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Google Cloud Media and Entertainment United States

Maxim

Marketing Personalization · Real-time Analytics

Maxim needed to reinvent a legacy media business with a modern data architecture that could connect print, digital competitions, and advertising performance without requiring engineers to keep building custom panels and decks.

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Reported impact

Business outcome

Maxim lowered analytics costs, created new performance-based advertising revenue streams, and embedded a metrics-first culture across a mostly non-technical organization.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth

Solution

Maxim uses Google Cloud to address this challenge: Maxim needed to reinvent a legacy media business with a modern data architecture that could connect print, digital competitions, and advertising performance without requiring engineers to keep building custom panels and decks. Looker serves as the live dashboard layer for business, operations, and engineering teams and now includes conversational capabilities for non-technical users, serving as the serving layer. BigQuery provides the read-replica style foundation for mixing historical experiments, transactional information, and real-time operational data, serving as the storage layer. Operational and campaign data is copied out of legacy systems into BigQuery, Looker surfaces live dashboards and natural-language analytics, and the same governed data foundation supports chargeback evidence, influencer tournament performance tracking, and downstream machine-learning models. The implementation integrates with Mixpanel, Segment, Webflow.

Data flow

Operational and campaign data is copied out of legacy systems into BigQuery, Looker surfaces live dashboards and natural-language analytics, and the same governed data foundation supports chargeback evidence, influencer tournament performance tracking, and downstream machine-learning models.

Solution architecture

2 components · 2 layers
  1. Storage
    • BigQuery provides the read-replica style foundation for mixing historical experiments, transactional information, and real-time operational data
  2. Serving
    • Looker serves as the live dashboard layer for business, operations, and engineering teams and now includes conversational capabilities for non-technical users

Architecture clues

  • Looker-backed chargeback dashboards help prove legitimate activity end to end
  • No feature is considered complete until a live Looker dashboard exists
  • The company reduced Mixpanel spend by removing about 80% of redundant metrics

Evidence from the source

Brand partners received 100 to 1,000 times more exposure.
By selecting Looker alongside BigQuery, Maxim established an agile framework to pipe transactional information directly out of legacy databases without impacting production performance.
Established a mandatory metrics-first engineering culture, enforcing policy that no product feature is complete without a live Looker dashboard.
Generated 5x to 10x higher revenue per placement through performance-based metrics, while multiplying advertising ad impressions by 100 to 1,000x
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 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    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
    4 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

    Maxim customer story

    First-party
    https://cloud.google.com/customers/maxim
    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

  • Sitemap last modified Jul 21, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jul 21, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    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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