The Fine Art of Visualizing Experiment Results
In this post from the Disney Streaming technology blog, Allen Chang (Sr. Data Scientist, Core Experimentation) shares how they help teams understand the results of their experiments through data visualization. The post covers the specific visualizations they use to represent statistical uncertainty as well as the tech that they use to build them.
Musa Tariq, GoFundMe’s CMO, is testing how to bring people together.
In this Forbes article, Musa Tariq (CEO of GoFundMe) explains the value and importance of testing. Musa understands that most experiments may fail, but “knowing that if one of those things work, it could be the thing that changes both the trajectory of the company and therefore helps more people.”
Using Experimentation To Understand Tradeoffs In Referral Incentives
In this post, Ilya Izrailevsky (Senior Engineering Manager, Experimentation & Metrics at Robinhood) shares what the Robinhood experimentation team discovered about the effect of referral incentives on a product, and some of the approaches to developing it.
“Drawing on the experiences of Harrah’s Entertainment, Sony, Bank of America, and Lego, Thomke and Loveman show how scientific methods can help companies discard ineffective practices, increase marketing and operational efficiency, boost customer satisfaction and sales, find new sources of growth, and even turn around struggling businesses.”
In this post from Lyft’s engineering blog, John Kirn (Product Manager for Experimentation) shares where Lyft’s largest challenges come from and how it’s the experimentation team’s job to solve these challenges. The post then focuses on the capabilities they have deployed in order to “enable decision-making” at Lyft as well as their plans for the future.
Even Split Increases Power of A/B Tests
In this post published on the Towards Data Science blog, Qike (Max) Li (Data Scientist at Wish), shares their learnings when setting up the experiment sample ratio to reach statistical significance faster.