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Google Cloud Retail United States

Etsy

Marketing Personalization · Real-time Analytics · Supply Chain Optimization

~80x increase in listings per theme with "algotorial curation" Nearly 90 million personalized shopping experiences for nearly 90 million shoppers

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5% increase in visits and 3% conversion lift with SEO optimizations
Etsy creates personalized experiences for its nearly 90 million shoppers by using Gemini platform, BigQuery, Dataflow, and Gemini models to better understand inventory, customer intent, and individual buyers.
Nearly 90 million personalized shopping experiences for nearly 90 million shoppers
Our inventory comprises more than 130 million items from more than 5 million sellers. Google gen AI and LLM technologies help us enrich our data to better understand our inventory's distinctive characteristics.

Reported impact

Business outcome

~80x increase in listings per theme with "algotorial curation"; Nearly 90 million personalized shopping experiences for nearly 90 million shoppers; 5% increase in visits and 3% conversion lift with SEO optimizations.

  • Cost Reduction
  • Customer Experience
  • Revenue Growth
  • Risk Reduction

Solution

Most of Etsy's inventory management products are built on Google Cloud and use BigQuery, Dataflow , Gemini platform, and Gemini models to understand inventory and connect every buyer with the right item. "Our inventory comprises more than 130 million items from more than 5 million sellers," says Yavuz. "Our approach to inventory search and discovery requires dynamic understanding or insights. That's only possible with today's generative AI technologies. Google gen AI and LLM technologies help us enrich our data to better understand our inventory's distinctive characteristics." The first step in this journey was to create a foundational dataset using Gemini models. The team then used Google Cloud AI technologies, with Gemini platform and BigQuery at the center, to connect, verify, and validate this data.

Data flow

stored or unified in BigQuery, orchestrated via Gemini platform, and surfaced through Gemini

Solution architecture

3 components · 3 layers
  1. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
  2. Serving
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
  3. Orchestration
    • Gemini platform Coordinates the solution's agentic and generative AI workflows on Google Cloud.

Architecture clues

  • Etsy creates personalized experiences for its nearly 90 million shoppers by using Gemini platform, BigQuery, Dataflow, and Gemini models to better understand inventory, customer intent, and individual buyers.
  • With the use of Gemini, Etsy improved alt text generation for listings on Etsy, increasing SEO-driven visits by 5% and boosting conversions by 3% for our sellers.

Evidence from the source

5% increase in visits and 3% conversion lift with SEO optimizations
Etsy creates personalized experiences for its nearly 90 million shoppers by using Gemini platform, BigQuery, Dataflow, and Gemini models to better understand inventory, customer intent, and individual buyers.
Etsy is working to scale that human connection so that each one of the nearly 90 million individuals who shop on the marketplace can easily find the items they consider special.
Nearly 90 million personalized shopping experiences for nearly 90 million shoppers
~80x increase in listings per theme with "algotorial curation"
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Etsy customer story

    First-party
    https://cloud.google.com/customers/etsy-ai
    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 2, 2025 · 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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