Product leadership requires exceptional decision-making skills. Depending on your team culture and environment, the number and difficulty of these decisions can vary. In agile development paradigms, key product decisions are made more frequently, with each sprint requiring clear action items. On decentralized/globalized development teams, product decisions tend to be more complicated. However, no matter your team’s specific situation, any decision you make will have negative ramifications if it is not informed by data.
Some product decisions are more trivial, such as what tool to use to create your roadmap. Others, however, are incredibly impactful, not just for your team but for the entire organization. Examples of these “big decisions” include whether to:
- Invest in a new market or segment
- “Buy or build” your product’s net new capability
- Move forward with your latest product launch
Any successful, product-driven organization uses data as the foundation for feature ideation and development. Often, the hard numbers will challenge your team’s thinking on how the customer is interacting with and experiencing the product. And this is a good thing. Any product manager who is the champion of their product knows that cognitive biases get in the way of good decision-making. That’s why it’s so imperative to apply data to every product decision.
In this article, I would like to share four key ways your team can use product data at various stages of the product development lifecycle.
Product hypothesis and discovery
Problem-solving is at the core of product management, and the average product person is dedicated to addressing their users’ main pain points. A PM’s role is to deliver creative solutions that have a measurable, positive impact on the customer’s goals. But how do you decide which problems are the most critical to solve? And how do you determine whether now is the time to craft a particular solution?
As you ideate on potential features and improvements, build hypotheses that you can validate with product data. Use these experiments to tell the story behind your customer’s specific problem and predict the impact solving it would have. All of this starts with identifying a pattern from the data you have/observe. Data is the foundation of your storyline and the basis for your product strategy.
Feedback loops and sampling analysis
Do you remember the Gmail BETA? Check out the Gmail email solution that’s now used by over a billion consumers and compare it with the 2004 product launch. The road from its initial form to its current version involved a huge amount of data analysis, likely in the forms of A/B testing, product usage analytics, and post-launch surveys. Over time, Google used this data to build an email solution that delights its users and makes their lives easier.
While these types of user testing are most common in B2C companies, B2B firms can also make use of them. In fact, a number of software companies offer design partner programs where they recruit customers to test and validate the features of a beta product before the product gets announced as GA (general availability) in the marketplace. Whether you work for a B2C or B2B organization, do your research and find opportunities to analyze data whenever you can.
Data and behavioral analysis
I like to keep the following statement in mind when considering my product’s user experience: Harnessing behavioral data from customers on the usability of the product can drive net new CX flows. Product managers, UX researchers, and designers must collaborate to research and understand what customers need. Then, they should work together to envision solutions and infuse these insights into your product.
Customer experience or usability improvements should be based on data, not gut instinct. And remember that product management is not a solitary profession! Be ready and willing to accept ideas and even help from other departments. Their expertise can help you build a truly data-driven product.
Tracking, measuring, and reporting
In a B2B world, sales and marketing teams get applause for closing deals and building pipelines. However, at B2C firms, it is more about acquiring new customers. Other key metrics include usage frequency and retention, which both contribute to topline sales. But this wouldn’t be possible without the product features behind the scenes that drive adoption and conversion.
As a PM, you should define KPI and metrics which are quantifiable and measurable. This is true whether you work at a B2C or B2B firm. Next time you launch a new feature, measure these metrics that tie product engagement data to dollars and customer outcomes. Customer conversion, product retention, and engagement scores are just a few of the numbers you should track in order to make data-informed decisions.
Data over assumptions
“Without data, you’re just another person with an opinion” – W. Edwards Deming
The above quote from famed engineer, statistician, and author W. Edwards Deming is one I think of often. In its earlier days, product management was very much a field driven by gut instinct. Nowadays, PMs must make decisions based on data, not emotion. Luckily, we have more product data at our fingertips than ever before. Make the best use of your metrics and you’ll see positive outcomes in your product’s performance.