The data made the call.
The streams followed.

The challenge

Tasked with the global rollout of a high profile collaboration, the team was operating on intuition about which track deserved the most marketing investment. With multiple strong contenders and limited budget, a wrong call could cost millions of potential streams.

The approach

We aggregated real time social signal across TikTok, Spotify and Instagram. While 'One Night/All Night' was the designated lead single, 'Neverender' was quietly accumulating superior momentum: more saves, higher completion rates and accelerating fan made content. The shift surfaced in the data before anyone had noticed it in the room.

The result

The team made the call to redirect the primary campaign focus mid rollout. Budget was reallocated, pitching was reprioritised and paid social shifted to 'Neverender'. The outcome was a global viral success that beat initial projections, driven entirely by analytics rather than gut feeling.

The challenge

With 'Sweet But Psycho' showing early promise, the team needed to know exactly where to invest: which territories, which platforms and which audiences carried the highest potential for viral lift. Spreading budget evenly across every market would have diluted the impact.

The approach

We analysed streaming behaviour, social engagement and playlist traction across dozens of territories, then ranked markets by organic momentum and cost per engaged fan. That produced a specific rollout order: which cities to target, which creative angles resonated locally and how to sequence spend.

The result

The campaign was built entirely on that territory ranking. The song spread market by market in the exact sequence the data predicted and reached global viral status.

The challenge

A loyal global fanbase existed, but marketing focus stayed anchored to the US and Western Europe. Emerging traction elsewhere was going unnoticed, leaving real growth on the table.

The approach

Our territory layer picked up an early stage viral signal building in Latin America, in Tier 2 and Tier 3 markets that were not part of the active campaign. Stream velocity, playlist adds and social engagement were accelerating in the pattern that usually precedes a breakout. We moved a modest budget into those markets before the wave peaked.

The result

Entering LATAM early and cheap built a global streaming profile that caught the attention of algorithmic playlists and label partners worldwide. That international volume then triggered accelerated growth back in the US, the exact market originally prioritised.

The challenge

Atlantic Records needed to build a US presence ahead of an American tour. The hard part was converting streaming listeners into real, geo located fans who would actually show up to venues, across Spotify and Instagram at the same time.

The approach

We tracked listener growth and geographic concentration across Spotify for tracks including 'Dumb' and 'Vandalist', identified the cities with the strongest fan density, and ran targeted Reels campaigns to amplify visibility in exactly those markets. Touring readiness was assessed city by city before anything was booked.

The result

The coordinated Spotify and Instagram strategy built real audience density in key US cities before the tour was announced. Listener heatmaps informed venue selection directly, so the rooms matched the demand.

The challenge

Management needed to grow streaming presence for 'One Bad Habit' and mobilise the fanbase for a People's Choice Awards voting campaign at the same time. Two objectives, different tactics, one audience.

The approach

We unified Spotify listener data, audience demographics and engagement patterns into a single intelligence layer, isolated the highest value fan segments by age, geography and listening frequency, then built both a Spotify ad strategy and a fan activation playbook off the same data set.

The result

The paid campaign drove meaningful listener growth for the single while the activation mobilised core supporters to vote. One data set powering two coordinated campaigns cut cost and time on both.

The challenge

Aviram came to us in March 2023 as a rock artist with no streaming history and no paid media in place. Rock is a hard genre to buy attention in, and the budget was modest: twenty to fifty dollars a day, roughly one thousand dollars a month.

The approach

We ran our hyper A/B testing method from day one. Creatives, hooks and audiences were rebuilt weekly, spend followed the variants that produced the cheapest qualified click, and every result was read inside Spotify for Artists rather than in ad manager alone.

The result

Week one produced 345 streams at eleven cents each. By month five the same method had compounded into 122,939 streams at a cost per stream of $0.0056, and the twelve month view shows over 2.5 million streams with 192,770 saves and 104,665 playlist adds.

The challenge

An upbeat nightlife dance track needed to behave like a playlistable anthem fast. Without early volume and engagement, dance releases stall before the algorithm ever notices them.

The approach

We built a Spotify conversion funnel: Meta campaigns for mass awareness, a smart link for click to listen intent, and engagement quality as the optimisation target. Twenty eight individual ads ran across four ad sets targeting Alesso fans, dance music fans, David Guetta fans and house music lovers.

The result

7,722 clicks from 192,424 reach at $0.14 per result, with the best ad delivering 4,581 results at $0.13. Spotify returned 242,725 streams, 103,672 listeners, 8,393 playlist adds and 9,545 saves, with 31 percent of streams coming from algorithmic playlists.

The challenge

A house producer starting from nothing, with daily budgets of twenty to fifty dollars. The goal was not a spike on release week but a growth curve the algorithm would keep feeding long after the ads paused.

The approach

Hyper A/B testing on paid social to bring qualified off platform traffic to Spotify first, then carefully timed playlist placements around it. Overusing placements too early is the most common mistake we see: it suppresses the algorithm instead of triggering it.

The result

150 million streams in twelve months from a standing start, 607,420 saves, 694,208 playlist adds and 27,811 followers, with 77 percent of streams arriving through Spotify algorithmic sources.