Introducing much better segmentation
Today we are announcing a new feature called Segmentation that will enable you to analyze your data retroactively and perform even more complex data analysis then ever before--of course it's all in real-time. This is a huge step towards our overall goal: To help the world learn from its data.
To get to segmentation click on the new segmentation icon on the left hand side where events, retention, funnels and the rest of the icons are.
Segmentation is extremely flexible and therefore it's often tough to describe which problems it may solve because it solves so many. To help introduce this new feature we'll go over some simple use-cases that, hopefully, will excite you.
1. The ability to intersect and drill down on more than one segment.
A really simple example is if you have an ad campaign on Facebook and you would like to understand the demographic of users who ultimately purchase. Here we're curious and filter on users who have seen a specific ad campaign that we've created and are only female purchasers. More importantly we are curious to see the age distribution or breakdown of that type of customer.
This is extremely powerful because not only are you seeing deep insights into your data but you're doing it in real-time on potentially a large set of data. It's generally a pain to wait to see the result of a question you have about your data because as marketers or analysts we generally ask our questions iteratively until we have that "Ah ha!" moment.
Keep in mind that Ad Campaign, Gender, and Age are defined by us. You can define any segments you'd like. For example, if you were a social game you could define character type, source, and item purchased instead.
2. Analyze data that is numeric
Segmentation now accepts any kind of data that may be numerical. If you know the number of invites a user may send during their sign up process to increase virality of your product, you can now see a statistical distribution of that. This is extremely powerful and helps you understand your virality. In general, an average may not be truthful enough.
Other examples of numeric data you can send us: age, page load time, number of items selected in a list, anything with money involved, coins or credits, etc.
We also support true/false values and have plans to support dates soon which will let you ask questions such as "Everyone who signed up after this date."
3. Completely retroactive
If it wasn't already apparent this type of analysis is fully retroactive. You do not need to define any queries/questions up front as we will look back at data you've already sent us and analyze that.
We believe iterative data analysis is what our customers want and is a great way to find insights in your data.