No matter how well you think you know your users, product managers (PMs) should always remember one thing: you are not your users. You may empathize with them greatly and meet with them regularly, but you are not the people using your product day in and day out—which is where product data comes into play.
By understanding how users engage with the product, how they feel about it, and what they want from it, PMs can make better (read: data-driven) decisions and build products that truly meet the needs of their customers. The catch? Product leaders have a lot of data at their disposal, and knowing how to use it effectively isn’t always clear cut.
So, we brought three product experts together to discuss how PMs can best leverage data: Jay Brewer (VP of experience design at Rapid7), Daniel Mintz (product and data at Promise), and Trisha Price (CPO at Pendo).
Here are four of the top takeaways from the session:
The “right” data will look different for every company
When it comes to determining what data to focus on, Mintz pointed out that it’s important to first think about what matters to your business and then figure out what types of data you need to achieve that goal. PMs won’t get very far by prioritizing data in a vacuum. In other words, start with the outcome in mind.
Brewer echoed this sentiment, and advised starting at the macro level: get an aggregate view of product data and come up with some general themes based on user activity. Knowing the goals and objectives customers say they’re looking to achieve, how does actual product usage compare? This baseline understanding of activity in the product will then fuel discussions around areas of focus, prioritization, and where to dig into the data even further.
The “right” data will also depend on your specific role. Price explained that CPOs, for example, likely want to examine data at the overall product level (and across applications), whereas the micro-level data matters a lot more to a product manager of a particular product area or feature. Since one size doesn’t fit all, remember to always think about your role and what you’re trying to solve.
Find a balance to help avoid analysis paralysis
One interesting part of the discussion centered around common pitfalls of product teams as they try to use data. Mintz offered up two ways teams often misunderstand what it means to make data-informed decisions: by only doing whatever the data tells them, or only doing things when they have data that clearly supports that decision. Neither scenario is an ideal way of operating, and the latter can even prevent meaningful decision making from happening in the first place. Price added that the key is to find the balance between letting data influence your decisions, and using data to tell the story you already know you want to tell.
Foster a data-driven mindset throughout your entire org
As any product leader knows, just because you know how important data is doesn’t mean the rest of your company (or even the rest of your team) will. At the team level, one tactic our product experts recommend is to ask questions that can only be answered with data—for example, “What’s adoption looking like for the feature we just launched?” This will force others to turn to the data and help build these practices into habit.
Price also noted that if your team takes part in any sort of quarterly business reviews or debriefs, be sure to revisit the type of information you’re asking team members to include in their reviews. Are there any opportunities to include less anecdote, and more data?
Product metrics belong in the boardroom
The panel discussion closed out with an important question: Why aren’t product metrics as common in the boardroom? Price, Mintz, and Brewer offered up some advice for ensuring product data reaches this level.
In the most general sense, product leaders need to understand (and be able to explain) the tie between product metrics and business outcomes. How do things like growth, feature adoption, and product engagement impact the types of metrics that board members care about?
Mintz suggested making the case that product metrics are leading indicators of key board-level metrics like revenue and churn. For example, if you have a freemium product, users don’t start paying on day one—they first have to use your software, see its value, and then decide to upgrade. Thus, growth product metrics are leading indicators of revenue down the line. Whenever possible, make the connection between metrics the board already looks at and the product data that can help predict future results.
To watch Jay, Daniel, and Trisha’s full discussion, check out the webinar recording here:
