Top Analytical Skills Every Product Manager Needs in 2018
Product managers are modern-day Renaissance women and men. To be successful, PMs must have a solid understanding of various disciplines in order to inspire and lead a team of disparate skill sets and personality types through the turmoil of product development to a victorious launch.
Part engineer, part coder, part marketer, and part business analyst, product managers have the ability to analyze data, communicate across departments, and understand the costs and profit margins to keep the project within budget.
Among all of the necessary skills, proficiency in analytics is perhaps the most critical — but not in the way most people think. Today’s analytical technology tools have removed many of the technical tasks, such as data crunching and visualization, which has freed up product managers to focus on asking better questions and finding more answers.
As we head into 2018, we offer up four analytical skills that every product manager can master to take advantage of these new tools and position their products for long-term success.
Product managers have access to more than enough data and plenty of tools to measure almost every aspect of their product’s performance. But in order to make impactful decisions unique to their product, PMs must learn how to create custom metrics.
Every product is unique. While canned metrics will help deliver general insights like monthly active users or number of conversions, product managers can go deeper to create their own key performance indicators that will help them measure and optimize the behaviors that matter specifically to their company’s business results.
One way to develop custom metrics is to use the GAME framework, which is a four-step process that focuses on goals, actions, metrics, and evaluations.
Product managers have access to every possible metric. The analytical PM will focus on a smaller number of metrics that measure the vital signs of the product based on what matters to their business.
In the age when data analysis took weeks, asking the right questions was a critical step in the hope that the answers would come back with relevant insights. Most of the time, the answers led to more questions that would never get answered.
Today, analytics technologies can track every user interaction within the product and answer your questions in near real time. This means PMs will get much more out of their data by learning how to ask lots of imprecise questions instead of wasting time on crafting the “perfect” question.
Understanding how to identify the clues within each answer that can lead to better questions is an iterative process that will help PMs uncover the deeper issues in the product that were not obvious from the start.
One way to approach this new paradigm is to ask investigative questions rather than factual questions. A factual question is designed to get information, such as, “How many active users do we have?” Investigative questions can have more than one correct answer forcing you to investigate all the possibilities.
For example, “What are users doing before they upgrade to a paid plan?” is an investigative question with several possible answers. When you ask this type of question, the analysis will reveal clues that will compel you to ask follow-up questions.
Former Hewlett-Packard CTO, Phil McKinney, explains the benefit of investigative questioning:
In the same way that you ask questions within Google search that lead you down a path of discovery, learning how to ask investigative questions of your data that build off each other will uncover opportunities that weren’t on your radar.
Product managers are problem solvers, which means they often need to make tough decisions to push the product over any barriers.
