The Role of Data Analytics in Product Management
Dave Silverstein shares the role of data analytics in product management.
We know that data plays a massive role in how our society functions. Data is the collection of facts and statistics. It can come in different forms and is collected consistently. If you have a smartphone, your smartphone collects a lot of data from you. This is how it provides you with a tailored-to-you user experience. Almost any industry gathers data to help make decisions and accurate predictions from areas like products and services to sales and revenue. The product management field is no exception.
As a PM, data analytics is a critical part of what you do, and it means a lot when it comes to your product’s success. It’s important to remember, however, that collecting and interpreting data can be challenging, and misinterpretation happens. So if you’re seeking more knowledge when dealing with data, formal training can be an option. Let’s break it down more:
Data-Driven Product Management
What is data-driven product management? A Medium article by Towards Data Science contributor Luciano Pesci states, “The product managers who seemed to be the happiest all came from organizations where data wasn’t just a priority, it was a deep part of the decision-making process at every level (some product managers took their current job BECAUSE of this fact).” Data-driven product managers base most of their responsibilities and decisions on collected data; they view data as the core of product development.
Working with data requires PM’s to have a strong understanding of data infrastructure, data modeling, and both statistical and machine learning, in addition to the equally important traditional qualities of a PM. A data-driven PM knows that building successful products with data requires much more than collecting and putting it into a data warehouse. It requires building a data strategy–laying out a plan for how it’ll be used to improve a product or service.
Data Analytics Categories
As we mentioned earlier, data comes in all different forms, and it’s generally grouped into categories.
- Data Points
Individual points of data (metrics) that are collected and measured on a specific date and time.
- Segmentation
Put users in groups by common characteristics and usage patterns. Typically focuses on technical data, behavioral data, and demographic data. Segmentation can also be customized. However, it is crucial to make sure you’re using measurable characteristics.
- Funnels
A funnel takes a more in-depth look into the user journey by measuring each step. The steps provide insight into how the user accomplishes a task and if there are any leaks in the process. Mind the Product gives a great example of online user registration. In a multiple-step registration process, a funnel will show PM’s where there’s leakage in the funnel (ex. A step that isn’t completed). When a leak in the funnel is determined, a PM can use the visual representation to investigate what is causing the problem.
Analytics Implementation
Once product data is collected and grouped into categories, your next step as a data-driven PM is implementing the analytics. Analytics implementation isn’t just a quick process, though. Planning your implementation strategy is critical and will bring all of your data points together, paving the way for your product vision and Key Performance Indicators(KPIs). Simon Cast’s Mind the Product article breaks the planning process down into four steps.
- Define product vision
The product vision essentially defines what the product is and what it does for the user. In other words, it tells you what problem the product is solving for its users. Typically, a PM should have their product vision defined already, but as a PM starts collecting data, they might refine the product vision based on the analysis.
- Define KPIs that meet the product vision
Your KPIs are the second stage in implementing your product analytics. They set performance targets for your product to reach to improve its success. For example, a KPI for a software product could be increasing active users by 25%.
- Define metrics that lead you to KPIs
After your KPIs are established, you need to uncover how your product will meet them; this is where your metrics from your data points come in. Your metrics should always be tied to your KPIs and are generally quantitative and comparative to other metrics. Comparison of metrics is critical because it lets you see trends in your metrics and make the necessary adjustments to hit your KPI targets.
- Define and investigate funnels that impact metrics
The final step is defining and investigating your funnels; it is where you’ll focus on the user journey at large.
Data is one of the most important tools utilized in the product world. It keeps a PM and their team in the “know” about their product and the impact it has on its users. When used correctly, it leads to an overall more successful launch and user experience.