Retail & ecommerce · Databricks
Flaconi: Personalizing the beauty product shopping experience with data and AI
Public case study · not a Metric Oak engagement
- Millisecond predictions for real-time recommendations
- 300
- Faster time-to-market for new models
- 200x
- Reduction in staff costs
- 40%
What happened
The story in brief
A European online beauty retailer with a large catalogue struggled with streaming data volumes and costly infrastructure. On Databricks it built a platform for real-time recommendations and brought new models to market far faster.
How it compares
- 200x faster time-to-market for new models, against a median speed-up of 10x across 8 retail & ecommerce results.
- 40% reduction in staff costs, against a median reported cost reduction of 50% across 16 retail & ecommerce results.
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Frequently asked questions
What results did Flaconi report?
Flaconi reported 300 millisecond predictions for real-time recommendations; 200x faster time-to-market for new models; 40% reduction in staff costs, according to its customer story published by Databricks.
Which platform and technologies did Flaconi use?
Flaconi used Databricks on AWS, including Delta Lake, MLflow, Tableau.
How do these results compare with other retail & ecommerce programs?
200x faster time-to-market for new models, against a median speed-up of 10x across 8 retail & ecommerce results. 40% reduction in staff costs, against a median reported cost reduction of 50% across 16 retail & ecommerce results.
Source
Customer story published by Databricks: Personalizing the beauty product shopping experience with data and AI (opens databricks.com in a new tab) on databricks.com. Figures as published, retrieved 27 October 2026. The summary above is Metric Oak's own. This is not a Metric Oak engagement.
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