The purpose and value of experimentation (from a scientific and product perspective) is to produce new information. From a product discovery/product management perspective, the purpose of experimentation is to focus our efforts on invalidating our assumptions to reduce the risk of developing and going-to-market with the wrong product. It’s our way of intentionally managing through uncertainty — since, as Claude Shannon said, “the only resolution to uncertainty is information.”

Lean A3, PDSA (Plan-Do-Study-Act) and the Build-Measure-Learn or Think-Make-Check loop (to name a few loops) are all learning models informed by the notion that experimentation is the fastest (and most proven) route to product-market fit and achieving sustainable organic growth.

This presentation will dive into how proper experimental design, multiple hypothesis testing, cohort analysis, and split testing can effectively reduce batch size and meantime to validate insights accelerating your lead time from idea to cash. But the key to learning from failure, or failing well, is making sure you’re reflecting and learning the right lessons to enable better bets in the future.

Bad experiments inject noise into your decision-making process. Good experiments generate insights (information). Which would you feel more confident waging a bet on? Which would you trust your next paycheck on? We’ll cover all of this in 45 minutes with room for questions and criticisms!

Learning Objectives:

  • Learn the fundamental principles and logic of experimentation
  • Learn how to apply various models and experiments to various types of product risk
  • Learn how Design Thinking and Lean collaborate to speed up the discovery and validation of new strategies, products, and functionality
  • Key steps and practical guidance for building the Continuous Discovery Flywheel
  • Learn how to design for cognitive and social biases

March 16, 2023 at 11:00 am PT, 2:00 pm ET, 7:00 pm GMT


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