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Aug 28 2017 - 04:44pm
Web Analytics for Actionable Insights

Here are the slides for our presentation!

This past month the research team has been working on analyzing the usage and behavior on some of our EdLab Platforms. This work will be presented during this Tuesday’s (8/29) Development and Research Meeting.

We will be considering the purpose and goal of using web analytics to inform development decisions or develop useful features.

A recent blog post that I have read looks to distinguish what it calls “vanity metrics” and “actionable insights.”

Metrics, Metrics On The Wall, Who’s The Vainest Of Them All?

It all comes down to one thing: does the metric help you make decisions? When you see the metric, do you know what you need to do?

If you don’t, you’re probably looking at a vanity metric.

Vanity metrics are all those data points that make us feel good if they go up but don’t help us make decisions. Let’s say your visits look like the graph below. What should you do to help grow your business?

Additionally, this blog post suggests that if you are not processing your data in the right way you may be getting bad information throughout the whole process.

The Schema Conspiracy

A schema is something that data processing platforms such as Google Analytics apply to the raw hit data coming in from the data source (usually a website). The most visible aspect of Google Analytics’ schema is how it groups, or stitches, the arbitrary, hit-level data coming in from the website into discrete sessions, and these are actually grouped under yet another aggregate bucket: users.

These are challenges which every researcher looking to analyze web data in an organization must face. Below are a series of papers which implemented varied analysis methods to gain actionable insight. They go beyond what a dashboard such as Google Analytics may offer out of the box.

Clustering the Users of Large Web Sites into Communities

Personalized news recommendation based on click behavior

Understanding user behavior in large-scale video-on-demand systems

Automatic Identification of User Goals in Web Sear ch

Lown, C., Sierra, T. and Boyer, J. (May 2013). "How Users Search the Library from a Single Search Box." College & Research Libraries. 74(3), pp. 227-241.

More Information on NCSU's QuickSearch

Posted in: Research|By: Alvaro Ortiz-Vazquez|461 Reads