Watching those who watch us

At Data Applied, we use Google analytics to monitor incoming traffic to our website. We then crunch this data using our own product to extract more meaningful information than we would using Google’s UI alone. For example, we use clustering to automatically categorize visits into different groups, based on characteristics such as visit duration, page views, or location. We use association rule mining to identify hidden associations between visit time, keywords, and network names. We perform outlier detection to get a list of visits which may be out of the ordinary. Finally, we use our own super pivots to better visualize this information. In fact, if you have already created a free account, you should notice a new (anonymized) clickstream dataset we uploaded into the demo workspace.

Regarding network names, there is some type of asymmetric information warfare at play here. Because we are a small startup, we do not have the luxury of maintaining a private network. This means that, when we connect to any website, we appear under a generic (ex: “Comcast customer”) network name. The same however does not apply to other large Business Intelligence companies when they pay us a visit. Here is some summary clickstream data regarding recent visits to our web site. We’re publishing this information because we can, or more precisely because we find it interesting that we can but our visitors cannot. But of course, we’d still like to say thank you for paying us a visit!

Network	Visits	Pages/Visit	Avg. Time
microsoft corp	94	4.106382979	155.606383
sas institute inc.	42	6.666666667	294.7142857
acxiom-claritas	7	5.857142857	427.2857143
tableau software	6	1.833333333	171.5
kxen	4	18.5	981

PS: we reviewed Google Analytics terms of service to make sure it is ok to publish this type of information.

Update: for some unknown reason, we received a lot more visits (over 700 from Microsoft).

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