Seven rules of thumb for web site experimenters

Abstract

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.

Future User Engagement Prediction and Its Application to Improve the Sensitivity of Online Experiments

“…These studies concern, inter alia, experiments with different components of a web service (the user interface [13,8], ranking algorithms [27,8], etc. ), largescale experimental infrastructure [28,15], different aspects of user behavior and interaction with a web service (clicks [14,16], speed [19,16], abandonment [16], periodicity [8], etc. ), and so on.…”

“…Online controlled experiments, or A/B testing, have become the state-of-the-art technique to improve web services based on data-driven decisions [16]. It is utilized by many web companies, e.g., web search engines such as Bing [6,16] and Google [28], social networks like Facebook [1], etc.…”

“…It is utilized by many web companies, e.g., web search engines such as Bing [6,16] and Google [28], social networks like Facebook [1], etc. The largest ones have designed special experimental platforms that allow them to run A/B tests at large scale (e.g., hundreds of concurrent experiments per day) [28,15].…”

“…User engagement reflects how often the user solves her needs (e.g., to search something) by means of the considered service (e.g., a search engine). On the one hand, these metrics are measurable in the short-term experiment period, and, on the other hand, they are predictive of the long-term success of the company [14,15,16,25]. That is why engagement metrics are often considered to be most appropriate for online evaluation.…”

“…That is why engagement metrics are often considered to be most appropriate for online evaluation. In this study, we pay special attention to the metrics that reflect the loyalty aspect of engagement: the state-of-the-art number of sessions per user metric [14,27] (which is accepted as the "North-star" for A/B testing evaluation in major search engine companies like Bing [15,16]) and the recently proposed absence time metric [9]. Our approach raises the problem of prediction of the future individual user engagement.…”