Make your analytics accessible to everyone

Everyone in the company, no matter what their technical skill level is, should be able to answer their own questions about the business at a basic level. This means having a UI (usually third party in the early days), but it also means being conscientious about how the data is formatted. Using plain, clear language throughout is a good first step to making sure technical knowledge isn’t necessary to answer a question.

Track user intent, not just business metrics

There’s a temptation when engineers are implementing analytics to follow the technical architecture, not the user journey. This can lead to analytics that are only understandable to the people who wrote the code.

Tracking user intent, rather than simply tracking a handful of business metrics or technical operations, increases the chance you’ll be able to answer questions about your users in the future as well as the ones you have today, and makes the data more accessible to non-technical folks.

Stay flexible with tool choice

Different third party tools are good at different things, and we never managed to find one that did everything we needed.

Abstracting your analytics calls to a manager makes it easier to add and remove services without touching too much of your codebase, and if you want to take it one step further, many platforms have an HTTP spec and can receive events from your server. Passing events to your own server means you can switch new services on instantly for all clients, without waiting for app updates, and lets you store the data in your own database too — critical if you want to be able to clean up any errors in the data later on.

Test methodically

Analytics needs thorough testing because inaccurate tracking can invalidate your entire data set, or even lead the product and business down the wrong path.

Use a tool that has a live view of analytics events rather than a delay on tracking (Mixpanel is what we used), where you can see events coming in as they’re performed. This makes it easier to manually go through the different user paths and verify that the logging is correct as you go.

Run statistical analysis

Funnels, segmentation and retention analytics are useful but they tend to require that you define the question first. Statistical analysis on your data using services like Statwing can expose correlations in your data that you didn’t know were there, and lead to questions you didn’t think to ask.

Beyond analytics

A good analytics implementation is a fantastic base for a slew of other tools. A/B testing (and all its variations) becomes much more powerful when you can measure its impact precisely, and it also becomes possible to build out context sensitive app behaviors and notifications without much difficulty.

But ultimately the biggest reward is the freedom to ask questions, and get the answers quickly. A good analytics implementation will help you and everyone else in the company understand your customers and your business. So take the time and get it done right!

 

Title image is a co-authorship network map of physicians publishing on hepatitis C, by Andy Lamb. I left it enormous, because I read this.