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Google Cloud Software / SaaS Global

Supermetrics

IT Operations Automation · Real-time Analytics

Supermetrics wanted to turn marketing reporting from a manual weekly task into an agentic workflow that could fix broken data connections, interpret campaign performance, and recommend next actions automatically.

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Reported impact

Business outcome

Supermetrics eliminated manual reporting effort, gave marketers back strategic time, and created an agentic system that can keep improving its own data workflows.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Supermetrics uses Google Cloud to address this challenge: Supermetrics wanted to turn marketing reporting from a manual weekly task into an agentic workflow that could fix broken data connections, interpret campaign performance, and recommend next actions automatically. Agent Development Kit lets the team build modular functions that can be assembled into one marketing intelligence agent, serving as the orchestration layer. Vertex AI Agent Builder provides the runtime and tooling for the agent that automates reporting and error handling, serving as the serving layer. Imagen supports the generation of new creative options suggested by the marketing agent, serving as the compute layer. Marketing data connections feed the agent, ADK modules handle error detection and workflow logic, Vertex AI Agent Builder serves the live reporting assistant, and the system can analyze performance in real time and even suggest new creative assets through Imagen.

Data flow

Marketing data connections feed the agent, ADK modules handle error detection and workflow logic, Vertex AI Agent Builder serves the live reporting assistant, and the system can analyze performance in real time and even suggest new creative assets through Imagen.

Solution architecture

3 components · 3 layers
  1. Compute
    • Imagen supports the generation of new creative options suggested by the marketing agent
  2. Serving
    • Vertex AI Agent Builder provides the runtime and tooling for the agent that automates reporting and error handling
  3. Orchestration
    • Agent Development Kit lets the team build modular functions that can be assembled into one marketing intelligence agent

Architecture clues

  • ADK let the team start with small modules and expand the system iteratively
  • The agent handles both pipeline errors and downstream performance analysis
  • The marketing intelligence agent saves more than 15 hours per month per marketer

Evidence from the source

Acting like a digital assistant, the Supermetrics Marketing Intelligence Agent autonomously manages data connections, fixes errors, and analyzes campaign performance in real time.
It can even suggest new creative options using Imagen.
Supermetrics used Google Cloud ADK and Vertex AI Agent Builder to create an AI agent that automates weekly marketing reporting.
Transparency Evidence & methodology 70/100 reference value · 72% 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 value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 confidence72%
  • 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 evidence15/20
    3 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

    Supermetrics customer story

    First-party
    https://cloud.google.com/customers/supermetrics
    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 Mar 17, 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 Feb 23, 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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