In a previous post, I outlined how to load daily Adobe Analytics Clickstream data feeds into a PostgreSQL database. While this isn’t a long-term scalable solution for large e-commerce companies doing millions of page views per day, for exploratory analysis a relational database structure can work well until a more robust solution is put into place (such as Hadoop/Spark).
Before digging too deeply into the data, we should validate that data from the data feed in our database (custom database view code) matches what we observe from other sources (mainly, the Adobe Analytics interface and/or RSiteCatalyst). Given the Adobe Analytics data feed represents an export of the underlying data, and Adobe provides the formulas in the data feed documentation, in theory you should be able to replicate the numbers exactly:
The code snippet above shows the validation, and sure enough, the “two different sources” show the same exact values (i.e. differences are 0), so everything has been loaded properly into the PostgreSQL database.
Finding Anomalies For Creating Bot Rules
With the data validated, we can now start digging deeper into the data. As an example, although I have bot filtering enabled, this only handles bots on the IAB bot list but not necessarily people trying to scrape my site (or worse).
To create a custom bot rule in Adobe Analytics, you can use IP address(es) and/or User-Agent string. However, as part of data exploration we are not limited to just these features (assuming, of course, that you can map your feature set back to an IP/User-Agent combo). To identify outlier behavior, I’m going to use a technique called ‘local outlier factors‘ using the Rlof package in R with the following data features:
- Distinct Days Visited
- Total Pageviews
- Total Visits
- Distinct Pages Viewed
- Pageviews Per Visit
- Average Views Per Page
These aren’t the only features I could’ve used, but it should be pretty easy to view bot/scraper traffic using these metrics. Here’s the code:
A local outlier factor greater than 1 is classified as a potential outlier. Here’s a visual of the lof scores for the top 500 worst scoring IP addresses (vegalite R graph code):