Insights were trapped in 30 disconnected silos. Now, a unified Gen AI portal lets teams localize campaigns 10x faster.
A global luxury brand founded in 1895, operating over 2,300 boutiques across 140 markets with complex supply chain and retail operations.
Legacy data structures designed for financial reporting could not support real-time personalization, leaving insights trapped in disconnected silos....
“We came from a time when everyone had their version of the truth. Our data structures were mainly designed for financial reporting in the last two decades, not for real-time personalization in the 2020s.”
Luxury crystal jewelry, watches, and home accessories manufacturer and retailer.
Supported Swarovski's migration of over 1,000 data objects to its BigQuery Data Lakehouse.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Swarovski's Marketing personalization is part of this use case:
Related implementations across industries and use cases
Manual concepting dragged for eight weeks. Marketers now generate on-brand assets in eight hours using a custom AI platform.
Analysts spent days manually merging data. GenAI now generates localized campaign summaries for 20 audiences across six countries.
Merchandisers manually updated rules weekly to decode trade slang. Now, AI continuously trains on live data to process complex queries.
Exact-match search missed synonyms. AI now maps intent—linking "cooling goods" to inventory—turning vague queries into sales.
Scaling curation for 90M buyers was impossible. Now, experts build seed collections and Vertex AI expands them into millions of custom feeds.
Ad-hoc queries required custom dashboards. Now, specialized agents answer natural language questions with 99.9% accuracy.
Testing physical prototypes delayed evaluations by weeks. Now, researchers use AI consumer personas to simulate product trials in hours.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
Translating updates one language at a time bottlenecked distribution. Now, AI drafts multilingual commentary in minutes for editor review.
Insights were trapped in 30 disconnected silos. Now, a unified Gen AI portal lets teams localize campaigns 10x faster.
A global luxury brand founded in 1895, operating over 2,300 boutiques across 140 markets with complex supply chain and retail operations.
Legacy data structures designed for financial reporting could not support real-time personalization, leaving insights trapped in disconnected silos....
“We came from a time when everyone had their version of the truth. Our data structures were mainly designed for financial reporting in the last two decades, not for real-time personalization in the 2020s.”
Luxury crystal jewelry, watches, and home accessories manufacturer and retailer.
Supported Swarovski's migration of over 1,000 data objects to its BigQuery Data Lakehouse.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Swarovski's Marketing personalization is part of this use case:
Related implementations across industries and use cases
Manual concepting dragged for eight weeks. Marketers now generate on-brand assets in eight hours using a custom AI platform.
Analysts spent days manually merging data. GenAI now generates localized campaign summaries for 20 audiences across six countries.
Merchandisers manually updated rules weekly to decode trade slang. Now, AI continuously trains on live data to process complex queries.
Exact-match search missed synonyms. AI now maps intent—linking "cooling goods" to inventory—turning vague queries into sales.
Scaling curation for 90M buyers was impossible. Now, experts build seed collections and Vertex AI expands them into millions of custom feeds.
Ad-hoc queries required custom dashboards. Now, specialized agents answer natural language questions with 99.9% accuracy.
Testing physical prototypes delayed evaluations by weeks. Now, researchers use AI consumer personas to simulate product trials in hours.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
Translating updates one language at a time bottlenecked distribution. Now, AI drafts multilingual commentary in minutes for editor review.