Every independent artist I know has the same question about streaming platforms: how do I get the algorithm to notice my music? The answers floating around online range from genuinely useful to completely wrong. Let me try to separate the two.

I’ve combined information from published research, conversations with people who’ve worked at streaming platforms, and the experiences of Australian artists who’ve seen both algorithmic success and failure.

How Spotify’s Algorithm Actually Works

Spotify uses several recommendation systems that feed into different parts of the platform. The three most relevant for independent artists are:

Discover Weekly and Release Radar use collaborative filtering — essentially, “people who listen to X also listen to Y.” If your listeners also listen to a particular set of other artists, Spotify will recommend your music to fans of those artists. This is why having a genuine, engaged listener base matters more than raw stream counts.

Autoplay and Radio use audio analysis models that examine the sonic characteristics of your tracks — tempo, key, energy level, danceability, and more abstract features — to find similar-sounding music. This is entirely automated and based on the audio itself, not metadata or marketing.

Algorithmic playlists (like Fresh Finds, and various genre-specific playlists) use a combination of both approaches plus editorial input. Some are fully automated, some have human curators who use algorithmic data to inform their choices.

What Actually Moves the Needle

Based on conversations with artists and the available research, here’s what seems to have genuine impact:

Consistent release cadence. Spotify’s algorithm favours artists who release music regularly. A single every 4-6 weeks tends to maintain algorithmic visibility better than dropping an album once every two years. This doesn’t mean you should rush releases — quality still matters — but the platform rewards consistency.

First 24-48 hours matter disproportionately. When you release a new track, how your existing listeners engage with it in the first 24-48 hours significantly influences how widely Spotify distributes it. This is why pre-save campaigns, email list promotion, and release-day social media pushes make a practical difference.

Save rates are more important than stream counts. A listener saving your song to their library signals stronger engagement than a single stream. Spotify weights this behaviour heavily. If a thousand people stream your song once and move on, that’s less algorithmically valuable than three hundred people who save it and listen repeatedly.

Playlist adds by listeners (not just curators). When listeners add your song to their personal playlists, it signals organic demand. This feeds back into collaborative filtering and increases the likelihood of your music appearing in algorithmic playlists.

What Doesn’t Work

Buying streams or followers. Beyond the ethical problems, Spotify’s fraud detection is sophisticated. Artificial streams are increasingly detected and can result in tracks being removed and artists being flagged.

Uploading the same track multiple times. Some artists try to gain algorithmic advantage by uploading slight variations of the same song. This doesn’t work and can hurt your profile.

Metadata gaming. Adding misleading genre tags or featuring popular artist names in your track titles will get your music flagged and potentially removed.

Cold playlisting services. Many services promise to get your music onto Spotify playlists for a fee. Most of these playlists are either bot-driven or have no genuine audience. Even if they temporarily inflate your numbers, they can damage your algorithmic profile because the engagement signals (saves, repeat listens, adds to personal playlists) won’t follow.

Apple Music Differences

Apple Music’s recommendation system works differently from Spotify’s in some important ways.

Apple Music relies more heavily on editorial curation. Human editors have more influence over what gets featured, which means building relationships with the editorial team (through proper distributor channels) can be more effective than on Spotify.

Apple Music also pays more per stream on average, so building an audience there has direct financial benefits even if the platform is smaller than Spotify in Australia.

Practical Steps for Australian Artists

Here’s the playbook I’d recommend based on everything I’ve learned:

Build your email list. It’s the one audience you own that no algorithm can take away. When you release music, email your list first. Their engagement in the critical first 24 hours drives algorithmic distribution.

Release consistently. Aim for a new track every 4-8 weeks if possible. Singles, collaborations, remixes, and acoustic versions all count.

Encourage saves, not just streams. In your social media and email promotion, explicitly ask people to save the song to their library. It sounds small, but it matters algorithmically.

Pitch to Spotify editorial playlists through your distributor. Most distributors offer playlist pitching tools. Use them for every release, at least two weeks before the release date.

Focus on your niche. The algorithm works best when it can clearly identify your audience. An artist with a devoted following in a specific genre will get better algorithmic treatment than an artist with scattered listeners across many genres.

The streaming algorithms aren’t enemies. They’re systems that respond to genuine engagement. Make music people want to hear again, build an audience that cares, and the algorithms will work in your favour more often than not.

The analytics side of streaming is getting more sophisticated each year. Companies like AI consultants in Brisbane are building tools that help artists and labels interpret their streaming data more effectively, which is something worth watching.