Product School

What is Product Analytics?

Product Analytics is a type of data analytics that measures and provides insights on how users interact with a product, in order to inform product development and decision-making. It’s used to understand how users convert to customers and drive revenue.

Product Analytics in Practice

Product Analytics is the process of getting data about a product in order to understand how users are interacting with it. This data usually has to do with users or customers, providing insights into how they’re interacting with your product and why. Although the steps below are displayed as a list, it’s important to note that the most effective way to conduct analytics is on an ongoing basis. This way, you can iterate on your goals and continuously check in with users’ experience. 

How is Product Analytics Applied?

To apply Product Analytics follow these steps:

  1. Identify the overarching goal. Data is only useful so far as it’s used intelligently and with purpose. What is the question you need to answer or decision you need to make?

  2. Set metrics that measure progress towards your end goal. Once you’ve identified the end question you need to answer, it’s time to decide what is most helpful to measure. Choose a few of the most relevant key metrics. Not too many – you can end up lost in the numbers if you start measuring too many things. Make sure you understand why you’ve selected these metrics and what they prove. Use past data to create a benchmark where applicable, and set estimates for expected results.

  3. Collect data. You can collect qualitative data through research (i.e. surveys, interviews) or quantitative data via application instrumentation or embedded analytics. Data collection is typically the point wherein you begin to establish your data architecture; ensuring your data is organized, clean, and ready for analysis. 

  4. Analyze data. Using the data you collected, look for patterns or insights that help you understand your product’s performance. Try to understand how users are engaging with your product and why. Measure how you did, using the metrics you selected, and flag any glaring issues. You’ll usually collaborate with your Product Team to do this analysis, but depending on the size of your team or the complexity of the project you might need data scientists dedicated to this task. 

  5. Repeat! As we said above, analysis is iterative. If you’re able to collect and analyze data on an ongoing basis, you’re more likely to catch nuances in product performance

When to Use Product Analytics:

Product Analytics is relevant before, during, and after a product initiative. Dive into the data when: 

  • You have a hunch or hypothesis you’d like to test.

  • You have no hunches. You have no idea where to start, so you start by collecting information.

  • You need to back up your decisions with stakeholders. You know you’ve made a strategic decision, but someone in your team or leadership is unconvinced. They can argue with you, but it’s hard to argue with the data!

Product Analytics in Action

“With the power of Product Analytics, we identified key moments in our user flow where we were losing customers. Since testing and updating our UX, our drop-off rate has decreased dramatically.”

Product Analytics Videos

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