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Title
Flaconi personalizes beauty product recommendations in real time with Databricks
…Demo Login Contact Us Try Databricks Customer Stories / Flaconi CUSTOMER STORY Personalizing the beauty product shopping experience with data and AI 300 Millisecond predictions for real-time recommendations 200x Faster time-to-m…
Description
Flaconi, a European online beauty retailer with 2.3 million customers, used the Databricks Data + AI Platform on AWS, with Delta Lake, MLflow, and SageMaker's DeepAR, to build a streaming machine learning pipeline for real-time product recommendations, reducing prediction latency from 20 minutes to 300 milliseconds, cutting model deployment time-to-market by 200x, reducing ML staff costs by 40%, and increasing net order income by 5%.
…ndations 200x Faster time-to-market for new models 40% Reduction in staff costs With over 55,000 products and more than 2.3 million customers, Flaconi wanted to leverage data and AI to become the No. 1 online beauty product destination in Europe. However, they struggled with massive volumes of streaming data and with infrast…
Company
Flaconi
…rity and Trust Ready to get started? Get a Demo Login Contact Us Try Databricks Customer Stories / Flaconi CUSTOMER STORY Personalizing the beauty product shopping experience with data a…
Industry
Retail
…etter understand how models are impacting the business. Share this post Details Industry : Retail and Consumer Goods Use Case : Data Science , Data Engineering Cloud : AWS Product : Delta Lake , A…
Problem
Flaconi struggled with massive volumes of streaming data and infrastructure complexity that was resource-intensive and costly to scale, while needing to support different infrastructure requirements for data analysts and data scientists to deliver a personalized online shopping experience.
…nd AI to become the No. 1 online beauty product destination in Europe. However, they struggled with massive volumes of streaming data and with infrastructure complexity that was resource-intensive and costly to scale. To tackle these issues, Flaconi implemented the Databricks Data + AI Platform…
Solution
Flaconi implemented the Databricks Data + AI Platform on AWS, using Delta Lake for reliable streaming data pipelines, MLflow for model lifecycle management, and Amazon SageMaker with the DeepAR algorithm to forecast demand, enabling streaming analytics and machine learning for real-time product recommendations.
…ersioning, model registry, easy-to-track experiments and results visualization. As an AWS shop, they are also using SageMaker and the DeepAR algorithm to forecast demand against historical trends. Implementation has been fast and reliable, according to Dr. Schatten. “Databric…
Technology
Delta Lake, MLflow, Amazon SageMaker, Tableau
…structure and optimize our ML-dedicated data extraction processes. In addition, we are able to integrate with Tableau to deliver business performance reports for our stakeholders. This improves transparency within the business.” Specific components that have…
Use case type
Personalisation
…perience for their customers. Through streaming analytics and machine learning, they are now able to provide real-time predictions to ensure rapid and accurate recommendations for customers — driving bigger cart values and, therefore, making a positive impact on conver…
Headline outcome
…ore impacting conversion rates and the speed of purchasing decisions, therefore we expect increasing overall revenues with net order income increasing by 5%. The introduction of in-cart recommendations is also driving larger cart values…
Business value
Infrastructure setup time fell from two days to 15 minutes, letting the ML team deploy recommender models 200x faster; prediction latency fell from 20 minutes to 300 milliseconds; ML staff costs decreased by 40%; and the company expects increasing overall revenues, with net order income increasing by 5%.
…een considerable for Flaconi from both a revenue and a cost-saving perspective. Databricks enabled infrastructure setup to be reduced from two days to 15 minutes without dependencies from other departments — allowing the ML team to deploy recommender models to customers 200x faster, which has improved customer engagement. The cost of the staff needed to develo…
AI capabilities
Recommendation & Personalization, Predictive Analytics
…duced from two days to 15 minutes without dependencies from other departments — allowing the ML team to deploy recommender models to customers 200x faster, which has improved customer engagement. The cost of the staff needed to develo…
Deployment options
cloud
…Industry : Retail and Consumer Goods Use Case : Data Science , Data Engineering Cloud : AWS Product : Delta Lake , Agent Bricks Ready to get started? Try Databricks for fr…
