What is product discovery?

Why is product discovery important?

“Growth is about proving or disproving a fundamental thesis.” – Andrew Chen, Growth at Uber

The product discovery process is important because it helps teams build products that are vital to their customers, not simply nice to have. A necessary product is one that solves such a deep and genuine need for the customer that they feel unable to live without it. Examples range from Google search to smartphones to duct tape. Many of them evolve into verbs, as in “Google it.” The advantage here is obvious: Necessities enjoy higher demand and greater success. The difference between a necessity and a ‘nice to have’ product is effective product discovery. When companies fail at this, it’s often because “they weren’t thorough enough in their customer research and based their product on assumptions not backed by evidence,” says Josh Decker-Trinidad, a product designer and researcher at General Assembly.

Test your assumptions with product analytics: A product analytics platform can supercharge your customer discovery process by summarizing your customers’ behaviors into juicy insights.

Without data to prove or disprove their assumptions, companies can waste tremendous amounts of money, time, and resources chasing unattainable or undesirable goals. It is what has led to colossal product design flops such as New Coke, Apple Newton, and the Segway. To avoid product failure, companies must conduct product discovery that’s based upon data.

What must product managers know about testing data? Read Should Product Managers Be Technical?

How do I conduct product discovery?

To conduct or improve your product discovery process, think outside the building. That is, look outside your four walls to your customers for insights on how your products should be built. Your customers are the source of all truth for product discovery because “Customers know more than we’ll ever know about their needs and context,” says Teresa Torres, design teacher and founder of Product Talk. “Our customers are the experts. They’re better at evaluating whether our solution is a fit than we are.”

“Our customers are the experts.” – Teresa Torres

Despite the clear advantage of conducting product discovery based on customer research, most companies don’t, for two reasons. First, collecting customer data and constantly working to deepen your understanding of your customers can be time-consuming. Second, customer insights are usually relegated to a few members on the product or marketing team who conduct the research. To execute world-class product discovery, teams must gather more customer data and spread those insights more evenly throughout the organization.

Cut down on customer research time with product analytics: Product analytics software automatically tracks customer behavior so you can segment and conduct research from your desktop.

The three steps of product discovery

1. Challenge your assumptions

The first step to improving your product discovery process is challenging the assumptions that underlie your current methods. For example, do your product initiatives typically come top-down from the executive team? That assumes they know what’s best for customers, which they may not. Does your product team prioritize features based on past experience? That assumes their domain expertise outweighs fresh customer insights, which it may not. Get your team’s assumptions on the table, rephrase them as hypotheses, and test them with data. 

Learn how DocuSign tests its assumptions with data.

2. Conduct empirical user research 

There are two types of customer data with which to test your assumptions: qualitative and quantitative. Qualitative data is subjective and comes from surveys, customer interviews, and focus groups, and generally measures customers’ sentiment and intent. This data helps companies develop empathy for their users and learn to think from their perspective. 

Pro tip: To spread an empathetic understanding of your customer, involve all teams – from marketing to engineering – in interviews and qualitative data collection.

Quantitative data is numerical and may come from user data and testing. It’s the “data” people talk about when they refer to data-driven decisions and it’s critical for testing the validity of your assumptions.

Take, for example, a social media app that assumes that enlarging a ‘share’ button will increase sharing. If the team doesn’t first test the results against a control group, everything they build later on which involves that feature will be founded on potentially false assumptions. It’s a house of cards waiting to come down. Untested assumptions skew design results and lead to poor performance.

Product analytics can quickly test your assumptions: A good product analytics platform is designed to collect and analyze user data to automatically reach conclusions in a fraction of the time that it takes to do it manually.

3. Create design artifacts

Product discovery artifacts help companies keep their user data top of mind. They are ‘living documents’ and should be habitually updated, improved, and referred to throughout the product discovery process. Three examples of product discovery artifacts:

1. Journey map: This is a literal map of the user’s journey, with “actions” as the locations along the road to their goal. It is absolutely critical that these be based on empirical user research. More often than not, journey maps are created by one or two people within the company based on little more than experience and intuition. Inaccurate journey maps can lead companies astray.  
2. Empathy map: An empathy map is a four-quadrant diagram of the customer in which  companies record what their customers think, hear, see, and say that ties back to the customer’s challenges and opportunities. This will help develop a firm sense of how your products and services make users feel.
3. Consumer persona: A consumer persona is an approximation of a segment of your users, such as ‘power users’ or ‘aspiring amateurs.’ Most personas take the form of a catchy name followed by a list of demographics, psychographics, and behaviors that help companies visualize the customer as they design.

With better data discovery skills and a handful of customer artifacts, your company will execute on superior product discovery. Products based on actual customer data, research, and insights will yield far greater results and can tip your product from the category of ‘nice to have’ to absolute necessity. Not sure how to conduct data-driven product discovery? Learn more about product analytics.