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.”
Swarovski's Marketing personalization is part of this use case:
Luxury crystal jewelry, watches, and home accessories manufacturer and retailer.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Supported Swarovski's migration of over 1,000 data objects to its BigQuery Data Lakehouse.
Related implementations across industries and use cases
Basket analysis took five months to build. Now, marketers use conversational AI to segment customers and launch campaigns instantly.
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.
Data scattered across systems forced manual consolidation. Now, AI unifies insights to trigger personalized campaigns instantly.
Exact-match search missed synonyms. AI now maps intent—linking "cooling goods" to inventory—turning vague queries into sales.
Routine queries burdened HR. Now, an AI WhatsApp agent instantly answers remote factory workers, escalating complex issues to HR.
Disparate tools slowed 320,000 employees. Unifying on Teams and Copilot connected the workforce and embedded AI into daily habits.
Shoppers built lists item-by-item. Now, they build full baskets in one prompt, while staff automate internal workflows.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
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.”
Swarovski's Marketing personalization is part of this use case:
Luxury crystal jewelry, watches, and home accessories manufacturer and retailer.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Supported Swarovski's migration of over 1,000 data objects to its BigQuery Data Lakehouse.
Related implementations across industries and use cases
Basket analysis took five months to build. Now, marketers use conversational AI to segment customers and launch campaigns instantly.
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.
Data scattered across systems forced manual consolidation. Now, AI unifies insights to trigger personalized campaigns instantly.
Exact-match search missed synonyms. AI now maps intent—linking "cooling goods" to inventory—turning vague queries into sales.
Routine queries burdened HR. Now, an AI WhatsApp agent instantly answers remote factory workers, escalating complex issues to HR.
Disparate tools slowed 320,000 employees. Unifying on Teams and Copilot connected the workforce and embedded AI into daily habits.
Shoppers built lists item-by-item. Now, they build full baskets in one prompt, while staff automate internal workflows.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.