tracking, I generated the dataset synthetically with the help of a chatbot to simulate realistic product usage patterns.

This enabled me to explore key questions such as:

• How many active users visit the product each month?
• What is the growth trend of active users over time?
• How frequently are users engaging with the feature?
• What is the average rate of feature adoption?
• How is user retention trending over a 3-month period?
• What is the drop-off rate?
• What is the conversion rate at each stage of the funnel?

Building on the key questions above, this analysis aims to help product teams uncover how users interact with a specific feature - how often they engage, how adoption and retention evolve over time, and where users drop off in the journey. The goal is to deliver actionable insights that support evidence-based decisions, streamline onboarding, and ultimately enhance product performance. The accompanying dashboard provides a clear, month-by-month view of user engagement for January, February, and March 2025, enabling teams to monitor trends and make informed improvements.