That’s the most popular question we have faced during our Testing and Optimization efforts with our clients. This simple sounding but very important question asks a lot of itself. And the answer is that the duration depends upon the achievement of goals of the test and also upon the confidence level attained so far.
There has been a long-standing discussion among statisticians, as to what should be the sample size for any kind of advanced statistical analysis. Over time, this has led to a number of calculated solutions to mitigate the problem. The topic is embroiling, hence keeping the readers in mind, we will explore this debate on sample
size, how to quickly come up with a sample size for a particular case, and study some practical relatable examples.
As the gap between desktop and mobile devices is narrowing down and usages of these different devices are overlapping, optimization of efforts by methods such as A/B Testing is also becoming severely important for the mobile platform.
A Universal Challenge
Having worked with clients for more than five years in the field of digital analytics, I have noticed that one of the major roadblocks towards wider acceptance and use of analytics data is the lingering doubt about the data’s accuracy. In reality, managers would accept the reports we churn out, however, we know, based on the decisions they are making, that they have not wholeheartedly accepted them. Let us face it, after all, why should the decision makers trust the data we are presenting to them? In all likelihood, the Web Analytics System (the people, platforms, and processes) exist and work as a separate unit, rather than as part of the data user’s team.
The holiday season has come to an end; we are now in the new year! It is time to distil all the web analytics data we have collected in Google Analytics, and reflect on the lessons we have learned. The insights gained from the 2013 holiday season will help us optimize our efforts throughout 2014.
