Best Practices

The ‘gray area’ of users and 3 other challenges data-driven product managers face

Published Jul 23, 2020

As users’ expectations of the software products they use (both in their personal and professional lives) continue to rise, product managers are turning to data to better understand what customers want from–and how they engage with–their product. But like many things in life, it seems every PM (or at least, every company) has their own way of leveraging data.

For our latest e-book, we interviewed ten product leaders and heard a range of perspectives on the modern product manager’s relationship with data. Since one of the best ways to learn is by knowing the common challenges those in your position face, we also asked these experts what they believe is the most pressing obstacle for a PM when it comes to using data.

Here are the top challenges we heard (plus how to overcome them):

1. Ensuring you have the right data

A challenge that came up in multiple conversations was the idea of having the right data to make product decisions. Specifically, product managers need to make sure they understand the data they have (or the data they need to get) at the beginning of a project. Travis Turney, senior data strategist at Rapid7, noted that you can’t just tack data on at the end–this can lead to using data to confirm an existing idea, rather than using it to define what the idea should be.

And while it’s crucial to have confidence that the data you’re using is complete enough, more data isn’t always better, and can actually lead to PMs using the wrong type of data to try and understand something. Product managers should first understand what the problem is they’re trying to solve, and then look at the data through that lens. As Travis put it, “it doesn’t always mean that you have to have more data. Sometimes, more data can actually be worse.”

2. The ‘gray area’ of users 

As Sam Benson, product operations specialist at Firefly Learning, put it: “There will always be a gray mass of users–those in the middle between extremes in a dataset, and it can be challenging to identify how to best serve these users.” 

While it’s difficult to know how to engage this segment of users, that doesn’t mean you should ignore them altogether. Your attention will naturally gravitate to your very most and least engaged customers, but it’s important not to skip over this middle group–the way they interact with your product can offer valuable insights, too.

3. Internal alignment around the data

Since product data is meant to be shared and used for collaboration, there’s always going to be a challenge in aligning different perspectives on what the data means and what the go-forward path should be. Manosai Eerabathini, product manager at Google, said this can be especially common when you ship the first version of a product and the data is telling you something that invalidates the qualitative research you placed your bet on. 

The solution? He says: “The conversation should then be based on what you know now, and what you can do to put yourselves back on the right path–it’s okay to be wrong, but now the focus should be on what to do next.”

4. Getting face time with enough customers

One of the best ways for product managers to test their hypotheses is by hearing from customers directly. But, it can be a challenge to find enough customers to meet with and interview about a particular product, feature, or use case. Since this type of qualitative data is so valuable, Viraj Phanse, senior product manager at Amazon, believes the key is to develop a strong relationship with your sales, marketing, and support teams, since they’re on the front lines with prospects and customers every day.

He explained: “These teams have direct access to customers and by attending sales calls, escalation calls, or events, product managers can build relationships with customers directly, opening up channels for feedback to help guide the product roadmap in the future.”