What challenges did Merck Life Science face before AI implementation?

Merck Life Science needed deeper predictive analytics beyond page views and product sales. Volatile demand and complex global supply chains made it difficult to keep products available at the right time. Its previous third-party data solution was two days behind events.

What was the impact of the AI solution?

Product-comparison page traffic increased by 400%. Looker ecommerce dashboard traffic increased by 650% in two years. The company said product recommendations are a huge revenue driver. The supply-chain models help meet fluctuating demand and reduce inefficiencies. The work improved data literacy and transparency across the organization.

Source

GCP

Merck Life Science: Vertex AI + BigQuery product recommendations and supply-chain ML

Use case typePredictive decision supportUpdated Jun 13, 2026

Life Science business of Merck KGaA Darmstadt, Germany moved its data stack to Google Cloud to improve insights for product discovery and supply-chain planning. The company combined ERP and Google Analytics data in BigQuery, then used Vertex AI to build buy-it-again and personalized product recommendation models, similar-product comparison grids, and supply-demand forecasting models. The article also says Merck Life Science is exploring generative AI dashboards to explain demand spikes and summarize customer feedback.

Organization: Merck Life Science
Industry: Pharma
Location: Germany
Published: February 2025

Reported outcomes

+650%

quantified impactOther quantified impact

+400%quantified impact

Strategic outcomes

New product / capabilityBuilt personalized product recommendation modelsNew product / capabilityAdded product-comparison grid insightsBetter decisions & insightImproved supply-demand forecasting and anomaly analysisCustomer experience & trustImproved data transparency across departments

Primary read

Use case focus

Showing 3 of 4

1Product recommendation
2Supply chain optimization
3Predictive analytics

Merck Life Science needed deeper predictive analytics beyond page views and product sales.
Volatile demand and complex global supply chains made it difficult to keep products available at the right time.
Its previous third-party data solution was two days behind events.

Migrated the data stack to Google Cloud.
Combined ERP and web analytics data in BigQuery for near-real-time insights.
Built buy-it-again and personalized product recommendation models in Vertex AI.
Used algorithms to highlight differences in product-comparison grids.
Built machine-learning models in Vertex AI to support supply-demand forecasting and anomaly analysis.
Used Looker dashboards to democratize access to business data across departments.
Explored generative AI dashboards for demand-surge explanations and customer review summarization.