If you work on a product team, likely you have sat in a meeting where your team disagrees on the right feature to build. The conversations might sound like this:
“According to the user research, the user wants our product to do this…”
“Really? It really depends on how you interpret what the user is saying…”
“I think building it this way will lift our north star metric significantly…”
“I disagree, if I were the user, I would never need this feature…”.
Understanding user needs is one of the toughest and most important jobs of a product manager. It is the foundation upon which your product strategy, goals, hypotheses, and features reside. However, asking users what they want is unreliable. If you are working on an existing product, analyzing data is often one of the most reliable ways to understand user needs. Instead of relying solely on user research, data can tell a story about a user and his behavior (often better than the user himself). It is, in essence, a truer way to observe your users, what they do, and how they use your product.
Data helped Tik Tok to find the JTBD (jobs to be done).
Harvard Business School professor Clayton Christensen articulated the JTBD as: “Most companies segment their markets by customer demographics or product characteristics and differentiate their offerings by adding features and functions. But the consumer has a different view of the marketplace. He simply has a job to be done and is seeking to ‘hire’ the best product or service to do it.”
Data can help uncover the one job that your users want from your product. Alex Zhu, the founder of the short video app Musical.ly (now Tik Tok), discusses in an interview how data helped Musical.ly shift its value proposition and find its product-market fit. Back in 2015, Musical.ly was still an app that focused on adding background music in short videos. The product struggled to create a daily use case for users and growth was slow. One day, while looking at Musical.ly’s app download data, Alex noticed that on Thursday nights, there is always a surge in app downloads. This trend continued every week. Incidentally, it was also in 2015 when the show Lip Sync Battle debuted on Thursday night. Alex eventually pieced together the puzzle. After every show, fans would go to the App Store and search for “Lip Sync app” and Musical.ly always showed up in the top search result. Alex decided to double down and create features that would highlight Musical.ly’s lip-sync value proposition. A few months later in July 2015, Musical.ly hit the №1 app in the Apple App Store. The download data told Alex what is happening, and researching uncovered the why — users wanted an app that let them lip-sync, and Musical.ly/Tik Tok found its JTBD.
Data led Calm to its own mindful pivot
According to a study by the CDC from 2018, meditation’s popularity increased “more than threefold” in the US over the past five years, while the demand for meditation increased, search for a meditation app on the App Store and you’ll be overwhelmed by the choices. Calm is one of these meditation apps, how can it continue to grow in a fiercely competitive market? At an event I attended recently, the VP of product of calm, Dun Wang stated that when she was looking at the app’s data, they noticed usage surged around night time from 8pm-12am. Quite surprising from their hypothesis that people the app during the day to meditate, many used the app to fall asleep. Instead of a weekly resolution, this presented an opportunity for the app to become a daily habit. Calm then launched “Sleep Stories” to help users sleep- a pivot from just a meditation app to also a sleep app. Engagement and retention surged. Everybody on the planet could use a better night’s sleep, by pivoting to fix the user's need of the “sleep problem,” Calm increased their TAM to 7.5 billion people.
Data inform solutions for user needs at Spotify
As we have seen previously, data tells you what the users are doing. It’s often up to the product owner to find the why. When a solution doesn’t yet exist for a user need, the user often creates their own MVP by finding workarounds.
When I led app integrations at Spotify, our data tracking showed that users took millions of screenshots daily. At first, it wasn’t clear why so many screenshots were taken in a music app. After some research, I found many screenshots from Spotify on social media platforms like Instagram, Twitter, and Snapchat. There is a clear user need to share music visually, but what else is there? I also noticed in many of these screenshots, users would note “swipe up to listen” or write “link in bio”. Users also wanted their audience to be able to listen to the songs they shared. From these insights, we then developed and launched the sharing to Instagram Stories feature where users can share visually what they are listening to and have a link to direct their audience back to Spotify. This was an especially powerful feature when influencers who have thousands and millions of followings, which scaled social listening to the level that was not possible on Spotify’s platform alone. Data inform us what users are already doing on our platform, and based on this MVP, we were able to iterate and launch Spotify and Instagram Stories, and also eventually adding music clips also to Facebook Stories.
“If I had asked people what they wanted, they would have said faster horses.”
A user-centric PM is also a data-driven PM. Being user-centric does not mean asking users what they want, and being data-driven does not mean you ignore the human behind the data. Data allows you to get to know your users and empathize with them in their truest behavior, which provides you with insights that help drive product decisions.
This story is a repost from my Medium article.