What is Product Analytics?

A Data-Driven product manager is someone who arms themselves with as many facts as possible. Being dedicated to data is more about playing the long game. It's a long term practice that influences your product decisions and leads you to long term success. Being Data-Driven won't make you an overnight success.

There are a whole host of product analytics platforms out there, as well was business intelligence platforms who offer similar features. These are the most common and, arguably, the most important ones:

A/b testing offers you the chance to test out your hypotheses and improve your product based on the results. It can also help you to test out different versions of your product with different users, in order to understand who prefers what. For example, Netflix implements almost religiously.

When you start figuring out exactly how users interact with your product, how long they spend on one feature, where they drop off, etc…you’ll wonder how you lived without user tracking!

Pinterest found, through product Analytics, that men were the most Underserved demographic. The product team used this information to offer more personalized results for these users, which led to males becoming the fastest growing demographic within their user base. Segmentation can also help you to understand seemingly erratic user behaviour.

Gainsight says almost every team can benefit from product Analytics. Data gives you a common ground to begin discussions with Cross-Functional teams. If you see that no one is clicking on an important CTA, the objective truth is that no one is clicking on what you need them to click on. The next step is to take this problem and form your problem statement.

You won’t know what questions to ask from your data if you’re not 100% what problem you’re trying to solve. A problem well defined is a problem Half-Solved.

Whether the problem comes from your users or another set of stakeholders, you need to properly understand the problem. The only way to do that is through a combination of research, and empathy. This means gathering both qualitative and quantitative data. Find out how the user/stakeholder feels about the problem, as well as how they behave in response to it.

Work out how much of your team’s work hours, budget, and resources will be needed to complete the project and solve the problem. Balance this out by seeing how the Best-Case scenario (Eg, you completely solve the problem) will benefit your product in terms of Okrs, and the bottom line.

Defining the success of a project really boils down to the final part of your risk vs reward assessment. What does the best case scenario look like?

If the answer isn’t obvious, think about your company’s North Star metrics, or your team’s KPIs. If the project is worthwhile, its goals should align with either, if not both, or these.

Data science is a science. That means asking Iterative questions, testing multiple hypotheses, and using data to do more than just keep your boss happy. One key thing to remember when asking your questions is to remember that data science is a science.

Most tools offer a free trial or a tour, allowing you to test before committing. If you’re particularly interested in data visualization, check out this All-You-Need-To-Know guide. If you don’t know where to begin with choosing the right tool, consider hiring a product ops manager to your team. Still got questions? try asking our community of 60,000 PMS on slack.