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An essential guide to data-driven product management

Last updated: May 2024

You know that data matters. You know it is essential for setting goals. And you know it can bring greater clarity to your product decisions — helping you understand what is best for your product's success and the people who use it.

That much is straightforward. But for many product managers, putting these ideas into practice can be challenging. What types of data should you be looking at? And how do you make choices based on what you see? It takes effort to collect all the right data, and thinking strategically about it is one of the hardest parts of your job.

You also have to consider how to balance numbers against knowledge. Many folks believe that hard data is the most truthful and accurate measurement of success. Yes, data matters. But it is not the only thing that matters. Your values, vision, and experience are what give meaning to the metrics.

It is true that you need numbers, but numbers only tell part of the story.
You also need to incorporate insights from real humans. Your job is to build a product that makes a difference in the lives of real people — one that generates genuine love and adoration.
Brian de Haaff
Aha! co-founder and CEO

Analyze your product data with Aha! software. Start a free trial.

An example of how to incorporate multiple types of product data with a product value scorecard in Aha! Roadmaps

Read more: Introduction to the product value score

Tracking different types of product data

Once you have a shared understanding of the data you want to track and how you will bring it all together, it is worth going over the methods you will use to collect it. You want to be aligned on which tools you will use and agree on consistent parameters for pulling the data to ensure accuracy. This matters especially if you use multiple tools, as most product teams do. Google Analytics, in-app analytics, and product development software each help you measure different things.

The more you can consolidate, the better. It can be challenging — and certainly more cumbersome — to glean insights when you are looking at customer data in one tool and progress reports in another. You want to be mindful of how you organize this information.

It is helpful if you can analyze multiple types of product data in one place. (And if you guessed that we would use Aha! software as an example of how to do this, you are correct. We designed it with this need in mind).

Let's say you are a product manager working on a new feature, but you are not sure of the best way to design it. If you use Aha! software, here are some ways you could use product data to help inform your decision:

With all of this product data available in a single tool, it is much easier to analyze and incorporate it into your decisions. If you are curious to see some of this in action, check out the tutorial below.

Editor's note: Although the video below still shows core functionality within Aha! software, some of the interface might be out of date. View our knowledge base for the most updated insights into Aha! software.

Product data can offer a wealth of insight. But it does not reveal the whole picture on its own. As a product manager, it is up to you to dig into and interpret the real meaning behind it. This is also important for avoiding certain pitfalls. Data can sometimes be inaccurate or poorly sourced, or it might be accurate and still lead to poor assumptions. You have to look out for the truth.

For example, let's say you notice a spike in traffic to a support article on a recently released feature, coupled with decent initial feature usage. This could be an excellent sign that your feature is popular and your customers really want to use it. It could also mean that people are confused by how it works and are getting stuck as they try to figure it out. In this case, you might dig into other metrics for clues or even reach out to your customer success team members to get more context from what they are hearing.

Use your intuition. Investigating the "why" is how you pair product data with your expertise as a product manager — using both to guide your decisions. That is what data-driven product management is all about.

Read more: 4 smart ways to analyze product management team success

Why data-driven product management matters

In addition to supporting your daily product decisions, a data-driven approach matters in other ways too — all of which can help you be a better product manager. Here are some examples:

Goal achievement

Product data helps you keep a pulse on strategy. Most goal frameworks (e.g., OKRs and SMART goals) are time-bound and measurable. The closer you track your success metrics, the better you know how you are progressing against your goals — as well as what you could do to reach them faster.

Stakeholder alignment

Product managers work with multiple types of data, but also with multiple teams. Although this cross-functional collaboration is less quantifiable, it is crucial to successful product development. Bringing product data into your team discussions can help you be more productive, keeping everyone focused on the most pressing needs and problems to solve.

Product-building confidence

You cannot rely on product data alone. But you can use it to help validate what you feel is best for the product, uncover new ideas, or solve challenges when things are not progressing as you had hoped. Later on, product data helps you reflect on past decisions and understand what went well and what did not — so you can go boldly forward.

And remember: Data-driven product management is only effective when you have a clear vision in mind. Stick to it, and let product data be something that helps you cut through the fog along the way. Take that approach, and you might impress yourself with what you can achieve.