Thanks, Olga Berezovsky! I'm delighted to contribute to your Data Analysis Journal. As you say... maximizing product KPIs truly comes down to understanding the causal relationships between metrics and user actions. I hope that your subscribers find the detailed (conversion, retention, engagement, and funnel) analyses, the insight on feature impact throughout the user lifecycle, and other information, such as building an opportunity sizing model, practical and inspiring advice that helps them drive growth for their companies! #productgrowth #causalml
Measuring the impact of product optimizations is more complex than it appears: - Increased feature usage doesn't always lead to higher conversion rates. - High conversion rates may not necessarily improve retention. - Improved retention might not translate into increased revenue. It all comes down to understanding the causal relationships between metrics and user actions. How to unlock it? This week, I'm thrilled to host Tom Laufer, CEO and Co-founder of Loops, to explain cause and effect relationship and share 4 different analyses with us: 🚀 Conversion Drivers analysis: Uncover usage patterns to identify what really drives conversions. 📊 Retention Drivers causal analysis: Learn how each feature contributes to retention. 😍 Engagement Drivers analysis: Examine activated users to understand consistent usage patterns. ⏳ Feature Funnel Analysis: Identify strategic opportunities to increase feature adoption. Learn how to run cause and effect analysis, build an opportunity-sizing model, understand feature usage, and more - https://lnkd.in/gTFvfsHD. Huge thank you to Tom for writing such a great guide!
Data science lead at Loops
2moGreat stuff. Thanks for sharing!