Use Case Intelligence RSS Search use cases Search
Accenture Media and Entertainment Global

Spotify

Marketing Personalization

Spotify wanted to scale its advertising business across far more markets and make digital-audio advertising easier for brands to buy, create, measure, and optimize, but campaign operations and creative workflows needed to move faster to support that growth.

Read the original story Explore all use cases
24 hours to traffic ad campaigns
27% Increase in customer purchase intent
34% higher conversion rate than audio industry benchmarks
40% faster time to market

Reported impact

Business outcome

Spotify scaled its ad operation into many more markets, shortened campaign-launch times, reduced trafficking effort, enabled advertisers to produce audio creative more easily, and improved internal visibility into ad performance and growth opportunities.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

Spotify and Accenture combined workflow automation, ad-tech operations, and generative AI tools to expand the company's advertising engine. Accenture has supported Spotify advertising operations since 2018, helping scale the business from 20 to more than 150 ad markets while automating campaign-launch workflows so advertisers can get to market faster. Spotify's ad stack includes channels such as Spotify Ads Exchange and Ads Manager, and the newer layer of gen-AI-powered audio production tools makes it easier for advertisers to create scalable, lower-cost ad assets. On the analytics side, Accenture is also helping build Insights Studio, a dashboarding and reporting service that gives sales teams better market and campaign intelligence. The overall flow now links campaign creation, trafficking, measurement, and optimization more tightly so Spotify can move ads into market faster, support more advertisers, and improve ROI visibility for brands using the platform.

Data flow

Advertiser campaigns enter Spotify through buying channels such as Ads Exchange and Ads Manager, where automated workflows speed launch and trafficking. Gen-AI-powered audio production tools help create scalable ad assets, while campaign and market data flows into Insights Studio so sales teams can report on performance and optimize how advertisers use Spotify's inventory.

Solution architecture

4 components · 3 layers
  1. Compute
    • gen-AI-powered audio production tools Help advertisers create high-quality audio ads quickly and at lower cost.
  2. Serving
    • Spotify Ads Exchange Provides one of the buying channels through which advertisers place digital-audio campaigns on Spotify.
    • Ads Manager Supports advertiser self-service campaign creation and management across Spotify's ad inventory.
  3. Orchestration
    • Insights Studio Delivers dashboarding, reporting, and market analysis to Spotify's sales teams.

Architecture clues

  • Campaign-launch workflows are automated across advertising operations.
  • Generative AI supports ad-asset creation, not just reporting.
  • Insights Studio provides a separate analytics service for sales teams.
  • The same operating model supports both ad buying and ad measurement improvements.

Evidence from the source

"24 hours to traffic ad campaigns"
"7.5X ad market expansion"
"Accenture is also partnering with Spotify to create Insights Studio"
"gen-AI-powered audio production tools to create and launch ads rapidly and cost effectively"
"one that is getting advertiser campaigns to market 40% faster"
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Accenture

    Spotify customer story

    First-party
    https://www.accenture.com/en/case-studies/communications-media/spotify-advertising-revolution
    Source type
    Vendor-published customer story
    Published
    Feb 5, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 5, 2026 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
Google Cloud Modified · Oct 9, 2026 HOLYWATER TECH Media and Entertainment Ukraine HOLYWATER TECH wanted to produce long-form fantasy video content faster and more consistently, but traditional filming and fragmented AI tools made it hard to keep characters, settings, and sound synchronized across… 10x ARR growth for My Muse since implementing Marketing Personalization BigQuery · Gemini · Gemini platform Google Cloud Modified · Oct 8, 2026 Meitu Media and Entertainment China Meitu needed one cloud platform that could supply advanced image-generation models, developer productivity tooling, performance telemetry, and large-scale analytics so it could improve AI imaging quality for a global… As of December 2025, Meitu had 276 million monthly active users around the world. Marketing Personalization BigQuery · Firebase · Gemini Code Assist Databricks Sep 30, 2026 Showtime Media and Entertainment United States Showtime wanted to leverage data to drive content strategy, but they struggled with scaling limitations of legacy systems and inefficient data pipelines. Unfortunately, legacy technology architectures were pulling teams… 6x Faster data pipelines enables faster decision making Data Lake / Data Warehouse ModernizationMarketing Personalization Databricks Data + AI Platform