Spotify’s approach to building their product organization represents one of the most influential case studies in modern product management. Their willingness to experiment, learn, and adapt created not just a successful product but a distinctive organizational philosophy that continues to inspire product teams worldwide. The first is how the company decides the most important problems to solve โ the product strategy.
Discover Weekly focuses more on newly released songs that fit a userโs taste vector. The AI DJ weaves user preferences into a steady stream of contextual listening, which adapts as preferences update. This real-time foundation fuels what Spotify internally refers to as the โTaste Profileโ, a dynamic, user-specific dataset built from both behavioral signals and metadata from the content itself. When a user listens to specific genres, skips certain tracks, or searches for new music by a lesser-known artist, those signals are logged, weighted, and translated into insights. Youโll see in the above diagram that the key question Spotifyโs product and management team asks is โIs the MVP good enough for real users? โ By making its MVP narrative-complete and not feature-complete, Spotify is able to inherently satisfy all five qualities for a desirable and usable MVP.
They provided coordination when needed but maintained the autonomy of individual squads. Matchist.com cofounder, Stella Fayman, aptly states the goal of an MVP is to prove that people will use your product. Landing pages and paid ads are a great low-cost way to gauge interest because you test the basic value proposition first (or narrative, in the case of Spotify) before sinking money into anything. When developing its Mobile Free Radio (one version being โRadio you can saveโ), Spotify ran a Google Adwords campaign to test narratives. While Lean is used to efficiently define and build a marketable product, Agile is the means to accomplish this in software development. So how did they develop a product that fulfilled their vision without driving them into bankruptcy?
She asked the gathered crowd of machine learning experts and UX design leaders to consider tomorrowโs users as well as todayโs when crafting ML products. โItโs critical for designers today to design for the future,โ Sohail said in closing, reiterating the need for those working on machine learning products to keep people at the center of their work. Realizing that recommendations needed to become a core part of the product strategy, Spotify had recently acquired Massachusetts-based start-up The Echo Nest.
But now Spotify had the evidence they needed to show that reconstructing the playlist system to accommodate the requisite scale would be worth the investment (this is what we refer to as a high-integrity business case). On top of that, certain industry pundits argued that lean-back users simply werenโt interested in exploring new music. This balance is exactly what companies look for when interviewing product managers.
How the Amazon Planning Process Drives Success
Beyond organizational structure, Spotify developed distinctive approaches to product development that contributed to their success. Squads functioned as mini-startups, with autonomy to decide what to build, how to build it, and how to work together. Each squad had a dedicated product owner who was responsible for prioritizing work and ensuring the squad was building the right things for users and the business. Spotify’s initial success came not from organizational innovation but from relentless focus on solving a real user problem with superior technology and experience. Spotifyโs approach is undeniably effective, but the real lesson isnโt to copy their model outright. Itโs to adapt their principlesโautonomy, risk reduction, and iterationโto fit your teamโs size, culture, and goals.
Building Your Own Product Organization
Machine learning models update them continuously based on listening habits, frequency, engagement depth, and individual preference shifts over time. This profile exists for every user, and underpins all personalized recommendations across the platform. Spotify delivers one of the most personalized content experiences on the planet. They use event-driven data pipelines to track every user interaction in real time. Each play, skip, or playlist creation is captured as an event and processed through Apache Kafka. That means Spotify isnโt waiting to analyze data afterward, itโs reacting to user behavior the moment it happens.
Scorecards: Creating Alignment Throughout the Process
We take a similar iterative approach to Spotify by starting out with several lightweight, even low-fidelity, prototypes and narrowing down the options from there. To that degree, each one of Spotifyโs four stages are Lean since small teams are always working smartly to test assumptions. The โThink Itโ stage tests the merit of conceptual MVPs while the โBuild Itโ stage releases a physical MVP only after itโs been tested for quality. The โShip Itโ and โTweak Itโ phases ensure long-term quality and customer alignment by releasing the MVP gradually, learning from feedback, and iterating tirelessly.
