Product managers have to strike a balance of meeting a few core obligations: decreasing time to market, driving product value, and delivering a realistic roadmap. They are accountable to not only the CEO but to end-users who use the product, and either manufacturing, supply chain, or DevOps and software developer teams (if the product is application-based). Data is the nebulous yet defined information that, if organized, can speed up product road-maps, deliver more features on a tighter timeline and budget, provide clear direction to your directly-related teams to guide their processes to bring the product/feature to market faster, and much more.
Let’s say that you’re the product manager for an application company. You want to understand why user adoption is plateaued, what features would make your application more appealing, and also how to give direction that is backed by evidence; this is where analytics comes into play. Your core application is receiving all sorts of data from first-party users. If that first-party data was organized in charts, graphics, or easily digestible ways, you could use that data to extrapolate insights like why users don’t use a particular app feature. From there, you can bounce those ideas with your Executive team, then make actionable recommendations to your Development team.
You don’t need to be a technical data scientist; data democratization is the future, and the ability to garner insights from dashboards should not be siloed. Utilizing user or customer-data is another skill that will set you apart as a product manager and as a leader. Here are three ways that you can use data to your advantage:
To Recognize Trends And Patterns
Let’s say you want to become a doctor, and you’re taking the MCAT practice test. The old adage is to practice, practice, practice–but simply taking practice tests is not going to suffice if you want to score high. What would be more effective is to look through all of those practice tests and recognize patterns; what kinds of questions do you tend to get wrong? Is there a recurring feature among those incorrect questions? From there, you can classify those wrong questions, let’s say loosely as ‘matching” type questions, and then in granularly, questions about organic chemistry. You can keep practicing those types of questions separately, then think of exercises that could strengthen that area. This is how you should approach extrapolating insights from data generated from your product’s consumer use. Recognize patterns, find correlations, and classify them. It’s the first start to finding meaning and improving upon that new revelation.
Find A Surprising Discovery (Data Outlier)
Connect dots that aren’t connected. Find the surprising or unexpected object, event, or behavior in the data. Even small changes or adjustments can make a large impact. For example, a Dutch Airline called Transavia Airlines found that a customer of theirs who was a repeated flyer always had a companion ticket, which they discovered, was her young daughter. What was unusual about a particular day she was flying was that it was her daughter’s birthday. When the flight crew was given this information, they went and bought a birthday cake and sang Happy Birthday to her. Someone on the plane filmed this, and it went viral, contributing to overall brand awareness, loyalty, and an increase in passenger demand.
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Find Associations
You may find that unusual data has some association with “usual” data. For example, you might find that there are fewer sales or lower user adoption because of a change in weather or a solar eclipse. Even something as random as this can give you an idea of what else to take into account when suggesting changes or improvements to your product. Data is about listening and understanding your customers and users. Listen to what they need and discover what subtle things impact them, and figure out ways to better serve those needs. If you get better and better at this, not only will your user adoption/customer satisfaction improve, but you will build loyalty because of your commitment to a better product that ultimately serves the interests of your consumers.
Data literacy is a skill limited to just data scientists and analysts; it’s a necessary life skill that can take you far. As a product manager, this can set you apart. You are communicating across departments and to different stakeholders, but being data-driven in your suggestions and ideas takes you to level up as a leader. You want to be able to communicate your ideas confidently but intelligently, and the numbers can help you do that.
