Strategy & Process

Experiment Ideation: Why it’s a Team Effort

At Brooks Bell’s 8th annual Click Summit in May, I had the pleasure of leading a conversation titled, “What’s Fueling Your Experimentation Ideation?”  Throughout the session, participants talked about the ways to use quantitative and qualitative data, past test learnings, behavioral economics principles and the challenges they face with executing tests to their full potential. (Download the top takeaways from this session, as well as all the “Clickaways” here.)   But there was one more important attribute we discussed – the importance of the team!  

Truly Understanding Minimum Detectable Effect

Now you know how to Predetermine Your Test Sample Size and How to Reach Statistical Significance & Power within Your Experimentation Programs. Our final blog concludes the series and will expand your understanding of how to use Minimum Detectable Lift, or Minimum Detectable Effect for optimum test design and results.

Economists predict e-Commerce sales will range from $427 – $443 billion in 2017, a growth rate of three times higher (8-12 percent) than the entire retail industry.  This upward trend will continue. In fact, Business Intelligence forecasts consumers will spend $632 billion in 2020.

How To Reach Desired Confidence & Power Levels with Your Experimentation Programs

This is the second of a three-part blog series. If you missed the first post, “Using Fixed Time Horizon to Generate Credible Optimization Results,” read it hereThe final blog, “How to Understand Minimum Deductible Lift,” will be published next week.

Enterprise businesses continue adopting the concept of data-informed decision-making. They are shifting their organizational cultures to those where more and more decisions are based on facts and insights. At the same time, optimization continues growing in popularity among top brands and is essential for business success in today’s digital world.