Optimize For Learning
Why Insight Is Critical For Building Successful Products
A great product needs to solve meaningful problems for people, which requires product teams to deeply understand what users care about and why. However, companies often fail to accurately grasp what motivates users to choose a product. They fall into the trap of maximizing metrics that may produce immediate results but restrict sustained long-term growth. While metrics are important, an over-reliance on numbers can limit true customer understanding and growth.
Commercial success is a strong indicator of user value. Industry leaders established their position because they have leveraged critical insights to create something that resonates with people. These companies have achieved sustainable growth by continuously learning about their users and adapting their products accordingly. Optimizing for learning can lead to more impactful product development that ultimately translates to more resilient, valuable products that stand out in a crowded marketplace.
Why Learning Matters
It gives teams the full context behind business metrics.
For many product teams, metrics, such as revenue, active users, engagement, etc., have traditionally been the primary indicators of success. However, metrics can lead teams to prioritize short-term wins over long-term value, when they are not aligned with what users care truly about. While metrics are great for setting targets and evaluating progress, they don’t always tell the story of the people we are building products for.
Human behavior is motivated by both rational and irrational factors. Teams have to be genuinely curious about their users’ problems to develop solutions that address their real needs. They have to understand what drives their users and why. This helps them ask better questions and listen for the right information. It drives them to dig deeper and uncover valuable insights hiding behind the numbers, giving them the perspective needed to create impactful solutions.
It helps teams create better outcomes for customers and the business.
Optimizing for learning is about maximizing the long-term gains from our efforts. Insights allow us to identify our strategic advantage and align it with user’s goals and current opportunities. Teams that incorporate these insights into their planning process are better equipped to respond to market shifts, strengthen their value proposition., and create a sustained competitive advantage.
Learning should be integrated into the roadmap; teams should have actionable learning objectives that define what they hope to learn and how they will act on new information. It should inform how they design and develop features to address user needs more precisely. They also need to pinpoint areas where learning could reduce risks or open new opportunities. This helps them gather information that directly supports their goals.
Example: Spotify
Spotify invests in understanding users’ music listening and discovery habits instead of just focusing on subscriber growth. This focus allows them to develop personalized music recommendation features, creating a loyal user base that values the platform’s ability to understand and cater to individual tastes. By putting learning at the center of their strategy, they were able to achieve both growth and retention in the competitive streaming industry.
How To Learn Effectively
Experiment Early And Often
Experimentation allows us to test assumptions and discover what works.
Experimentation is a practical way for teams to test ideas in a controlled environment, learn from the results, and make informed decisions. There are many ways to experiment: A/B Testing allows teams to compare two versions of a feature to understand which one resonates more with users. Prototyping allows them to evaluate ideas by giving people something real to interact with and provide feedback on. Regardless of the format, experiments help teams validate their hypotheses and test the feasibility of solutions before committing serious resources. Each experiment refines their understanding of what works and what doesn’t, keeping the product aligned with user needs.
Example: Amazon
Amazon’s Prime service started as an experimental concept focused on improving the shipping experience. They hypothesized that fast, free shipping could attract loyal customers, but needed to understand how their users would respond to it. They tested the concept by gradually rolling out faster shipping options and adjusted the offering based on insights. This allowed Amazon to learn what worked, refining Prime’s benefits over time to create a service that redefined user expectations for online shopping by tapping into user’s desire for convenience.
Engage In Research And Discovery
User research and discovery are essential for uncovering what really matters.
User research provides teams with valuable insights into user needs, motivations, and pain points. Discovery is the process where teams sift through research findings to identify actionable insights. Together, these processes provide context on people’s actions, thoughts, and feelings through various methods, such as interviews, observations, and surveys. Teams analyze patterns in user behavior, prioritize feedback, and align product ideas with actual user needs. This keeps the product development user-centered, reducing the risk of costly missteps by prioritizing meaningful problems that are valuable to the customer and the business.
Example: Airbnb
In 2013, Airbnb embarked on a journey to redefine its mission. They conducted interviews with 480 employees, guests, and hosts worldwide to explore what made Airbnb unique. They discovered that guests didn’t want to be passive tourists; they craved authentic local experiences and a sense of connection. They embraced this concept, rebranding around the mission of helping people "belong anywhere," which led Airbnb to reposition itself from a simple accommodation provider to a company with a higher purpose—fostering community and belonging in a disconnected world.
Implement Continuous Learning Loops
A system of ongoing feedback keeps teams aligned with user needs and market shifts.
Continuous learning loops are a structured approach to creating a cycle of informed decision-making. Teams review the outcomes they produced by gathering feedback and extracting insights, to make informed adjustments. They build upon previous insights, creating a self-sustaining loop where improvements are consistently informed by real user data. This cyclical process enables teams to continuously improve the product, rather than waiting for large updates or overhauls. It ensures that products stay relevant and valuable over time.
Example: Slack
Slack spends considerable time understanding the challenges faced by remote and distributed teams. They involve customers directly in the design and iteration of new features. They collect user insights, through interviews, feedback sessions, surveys, support calls, etc., to analyze how users interact with the platform. This helps them identify and prioritize the features that provide the most value, and highlight areas for improvement. By embedding user input into their design process, they create a product experience that is closely aligned with their user’s actual workflows.
Conclusion
Our world is constantly changing and the list of the things we need to know keeps expanding. Optimizing for learning helps us gather the building blocks we need to create what we want. It’s not just about blindly acquiring knowledge. It’s about utilizing that knowledge effectively to create the outcomes we want. When product teams focus on learning, they can develop solutions that offer lasting value to users, create meaningful experiences, and unlock new avenues for growth. It ensures that products evolve alongside what users truly want and need.
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References
Spotify | How Spotify Uses ML to Create the Future of Personalization
Slack | Designing teamwork: How our customers helped shape the future of Slack
Vox | The making of Amazon Prime, the internet’s most successful and devastating membership program