Post-Launch Evaluation & Data Analysis Framework

🎯 The Challenge

Teams were shipping features without systematic post-launch evaluation, missing opportunities to learn from real usage, identify struggling users, and make evidence-based improvement decisions.

🔧 What I Built

Post-launch evaluation framework using adapted HEART metrics (Activation, Adoption, Stickiness, Task Success) with reusable Amplitude dashboard templates.

📋 Process Established

Five-step process for systematic feature evaluation.

• Define metrics before launch – What does success look like?
• Set up tracking – Consistent event taxonomy in Amplitude
• Create dashboards – Ongoing monitoring templates
• Combine with qualitative research – User interviews, CSM feedback
• Identify outliers – Power users to learn from, drop-offs to investigate

📊 Metrics Framework

Adapted HEART metrics for feature evaluation.

• Activation – Who discovered and enabled the feature?
• Adoption – Who tried it? Who kept using it? How does it compare to alternatives?
• Stickiness – How often do users return? Daily/weekly engagement patterns
• Task Success – Does the feature help users achieve goals faster?

🎛 Amplitude Dashboard Template

Created reusable dashboard structure tracking:

• Cumulative adoption (unique users over time)
• Usage comparison (new feature vs existing alternatives)
• Per-customer breakdown (by email domain)
• Session and search frequency trends
• Average usage per user

✅ Applied Example: Semantic Search

Full post-launch evaluation for Semantic Search early access.

• Tracked activation through Innovation Lab
• Measured adoption comparing semantic vs keyword search
• Identified top-engaging customers for interviews
• Combined Amplitude with Metabase for task success metrics (time to find content)
• Fed findings back into design iterations

📚 Knowledge Transfer

Shared methodology with design team so post-launch evaluation becomes standard practice across all feature releases.

🌊 Impact