We experiment to measure, to reduce risk, to optimize, to forecast, and—most importantly—to learn. When a single experiment wins, we know what worked for one audience on one page at one point in time. But the real value of experimentation comes from learning at scale. With proper analysis, your experimentation program can become greater than the sum of its parts. In this talk, you’ll learn to:

•               Design experiments that result in actionable learnings, whether they win, lose, or are inconclusive.
•               Conduct thorough archive analysis to learn from all of your previous experiments.
•               Use archive analysis to run smarter experiments and make better business decisions.