The Power of Data-Driven Product Management

Anything “data-driven” really just sounds like a cliché at this point of the game. Oftentimes, it involves poring over spreadsheets, charts, and other analytics just to come up with a list of insights that don’t mean jack.

But since we’re entering a new age of business where we’re got big data and AI knocking at our doors, we wouldn’t want to greet them unprepared. These tools make “data-driven” a lot more of a reliable and tangible approach, where we can boost product features and appeal and expand our reach.

Want to give data-driven a new meaning in your business operations? This post will go over:

• The definition of data-driven product management
• How you can make data-driven product management an MVP 
• When to move away from data-driven

What is Data-Driven Product Management? 

With the data-driven approach to product development, PMs back just about everything they do with insights from data collected through sources like customer feedback, measured KPIs, and market trends. Essentially any source that you could extract valuable information from has the go-ahead.

A lot of people are under the opinion that anything data-driven is fussy and over-complicated, and that’s one of the reasons why businesses unfortunately shy away from it. The truth is that it can really be as simple as studying sales patterns to figure out future demand or looking through customer reviews to improve what you’ve got, especially if you employ the right tools and strategies. It’s not always about mining through datasets or what have you – it’s more about using the heaps of information you’ve got at hand.

6 Tips to Do Data-Driven Product Management (Better) 

If you want to do data-driven product management the right way, it might mean erasing all the things you think you know about the concept and starting from scratch. Ready to expand your mind? Here’s how you can do data-driven better:

1. Gather high-quality data: Not all data is good data, but it does help to have a variety to start with. The rule of thumb is to sift through a bunch of sources and find data that tells a story related to your product. Perhaps you’ve taken the initiative to conduct usability tests and customer discovery sessions and found that context switching is a real problem for some of your users – there’s an instant opportunity right there to implement a new feature.
2. Infuse your data with a human touch: Too many times, researchers focus too much on looking for data that sounds good or looks good, rather than digging deeper and figuring out what those statistics actually mean. Those numbers need to be interpreted from a real-world perspective. For example, increased cart abandonment might not mean that people aren’t happy with your selection – it could actually signify that you need to rejig your checkout process or offer more payment options.
3. Embrace the endless process: Data needs decoding, as we’ve covered above. And it can take a lot of trial and error to figure out what it’s actually telling you. If we take the same example of cart abandonment, experiments could involve working with a new payment processor, simplifying the interface, or even offering small discounts through email triggers upon abandonment. It’s important to deploy just one at a time, so you can analyze the impact, make any tweaks, and move on if needed.
4. Make sure your entire team understands the data: Not everyone will be well-versed in data, but a data literate team will certainly give you a leg up over the competition. Any findings you take away from your research should be instantly shared with all co-workers – and it’ll help to speak their language. From the insights you gathered, what’s most valuable for the design team to know? Or the IT team? Or even the PR team?
5. Get the right tools for the job: Analyzing data from so many sources and in so many ways can get overwhelming without tools that streamline the process. Ideally, you’ll want tools you and your team are comfortable using, with plenty of room for customization. Fibery, for instance, doesn’t confine product teams to a rigid framework. It lets teams interact with and manage their data in a way that feels right to them with features that simplify data analysis.

6. Embrace the unexpected: Once in a while, you might come across surprises from your data that you didn’t expect. Even if you or your team was previously convinced of a potential fact, you should always welcome any surprise as a hidden opportunity to do something innovative. Anomalies can often be the secret to changing your product for the better or find a new way to appeal to your audience.

When Data-Driven Becomes Counterproductive 

There comes a time when data-driven can turn into data-obsessed – and that’s nothing but counterproductive. Reeling yourself back during these scenarios will help you not get sucked into the black hole of data.

• Experiencing analysis paralysis: Spending days, weeks, or even months going over charts and making correlations can lead to an “oh no” moment where you’ve realized you wasted so much time analyzing and no time making things happen. Too much analysis can lead to stifling creativity and a poor product development process, so be sure to establish timeframes.
• Making too many tweaks: With so much data in front of you, you might be tempted to continuously tweak even the smallest aspects of your product. Too much of that, and you can end up with a worse product or wasted time with no returns.
• Losing sight of the bigger picture: An incessant obsession with data could result in forgetting about the bigger picture. Along with data, what are business or societal trends leaning towards? Are there any new market dynamics you’ve missed that could completely change your product management tactics? Take a breath – and remember that there are factors other than quantitative data.
• Becoming data-blind: Data can really only tell us what’s happening but not always why, especially when it comes to more intangible things like customer sentiment. No matter how many heat maps, graphs, and metrics you look at, sometimes it’s intuition and human insight that will fill in the blanks for you. 

The PM’s Hot Take 

Data-driven product management involves principles of both art and science – and the best approach to data-driven practices will keep that in mind. Having the ideal tools, high-quality data, and learned tactics are non-negotiable in data-analysis, but at the same time, it’s just as valuable to trust your instincts and put your experience in the field to work. 

It might even happen that you’ll need to go against the data in the sense that the numbers might convey one reality, but your years of PM experience tell you that implementing a certain feature in response will throw things off the rails entirely. Data is extremely useful, but wisdom – that’s something that comes from a deep understanding of your product and customers. 

Conclusion 

The idea of data-driven product development or management is both fascinating, difficult, and everything in between. It’s something you learn and hone over time, like a fine wine or aged cheese. For newbies and seasoned professionals alike, Fibery offers a usable platform that makes dealing with data intuitive and fun. Start your 14-day free trial today and see what data-driven truly looks like.

FAQ

Q: What does it mean to be a data-driven product manager?

A data-driven product manager simply refers to a PM that uses data to back up just about every strategy and decision they make. It requires a deep dive into many data points, without forgetting a generous sprinkle of intuition and PM expertise.

Q: What is data-driven product strategy?

Adopting a data-driven product strategy means relying more on quantifiable information like user analytics and customer feedback in all aspects of the product life cycle. Rather than using data tools only in certain stages or for certain purposes, the evidence is used to inform product features and more.

Q: What is data-driven management?

Data-driven management revolves around analytics. Leaders use data to track performance, figure out what’s going on, and consequently, use those insights to plan ahead for the future. This way, business decisions are based on fact rather than gut instinct and vague predictions.

Q: How is data used in product management?

Data is used as a guiding tool in product management, helping to back decisions, predict trends, and improve how products perform. It can reveal plenty about the audience you’re targeting, how things are doing, and what product managers potentially need to do to make the product more successful.