If algorithmic playlists aren’t triggered by scale or engagement thresholds, how should artists and labels evaluate performance? In Part II of our algorithmic optimization series, we introduce Music Tomorrow’s audit framework for measuring algorithmic positioning and growth potential. By analyzing audience clusters, addressable opportunity, exposure-to-feedback conversion, and scene-level momentum, we show how algorithmic visibility can be transformed from opaque stream counts into a structured investment decision system.

Why do some tracks trigger Discover Weekly or Release Radar while others stall — even after strong playlisting or early traction? In this article, we unpack the most common misconceptions about Spotify’s algorithmic playlists and explain why popularity thresholds, engagement benchmarks, and broad exposure strategies often fail to produce sustained recommendation support. Drawing on large-scale audit data, we show how modern recommender systems evaluate context, audience alignment, and signal clarity — and why algorithmic visibility is ultimately a question of positioning, not scale.

A behind-the-scenes look at how algorithmic insights reshaped a music rollout. From audio profiling to feedback loops, this case study shows what happens when creative direction meets streaming data — and the results speak for themselves.

Explore the evolution of music marketing in 2025. From Swifties to vaporwave fans and biophilic beat lovers, learn how diverse superfans are transforming audience engagement and artist marketing campaigns.

Back in 2023, the French indie label Tôt ou Tard brought Music Tomorrow on board to work on the optimization of the upcoming Vianney release — a collaborative album featuring dozens of artists. Here's how we leveraged our data tools to help the team deliver an optimization strategy that led to a near 3x uplift in algorithmic streams.

Two years ago, we started developing our own platform, based on our knowledge about music recommender systems, to help labels make their artists more visible. Since then, we made significant advancements: you can now use our analytics tools to gain insights into how Spotify's algorithms perceive your music, and use this information to amplify your online presence. We collaborated with both major and independent industry players, including Universal Music Group, Warner Music, Handwritten Records, Tôt ou Tard, Because Music, among others, to fine-tune our tools and data, ensuring they meet the evolving needs of artists and labels alike.

We bring you our first case study, showcasing how Music Tomorrow leveraged its proprietary data tools to gain clear insight into the algorithmic positioning of an artist on Spotify and develop an optimization strategy that had a clear and strong positive effect on the algorithmic performance of the newly released music

Fake streams are still a widespread practice in the music business — both among the DIY artists and established music professionals. Streaming fraud has its obvious risks, but even if you manage to get away with it, this shortcut will come with a hefty price to pay. Here's why engaging with these fake streaming bot farms is a sure way to ruin your algorithmic potential.

In the second part in our series on RSO we present a framework for algorithmic optimization on DSPs (and, specifically, Spotify). Learn how you can manage the inputs processed by the recommender to amplify algorithmic traffic and discoverability of your catalog.

This is the first part in our series on algorithmic optimization on streaming platforms or simply RSO (for Recommender System Optimization). In this introductory piece, we will cover Music Tomorrow’s approach to algorithmic optimization and present the data tool we’ve developed that allows us to extract and analyze artists’ algorithmic profiles on Spotify.

Today, editorial playlists across major streaming services like Spotify are some of the most valuable real estate in the music business. But how fair is the editorial landscape when it comes to showcasing independent, emerging artists? We run the analysis of the biggest playlists on Spotify to try and answer that question

When it comes to music SEO, the property you’re optimizing is a song (and sometimes a video or a playlist), and in the best-case scenario, you have just a few lines of text to work with.But can these few lines still make a difference? Let’s try and figure it out. 

Check out this piece based on the panel about Streaming & Algorithms I organized with shesaid.so France during the JIRAFE event put together by the Réseau MAP in Paris, where I interviewed Elisa Gilles, Data Scientist Manager at Deezer, and Milena Taieb, Global Head of Trade Marketing and Partnerships at Believe, about music discoverability on digital streaming platforms.