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Google Cloud Commercial and Professional Services United States

NextAfter

IT Operations Automation · Marketing Personalization · Real-time Analytics

With clients ranging from higher education institutions to advocacy groups, NextAfter strives to provide high-level, multi-channel insights. The challenge? Doing so with a two-person data team that supports a broader client services team.

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1,000 hours saved by consolidating dashboards into a single view with Looker
Reduced key pipeline runs from 15 to two minutes with Gemini Data Engineering Agent
Unified data visibility across 10,000+ digital experiments and unified clients' data silos into a single source of truth
~50% reduction in time to onboard for new analysts

Reported impact

Business outcome

Reduced key pipeline runs from 15 to two minutes with Gemini Data Engineering Agent; Deployed new AI agents in three weeks versus months with Agent Development Kit; ~50% reduction in time to onboard for new analysts; Unified data visibility across 10,000+ digital experiments and unified clients' data silos into a single source of truth.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

This has had a profound impact on the speed of development. A meta-optimizer tool that previously lingered in the prototyping phase was coded and standardized for Google Cloud in just six hours using the Gemini Data Engineering Agent in BigQuery. Similarly, Gemini helped rebuild a data pipeline that queries 55 different Google Analytics sources, reducing the run time from 20 minutes to just two. Beyond data unification, NextAfter has transitioned to Google Workspace with Gemini and Gemini Enterprise , which acts as a force multiplier for the company's lean team. Using Agent Developer Kit, a no-code agent builder, the team also created "Winston AI." This agent sits on top of the company's experiment database—called Winston—to help account leads brainstorm new A/B testing ideas based on historical learnings.

Data flow

stored or unified in BigQuery, and surfaced through Gemini Enterprise, Gemini, Looker

Solution architecture

5 components · 3 layers
  1. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
  2. Serving
    • Gemini Enterprise Provides a secure enterprise environment for teams to use Gemini with governed data access.
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
    • Looker Provides the governed semantic layer, dashboards, and self-service analytics used by business teams.
  3. Governance
    • LookML Defines governed metrics and semantic models so teams and AI workflows use the same business logic.

Architecture clues

  • "Data is bigger than just running queries. With Gemini, I can train my small team to do more," Duff-Hudkins says. "It gives our account leads the ability to ask questions themselves without relying on a data bottleneck."
  • 1,000 hours saved by consolidating dashboards into a single view with Looker
  • NextAfter uses BigQuery and Gemini to process donor data faster, empowering a lean team to scale nonprofit fundraising.
  • Reduced key pipeline runs from 15 to two minutes with Gemini Data Engineering Agent
  • long-term innovation with Gemini
  • on BigQuery and Looker

Evidence from the source

1,000 hours saved by consolidating dashboards into a single view with Looker
Deployed new AI agents in three weeks versus months with Agent Development Kit
Reduced key pipeline runs from 15 to two minutes with Gemini Data Engineering Agent
Unified data visibility across 10,000+ digital experiments and unified clients' data silos into a single source of truth
~50% reduction in time to onboard for new analysts
Transparency Evidence & methodology 100/100 reference value · 79% 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 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. Google Cloud

    NextAfter customer story

    First-party
    https://cloud.google.com/customers/next-after
    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 May 16, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    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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