Last August we released an Adobe Air app called Hairball. Hairball allows you to perform complex list segmentation queries offline on your own machine. When you’re done, you can push a static segment containing the results back to your MailChimp account. In depth installation and use discussion for the previous version can be found in the Hairball knowledge-base article and How to Use Hairball guide. Today, we’re happy to announce a second version of that software which is available for download here:
Along with various bug fixes and speed improvements, the new version of Hairball includes some fancy new features that users have been asking for. Specifically, the new features beef up what Hairball can do with campaign-performance data, allowing users to take the first step in interest-based segmentation using historical reader engagement data.
Comparing segment performance on past campaigns
Hairball v2 allows users to browse through their campaign stats to see how many people opened, clicked, and ignored a particular campaign or link. This allows the user to focus on segmenting a list with regard to those campaigns that were perhaps successful or unsuccessful in the past. Furthermore, in Hairball v2 we allow users to compare the performance of the segments they create to the whole list with respect to past campaigns. Simply click the “Compare to a Hairball Segment” dropdown when viewing the performance of a past campaign and select those segments you’re interested in:
Further segmentation based on the comparison results can be performed without leaving the comparison screen. For example, if I want to target those 77% of high-member rating recipients who didn’t open the campaign I sent (orange bar in the chart), I can click the value on the table below and select “create segment.”
Segmenting using historical recipient data
Now let’s say you sent a campaign last month and you were a bit too aggressive in your segmentation or had a bunch of subsequent signups to your list a few weeks later. With Hairball v2, you can segment by those on a list who did or did not receive a particular campaign or set of campaigns. To do this, you’d click “New Segment” under the list title:
The dropdown menu on the rule allows you to choose from newsletter subject lines that you sent to your list. Once you create that static segment and sync it back to your account, you’re free to let those recipients know what they missed out on. Just as in the previous version of Hairball, you can segment lists not only on who received a campaign but also who opened, clicked, or didn’t open/click. We’ve changed that only slightly. Hairball used to consider someone to have not opened the campaign even if they didn’t receive it (perhaps due to a segmentation query laid on top of the list). Now, when you ask Hairball to give you the list of emails who didn’t open a campaign, it gives you the list of email addresses who actually received it and chose not to open.
Segmenting on click data
So we’ve covered receiving, opening, and clicking. But what if I want to get more specific? Perhaps I’m a publisher who sends out a digest of my week’s content, and I don’t just want to grab who clicked any old link but who clicked on a particular topic. This is where Hairball v2 gets Rad. Let’s say we’re interested in segmenting our “MailChimp Blog Updates” list to find those readers who clicked a link involving our new transactional product, Mandrill. We can set up a rule looking for clicks on any URL containing the keyword “Mandrill.”
Note that as we type a keyword (or entire URL if that’s your poison) into the “Clicked Link” rule, Hairball drops down URLs from your previous campaigns to choose from. You can select one of these specific URLs if that’s what concerns you, or just leave the keyword alone. The latter will pull subscribers who clicked any of these URLs. This is a great way to segment those truly interested in a specific topic for some more targeted follow-up. We give you the options of matching clicks to links using the rules “is,” “is not,” “contains,” “does not contain,” “starts with,” and “ends with.” I asked for a rule based on hamming distance to a keyword and got shut down. Oh well—v3, perhaps.