{"id":10196,"date":"2016-08-29T17:22:43","date_gmt":"2016-08-29T16:22:43","guid":{"rendered":"https:\/\/blog.intercom.io\/?p=10196"},"modified":"2020-07-30T12:59:17","modified_gmt":"2020-07-30T11:59:17","slug":"closing-gap-product-analytics","status":"publish","type":"post","link":"https:\/\/www.intercom.com\/blog\/closing-gap-product-analytics\/","title":{"rendered":"Closing the gap between data and product development"},"content":{"rendered":"

Our product analytics team are creatures of habit: every morning we go to the same coffee shop.<\/p>\n

The barista recently asked us to try a new coffee, but when we tried it, one of us made this face: ?<\/p>\n

The information transmitted with that single, involuntary expression was likely worth 10x more than a verbal response.<\/p>\n

Our goal is always to make data make sense to everyone in the company.<\/p>\n

We often explain Intercom\u2019s value this way: talking to customers on the internet should feel like talking to your local barista in a coffee shop. But moments in a coffee shop aren\u2019t only about conversations. In the moment, humans observe and capture small, non-verbal interactions as they communicate.<\/p>\n

That\u2019s what a good analytics event does: it gives us a deeper view of an interaction. If you have a cleverly instrumented product, and data that makes sense, you can interpret why an interaction happened. Then you can decide what to do about it.<\/p>\n

What we talk about when we talk about data<\/h2>\n

In product analytics<\/a>, we collect data about how people use our product. That data can then become a living link between our users and product development.<\/p>\n

To make sure that happens, our goal is always to make data make sense to everyone<\/em> in the company. A deep understanding of the data helps product teams improve the product.<\/p>\n

The data we collect are sent to us as events. As well as letting us send more personalised messages to our users, these events show us patterns of use \u2014 in the Intercom app and the Intercom Messenger \u2014 that help us improve how we build our product.<\/p>\n

Until recently, we had about 350 of these analytics events set up across the Intercom app and Messenger. But with new product shipping all the time, our process for naming those events was pretty fast and loose: you needed deep context to understand them. Plus, with no underlying structure it was impossible to link events together to identify reactive and proactive behaviours.<\/p>\n

Here are a few examples of these confusing event names:<\/p>\n

keyboard-shortcut-switch-tab-comment
\nunauthenticated-warning-hovered
\nconversation-controls-saved-reply-inserted<\/strong><\/p><\/blockquote>\n

Clearly, our event names failed a key principle of analytics: they made very little sense to anyone but the analytics team.<\/p>\n

Making our data democratic<\/h2>\n

Our product is purposely built to help people understand customer behaviour, so we\u2019d long been aware of the irony that our own data was hard to understand.<\/p>\n

We don\u2019t have to spend ages trying to figure the\u00a0data\u00a0out.<\/p>\n

We also wanted to do something different with our data: rather than looking at every event independently, we wanted to better show the journey our users took in the product.<\/p>\n

That\u2019s why we started to explore a new solution: moving from a flat list of event names to a reusable and structured set of categories.<\/p>\n

We thought this would be a technical challenge, but it was an information challenge<\/em> more than anything. We needed content strategy and analytics to work together to understand these categories.<\/p>\n

We ended up with a structure whereby each category had an: Action, Object, Place, and Owner.<\/p>\n