Work Smarter Webinar: From Data to Decisions: A Practical Introduction to Optimizely Analytics for Everyday Users

Instructor: Sathya Narayanan, Senior Director, Product Management

Description: Ever found yourself staring at analytics data, wondering if you can truly trust it, or even where to begin? You're not alone. In today's data-rich world, the real challenge isn't a lack of information, but a lack of confidence in using it. This high-level webinar cuts through the noise, introducing you to the core principles of modern experimentation and product analytics. Discover how Optimizely Analytics empowers your team to transform questions into clear insights and make decisions you can truly stand behind—all without needing to be a data scientist. Plus, we'll show you how Opal helps you get there even faster.

Audience: For Optimizely Experimentation customers with Analytics who want to unlock its full potential: if you feel overwhelmed by data, unsure where to start, or want more clarity and confidence in using it daily, this webinar is for you.

This session is perfect for you if: ✅ You're not a data expert. We'll cover high-level principles and show you how to gain powerful insights without needing to be an analyst or write complex SQL queries.

Why Join? Unlock Your Data Confidence.

Join us to learn how to:

• Answer Real Questions: Move beyond just numbers to get meaningful answers that drive action.
• Validate Ideas Faster: Speed up your decision-making and reduce risk when launching new changes.
• Trust Your Results: Understand why data can feel confusing and how Optimizely provides consistent, warehouse-backed metrics for clear comparisons.
• Go Faster & Smarter with AI: See how Opal suggests data explorations, explains insights, and helps you overcome analytical roadblocks.
• Hear Real Success Stories: Learn from Optimizely Experimentation customers using Analytics who transformed their data friction into decision confidence.
• Live Q&A: Bring all your questions – no question is too basic! We'll cover common concerns like data discrepancies, understanding results pages, and how to formulate the right questions.