Web site owners, from small web sites to the largest properties that include Amazon, Facebook, Google, LinkedIn, Microsoft, and Yahoo, attempt to improve their web sites, optimizing for criteria ranging from repeat usage, time on site, to revenue. Having been involved in running thousands of controlled experiments at Amazon, Booking.com, LinkedIn, and multiple Microsoft properties, we share seven rules of thumb for experimenters, which we have generalized from these experiments and their results. These are principles that we believe have broad applicability in web optimization and analytics outside of controlled experiments, yet they are not provably correct, and in some cases exceptions are known.

To support these rules of thumb, we share multiple real examples, most being shared in a public paper for the first time. Some rules of thumb have previously been stated, such as 'speed matters,' but we describe the assumptions in the experimental design and share additional experiments that improved our understanding of where speed matters more: certain areas of the web page are more critical.

This paper serves two goals. First, it can guide experimenters with rules of thumb that can help them optimize their sites. Second, it provides the KDD community with new research challenges on the applicability, exceptions, and extensions to these, one of the goals for KDD's industrial track.