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“Multi-armed bandit” A/B testing optimality proved?

Correct me if I’m wrong, but it seems that this paper proves optimality of “multi-armed bandit” approach to A/B testing. The latter one was described in this post earlier this year.

For those who do not understand what it is about: A/B testing requires investment in the form of sample size (usually it is equal to number of unique users), which is time and money. “Multi-armed bandit” approach is about optimising this investment.

I wouldn’t say you’re ancient if you aren’t doing it already, but it’s interesting to see how abstract science creates new opportunities for business.

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Measuring time spent on page

One of the challenges of A/B testing is insufficient observations due to low traffic. In other words, if you measured the conversion rate on our web site, it would take months or even years before we’d get conclusive result. What you can try to measure are microconversion and microobservations. That’s what I was up to recently. There are couple of microobservation types I identified so far: time spent and the depth. The time spent is basically how much time a visitor has spent on the web site in seconds and the depth is how many clicks he made after seeing the landing page. As you might notice, you always have some time spent and depth measurements, unless the visitor is a bot.

The other way you can enlarge your data set is by using visits instead of visitors. In case of time spent and depth metrics it makes much more sense.

I used standard Nginx userid module in order to identify visitors. When a visitor requests a page, a special action in C++ application is requested through a subrequest using ssi module. This actions registers the UID and the experiment in memory table and assigns a variant (A or B). Then it returns the variant in response and it gets stored in an Nginx variable. After that I use the value of this variable to display proper variant of the page. Continue reading “Measuring time spent on page”