Building for everyone
All the testing and validating in each phase also keeps Spotify on the Lean path even if product requirements must be changed to reflect customer and market needs. Listen to machine learning (ML) legend Andrew Ng and Spotify insiders on what it really means to develop products in an AI/ML first world, how we invented the term โAlgotorialโ and how reinforcement learning (RL) applies to music. The engineering teams were also directly involved in the delivery of Spotify Wrapped visuals in 2023. They worked alongside animators to implement Lottie animations, an efficient file format that supports scalable, high-performance playback across devices.
As with other product model companies, at Spotify, any empowered product team can roll out experiments to up to 5% of users without needing permission. The team decided to roll it out to 1.5% (1,000,000 users), watching closely as data began to trickle in. This holistic view of the business and the resulting focus allows the product teams to dedicate their energy to the most critical problems to solve, giving it a higher likelihood of success. Spotifyโs product strategy was shaped by insights on how their audience segmented.
- This learning mindset is particularly important in product management interviews, where demonstrating adaptability and growth potential often matters more than having all the answers.
- Hear the story from the source, featuring interviews with Spotify founder and CEO Daniel Ek, and former Spotify teammates Ludde Strigeus, Sophia Bendz, and Michelle Kadir.
- Every feature they build passes through a structured framework to reduce risk – Think it, Build it, Ship it, Tweak it – and it is central to how they deliver impactful features consistently.
- The playlist was presented to users at the conclusion of theirย Year In Musicย review.
- When I left the company after more than six years, I wanted to help other companies become more like Spotify.
Removing the friction of waiting every time you wanted to play music helped Spotify win over piracy and enabled the streaming revolution to take off. In a world where the product uniquely adapts to each user, weโve found that creating deeply personalized products requires a new type of mindset and approach to design. But when working with Machine Learning at Spotify, weโre now tackling entirely new types of challenges. Machine Learning (ML) has become an indispensable tool at Spotify for delivering personal music and podcast recommendations to over 248 million listeners across 79 markets and in 24 languages.
Their optimism was bolstered by a recent hack week project, called Play It Forward, that was an add on to Spotifyโs popular Year In Music (now known as Wrapped), a feature that provided a summary of the userโs year on Spotify. The launch was a resounding success, with 1 billion tracks streamed within the initial 10 weeks. Remarkably, 71% of listeners added at least one song to their personal playlists, and 60% of those who tried Discover Weekly proceeded to stream five or more tracks.
Navigating Organizational Challenges
- When you consider that Spotify is slowly inching towards iTuneโs market share as of 2014, Spotifyโs evolutionary product strategy is definitely working.
- Spotify understands that first impressions matter a lot, so it takes a cautious approach of having a limited release of something good before making it great and unleashing their brilliance.
- If you havenโt yet read the series of four articles, you should probably start here.
Easy access to music through streaming โ which Spotify had fought so hard to achieve โ was now table-stakes, rather than a differentiator. If you’re preparing for product management interviews, our Product Manager Interview Questions resource can help you practice articulating this balance between strategy and execution. This shift helped Spotify build more cohesive products while reducing the context-switching costs of frequently changing team compositions. Spotify institutionalized innovation through regular ยซHack Weeksยป where normal work stopped and employees could explore new ideas. Many significant features, including Discover Weekly, originated during these periods of focused creativity.
We hope our experiences at Spotify can help you and your team evaluate and consider the implications of applying Machine Learning to your products and experiences. If this article is helpful or if you have stories of your own, weโd love to hear from you. A focus on removing friction should feel familiar to every designer because we do that work every day. We define friction as anywhere in the user experience where a human struggles in pursuit of their goals. Machine Learningโand more broadly Artificial Intelligenceโis a new tool to help us in our mission to make experiences frictionless.
In a fast-moving environment, learning about how spotify builds products thatโs how you stay operational and innovative without dragging dead weight. To answer our questions, we started by evaluating the Home screen experience through a manual process โ by both assessing user feedback and identifying behavioral patterns in the data. It was only after we proved those hypotheses that we started to apply Machine Learning. To view friction another way, letโs break down the success of one of Spotifyโs most popular playlists, Discover Weekly.