This is the first in a series of posts I plan to do over the next month about Analytics and Django. In this post I'll walk through how I began to use a number of analytics tools to drive decisions about my site, IllestRhyme.

One of the first things I did when writing IllestRhyme was to sign up for Google Analytics. I had never run a web site before, but I was aware of Google's analytics offering. I admit, for the first few weeks after the site went live I would start at the Google Analytics page and hope to see users in the Live View. I wasn't really using the data for anything. I was using Google Analytics as a virtual scoreboard.

It wasn't until I signed up for Clicky that I started to take analytics seriously. In fact, more specifically, it was when I combined Clicky and django-analytical that I really took my first deep-dive into using analytics data for decision making. Since django-analytical already integrates with a number of analytics services, including Clicky, setup was a breeze. Sure, I could have inserted the raw code into my Django templates, but django-analytical gave me a single point at which to configure all my analytics services.

More importantly, and I can not stress this enough, django-analytical's Clicky integration let me see my users in Clicky via their contrib.auth usernames. This was huge. Now, instead of staring at IP addresses, I could follow users on their visit to the site (using Clicky's awesome Spy feature) in real-time by user name. Believe me, nothing will teach you more about you users than recognizing usernames and their associated behavior patterns. I could tell which users were hitting the site to check quickly for updates, which users hung around for a while, and which users used the site like it was crack. It was this last group that I was initially interested in.

I discovered I had about four or five hardcore users, that were on the site for hours a day. Now, part of that is just personality, but in addition, something on the site clicked for them. Since I want all my users on the site for hours a day, I decided to determine what they were doing in a more rigorous fashion. Naturally, I started coding...

I ended up writing a middleware that tracked a configurable set of users as they browsed the site and stored this information in the database. I then processed this information and organized it into "activities". If a user was going through all the new rhymes submitted and voting on them, this was 'updating'. If the user was submitting a bunch of new rhymes, this was 'bulk submitting', etc. After analyzing this information for my power users and a random sampling of other users, I noticed they were doing one thing more than any other, which I didn't even have a category for: they were following each other.

This was strange to me, as I didn't think of IllestRhyme as a community. To me it was more like a tool to get better at rapping. To some of my users, at least, it was a destination; a way to interact with like-minded people. I quickly realized the value of this and set to work adding tools that would encourage this interaction.

Using django-postman I gave them the ability to send Private Messages. I encouraged commenting by increasing the amount of Rep (virtual points representing a user's 'reputation' on the site, sort of like StackOverflow) adding comments was worth. Voting on other users' verses also received a boost.

That's just one example of the ways I use the analytics data I collect from Clicky, Google Analytics, HubSpot, and the rest to make decisions about my site. In my next Analytics post, I'll take a look at A/B testing in Django. Stay tuned!

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