What is Product Analytics?

The term product analytics refers to capturing and analyzing quantitative data through embedded tools that record how users interact with a product.

This type of usage data can include the most frequently accessed features of a product, the average time users spend taking a specific action, and a map of each user’s journey through the product.

Product analytics is the process of analyzing how users engage with a product or service. It is a framework for putting customers at the core of a business by analyzing behavioral data, identifying opportunities for conversion, and creating impactful digital experiences that bring about high customer lifetime value.

Product analytics lets your team track, visualize, and analyze real-time engagement and behavioral data so you can optimize your complete customer journey. You can go beyond vanity metrics and tie every step of the customer lifecycle to a concrete data point—empowering your team to improve the digital experience, win customer loyalty, and tie digital bets to business impact.

Product analytics is a type of business intelligence software that captures and exposes usage patterns from digital products like web and mobile applications via event tracking, event properties, and event and property grouping. This data informs decisions about how to improve the product experience, increase product engagement, and drive business outcomes. Usage data tends to be more reliable than user surveys and product testing alone.

Product managers are passionate about product analytics. After all, you want to deeply understand your customers. And you want to determine how well your product solves the problems it was designed to fix.

Product analytics focuses on quantitative metrics. For example, you might dig into usage metrics for a set of features or identify friction points in your trial signup process. Analytics helps eliminate guesswork by giving you data-driven reference points to guide your decisions.

Of course, you will still need to gather qualitative feedback — from customer calls, support tickets, and an ideas portal. Together qualitative and quantitative data paint a full picture of your customer's experience.

In order to get a quantitative understanding of what users are doing with your product, the first step is instrumenting it with product analytics. The idea is to fire an event for every action that a user can take in your product so you get an aggregated view of how many users use a feature, and how often they're using it.

In-product analytics can help you pay off empathy debt in two ways: with qualitative feedback gathered through activities like concept testing and customer interviews; and with quantitative data collected in-product with things like product analytics and NPS surveys.

Testing the future before it's here

While adding product analytics can be valuable for understanding how users use existing features, they're also extremely valuable for testing new features and experiences. If you have a clear goal for how much you want your feature to be used, having product analytics helps you work towards that old agile mantra of failing fast and iterating until you succeed.

The process that we use generally looks like this:

• Define a clear hypothesis for a product change – e.g. "By increasing the size of the comment box we expect to see a 5% increase in commenting."
• Build the cheapest possible implementation of this change, loaded with any analytics events we need, that will allow us to test our hypothesis.
• Deploy the change to a subset of customers in an A/B test.
• Twiddle our thumbs while we wait for results.
• Do a breakdown of the results, with the help of an analyst in the case of more complex changes, and decide whether the change was successful.

Just don’t forget to listen to your users, too —- As I mentioned, it’s great to be data-informed, but being entirely data-driven can sometimes leave you blind to the overall experience that you’re creating for users. Being dependent entirely on data can also be a bit crippling when it comes time to make a decision and you don’t have all the data you need.