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Google Cloud Retail United States

Roundel™ Media designed by Target

Marketing Personalization · Real-time Analytics

As one of the world's most beloved brands, Target's purpose is to help people discover the joy of everyday life, providing affordable essentials, fun, ease and inspiration at every turn - no matter when, where or how they shop.

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"For Target, because advertisers are seeing more results, we're seeing double digit growth in the actual revenue we're collecting from the new data capabilities," Collin says.
Double-digit growth in Target's ad revenue
Historically, ad performance data could take over 30 hours to arrive, resulting in delayed visibility, inefficient budget optimization, and missed revenue opportunities.
Two hours of reduced ad reporting latency (down from two days)

Reported impact

Business outcome

Our lakehouse architecture, with all the agentic, analytical, and developer tools on top, allows us to start building the future faster than we could have before. Google's AI ecosystem gives us a natural runway into the next set of capabilities.

  • Productivity Improvement
  • Revenue Growth

Solution

Partnering with Google Cloud, Roundel modernized the Target Product Ads technology stack from the ground up. Using Google Cloud's Lakehouse , Roundel completely rebuilt its data ingestion, processing, and measurement pipelines on Cloud Storage Managed Service for Apache Spark , and BigQuery "The Lakehouse is really powerful for us," Collin says. "When data is generated it lands in Cloud Storage almost instantaneously, and then we use a managed Spark instance to process. BigQuery is our analytical compute engine and allows folks to do ad-hoc queries in near real time to solve special client needs and power our AI algorithms faster than before." Unlike traditional lakehouses, which are engineered for static reporting, this next-generation lakehouse is built for real-time action, rapidly storing and processing raw signals from offsite media placements, ecommerce properties, supplier and logistics systems, and Target's ad platforms.

Solution architecture

5 components · 3 layers
  1. Compute
    • Compute Engine Runs containerized or virtualized application workloads with the elasticity needed for production scale.
  2. Storage
    • BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
    • Cloud Storage Stores source documents, media, or raw files that feed the automated workflow.
    • Lakehouse for Apache Iceberg Provides an open table format that supports low-latency analytics and data sharing.
  3. Orchestration
    • Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.

Architecture clues

  • "Our lakehouse architecture, with all the agentic, analytical, and developer tools on top, allows us to start building the future faster than we could have before," he says.
  • More than "lift and shift" migration story, Roundel's unified data foundation is helping position Target to lead in the era of agentic commerce, where AI agents will interact and transact on the behalf of consumers and businesses.
  • Our lakehouse architecture, with all the agentic, analytical, and developer tools on top, allows us to start building the future faster than we could have before.
  • Paving the way for agentic AI

Evidence from the source

"The Lakehouse is really powerful for us," Collin says. "When data is generated it lands in Cloud Storage almost instantaneously, and then we use a managed Spark instance to process. BigQuery is our analytical compute en
Partnering with Google Cloud, Roundel modernized the Target Product Ads technology stack from the ground up. Using Google Cloud's Lakehouse , Roundel completely rebuilt its data ingestion, processing, and measurement pip
Roundel™ Media designed by Target: Helping retail media ads hit the bullseye with Google Cloud
The biggest challenge was harnessing terabytes of fragmented data across multiple systems and turning it into actionable insights fast. Historically, ad performance data could take over 30 hours to arrive, resulting in d
With the ability to ground AI models in trusted data that accurately reflects real-world realities - like what a guest is looking at right now versus their past purchasing behavior - Roundel is empowering everyone, even
Transparency Evidence & methodology 75/100 reference value · 77% 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 value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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. Google Cloud

    Roundel™ Media designed by Target customer story

    First-party
    https://cloud.google.com/customers/target
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 9, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Oct 8, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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