Product Analytics

Many businesses today are still stuck in a web analytics ‘page view’ paradigm. They are not taking a user-centric view of their data. A user-centric view is not enough, though.

The main reason to use product analytics tooling is to have a user-centric view. Having a user-centric view from your product analytics tools provides the understanding needed to build a customer journey that makes sense, and to tackle things like optimizing conversion rates across key touchpoints.

Product analytics is the practice of gathering, analyzing, and interpreting data about the usage of digital products (e.g., smartphone applications and web applications).

Most product analytics provide the tools that examine the behaviour of users within your product. This provides critical information to optimize performance, diagnose problems, and correlate customer activity with long-term value.

Organisations of varying maturity will have differing needs for product analytics. The needs for a start-up are qualitatively different from a large enterprise organisation.

Product analytics has its roots in the early 2000s when the first web analytics tools became available. Over the years, product analytics has evolved, becoming more sophisticated and powerful as technology has advanced.

By the early 2010s, the term “product analytics” was being used to refer to the data collected and analyzed to inform product decisions.

With product analytics, teams can measure a broad range of product usage data such as: user engagement, feature adoption and usage, user flows, funnel analysis, and A/B testing.

The most famous model capturing the above is the AARRR model aka pirate model. Pirate metrics, or “AARRR”, is a term coined by entrepreneur and investor Dave McClure.

Other approaches like the HEART framework designed by Kerry Rodden, Hilary Hutchinson and Xin Fu, from Google’s research team can also be used within a product analytics set-up. The idea is a simple one; to deliver a series of user-centred metrics that allow you to measure the user experience on a large scale.

The value of product analytics is becoming hypothesis driven. Product analytics’ most valuable application is in discovery. It allows your Product Managers to sift through data to uncover new insights.

All the data in the world is no good if it’s impossible to use. For your data to be maximally valuable, it needs to be clean, organized, and consistent i.e. data quality needs to be a first rate issue.

With high quality data it is possible to leverage product analytics and influence how you make decisions.