Every team wants to improve, optimize, reach goals, to grow. Every team also has a massive pile of ideas on what to work on next. Prioritization is the key to success, ensuring they work on stuff with a high chance of making an impact. As an analyst, you should deliver the insights that feed the prioritization. Did you know that analytics is just a tiny part of those insights? In this talk, I'll show you the best sources to analyze to develop insights that optimize your prioritization.

Google bought Urchin in 2005 and, virtually overnight, made digital analytics available to all companies, no matter how large or how small. Optimizely was founded in January 2010 and had a similar (but lesser) impact on the world of A/B testing. What can we learn from ruminating on the past, the present, and the future (server-side testing! sample ratio mismatch checking! Bayesian approaches!) of experimentation? Quite a bit, if we pull in an industry veteran and pragmatic thinker like Ton Wesseling from Online Dialogue! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

MDE calculations: real data example how to know if you could run experiments on a specific location. Data quality: real data examples to show what it means to have 20% more users in your experiment. Prioritization: how to optimize your chance of success by prioritizing your experimentation roadmap based on data? Test results: what are valid results in the data and what not? When is the experiment done? Business case calculations: how much money is your experimentation program really making?