Rue La La

Customer Case Study

Rue La La

Rue La La strives to be the most engaging off-price, online style destination, connecting world-class brands with the next-generation shopper.

Vertical Use Case

Recommendation Engine

Technical Use Case

• Ingest and ETL
• Machine Learning
• Deep Learning

The Challenges

  • In order to give each of their 19M shoppers a unique shopping experience, they must ingest and analyze over 400GB of data per day.
  • Their data pipeline consists of 20+ different queries to transform the data for the data science team which was a highly computationally expensive process.
  • Scale was also a challenge as they lacked a distributed system to meet the performance goals they required.

The Solution

Databricks provides Rue La La with a fully managed, distributed platform that allows them to ETL their data much faster and leverage machine learning to power their recommendation engine.

  • Estimated query speedups of 60%.
  • Parallel model building and prediction resulted in a 25x time savings over competing platforms.
  • All of these improvements and benefits have made a tangible impact to their business, contributing to a 10x increase in purchase engagement, repeat visitation rates of over 60%, and incremental revenue gains of over 5% in a beta release program.

Databricks has enabled our data science team to work more collaboratively, empowering them to easily iterate and modify the models in a distributed fashion much faster.

Benjamin Wilson, Lead Data Scientist at Rue La La