Case study: Gabriel Ruiz — Ribeiro Data Studio

The challenge

Gabriel Ruiz is an independent artist from Rio de Janeiro who writes and performs his own MPB, samba and funk. He has released one album, plays a show every three months and has 9 songs on Spotify. He works solo, hiring production and content professionals as needed. Despite an established online presence, he had never taken a deep look at his own data.

Zero music data analysis — no Spotify data was being collected. No tracking of streams, saves or trends.
Playlists picked blindly — money spent on several paid playlists without knowing which ones actually paid off. Of the 10 tested, only 2 were worth it, but without data there was no way to tell.
Social media impact was invisible — there was no analysis of how TikTok or Instagram posts affected streams. The effect of a viral post was pure guesswork.
Management on loose sheets — everything lived in disorganized Google Sheets, with no defined process. No planning for releases, shows or content.
Fan communication was manual and sporadic — there was no system to notify fans about shows or releases. Every message was sent by hand.

What we did

Full music metrics dashboard

A Looker Studio dashboard with streams, saves and trends for all 9 songs, broken down by song, period and growth over time.

Machine Learning forecasts

A predictive model in Python that projects how each song will perform and helps decide where to invest based on data.

Playlist spillover analysis

A statistical study (Mann-Whitney, permutation test, Cohen's d) proving that playlists lift the entire catalog: +79% in average streams for songs that weren't on any playlist.

2026 stream projection

A machine learning–based projection report showing the expected scenario with and without playlists, including confidence intervals.

TikTok vs. Spotify report

A 14-week cross-analysis correlating 63 TikTok posts with streams. It mapped the viral post (53K likes) and its +891% effect.

Automated fan communication

An n8n workflow that sends messages via WhatsApp (586 fans) and email (85 contacts) about shows and releases. No manual work.

Automatic weekly summary

Every Tuesday, the artist gets a WhatsApp analysis of his songs using Spotify data.

Show and grant planning

Posting plans, public arts grant applications (6 submitted, 2 approved) and strategic planning.

Results

Streaming and social media metrics: real data from Spotify for Artists and TikTok Data Export.

+891%Stream growth after going viral on TikTok

16.1KStreams over 14 tracked weeks

+79%Spillover: growth in songs that weren't on playlists

+678New TikTok followers in December

671Automated contacts (WhatsApp + email)

2 of 6Public arts grants approved

How it worked

Data-driven decisions

Playlist spending is now based on the real return of each one. Of the 10 tested, the data showed only 2 were worth it. No more wasted money.

Full visibility into every song

A dashboard available anytime with the performance of each song. Streams, saves and trends in a single view.

Effortless weekly summary

Every Tuesday, Gabriel gets an automatic analysis on WhatsApp: what grew, what dropped, what needs attention. Spotify data, without opening a single dashboard.

Automated fan communication

Shows and releases reach 671 contacts (586 WhatsApp + 85 email) in one click. Before, every message was sent by hand.

A planned career, not an improvised one

Releases, shows, grant applications and social media content now follow a structured plan, discussed in regular data-based meetings.

Gabriel Ruiz

The challenge

What we did

Results

Tools used

How it worked

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