Top Analytical Skills Every Product Manager Needs in 2018
Top Analytical Skills Every Product Manager Needs in 2018
As we head into 2018, we offer up four analytical skills that every PM can master to take advantage of these new tools and position their products for long-term success.
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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.
1. Creating Custom Metrics
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.
Goals ensure that metrics are rooted in the business and act as a benchmark for success. Goals include both user and business goals. User goals are related to how people interact with the product like time on site, while business goals focus on things like revenue, costs, and product success.
Actions focus on what you want your users to do with the product. For example, you might want to understand what actions your users take to get the most value or what actions provide repeat visits. This is a qualitative exercise where you can brainstorm and prioritize the actions that you think are important.
Metrics take user actions and turn them into measurable values that can be tracked within your product. Once you've decided on your top metrics, it's important to evaluate your technical capabilities to ensure you can collect and analyze the necessary data.
Evaluation helps determine if the metrics are useful and drive value. You will want to evaluate if the metrics are flagging issues, monitoring the intended user behaviors and correlating to the overall success of the user and the business.
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.
2. Asking Investigative Questions
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:
To invoke creativity you should seek to ask questions with no common, quick-to-address answers; questions which will allow our thinking to diverge rather than converge. The benefit of focusing on investigative questions is their ability to help connect ideas which you may not first perceive as having been connected, rather than mere information gathering.
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.
3. Making Data-Informed Decisions
Product managers are problem solvers, which means they often need to make tough decisions to push the product over any barriers. But how do you know if you are making the right decisions?
Making decisions based on your data is not a cut-and-dry exercise where the data will simply give you the answers you need to make a decision. Data will arm you with information, but it is usually just a snapshot of what's really going on. This is the fundamental difference between data-driven and data-informed decision making.
Data-driven PMs who solely rely on their data run the risk of making decisions in a vacuum, void of external context, intuition, and experience. On the other hand, PMs who make data-informed decisions use both data and external judgments to come up with testable questions.
Once you've developed a hypothesis, effective decision-making comes down to how well you interpret the analysis in the context to your questions. Your data should help you reject your hypothesis rather than prove it to be true.
Let's say you need to decide whether to remove an underperforming feature in order to save costs — but the challenge is that many users still love the feature.Will those users abandon ship if they lose that feature?
As you interpret your analysis, you will never know for certain if the users will leave, but you can gather enough evidence to determine the likelihood. It helps to ask these three questions of your conclusion:
- Does the data sufficiently answer the question? How?
- Does the data help you build a case for your decision? How?
- Are there any outside variables that you haven't considered that negate the data?
If you can answer these questions with a high degree of confidence, you'll have the information you need to decide the best course of action.
4. Communicating Analysis Effectively
Product managers are the glue that keeps every stakeholder, including management, focused on a common vision for the product. But keeping people in the loop is a different skillset than articulating your data analysis.
At the same time, having strong analytical skills means little if you can't explain your insights in a way that helps others understand your logic. While most professionals are accustomed to hearing about data, don't assume that your conclusions speak for themselves. You need to connect the dots.
The way in which you communicate starts and ends with your audience. Product managers have many audiences, which means you often need to create different versions of your reports to work with your various stakeholders. The way you communicate to the executives will be different than the development team or sales and marketing.
Your audience will influence how you present your work, what details you choose to include, and the tone you use to engage the group. While knowing your audience is key, there are certain principles of cooperative communication that will help you effectively present your analysis with any group.
Anticipate your audiences' expectations and craft a message that will resonate with them. It will require some extra work to craft several versions, but it will save you a lot of time and heartache on the backend.
Finally, an often forgotten aspect of communication is active listening. PMs who successfully articulate their analyses and decision-making logic also spend a lot of time asking for feedback and perspective to keep them honest. There is a fine line between confidence in your analysis and stubbornness. When you are willing to listen, you will gain an extra layer of insight.
Solving Problems at the Speed of Thought
The evolving skill sets of product management are moving away from data science to a proficiency in critical thinking and reasoning. That said, if you don't have access to the right tools that analyze data quickly to answer your questions, these analytical skills lose their importance.
Behavioral and product performance data are plentiful, and choosing the right tools to collect, process, and analyze this data can be the difference between being stuck in the backseat of data crunching or in the driver's seat of using the data to solve problems and optimize your product.
Published at DZone with permission of Aditya Vempathy , DZone MVB. See the original article here.
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