Rue La La Eliminates Data Silos To Kickstart Email Personalization

With 16 million members on its emailing list, e-Commerce fashion retailer Rue La La has a lot of potential shoppers seeking an exclusive experience. Realizing that it needed to tell individualized stories to truly engage these shoppers, Rue La La turned to democratized data warehousing service provider Snowflake to:

  • Create more personalized email campaigns and curated product recommendations;

  • Gain a 360-degree view of its sales funnel; and

  • View significantly larger data sets that expose more shopper information to internal analysts.


Rue La La initially structured its information in an existing enterprise data warehouse and used another platform to house its Big Data streams, including email and clickstream activity from its web site and mobile app. But these disparate data silos limited its analysts’ ability to integrate and analyze the data in one fell swoop. Snowflake allowed Rue La La to unify the data from these platforms and gain a single view of the consumer.

The data integration has already paid dividends for Rue La La as the retailer has ramped up its email personalization capabilities. The e-Commerce retailer now targets its shoppers more accurately, with customized emails that display relevant content based on demonstrated interest in products made available in individual boutiques.

“Because our site changes so dynamically every day, often the only way we have to communicate with our consumers is through emails and push notifications to the mobile app, if they’ve download that,” said Erick Roesch, Director of Business Intelligence and Data Warehousing at Rue La La in an interview with Retail TouchPoints. “The first step of personalization is not to push the same exact story to our 16 million consumers. On any given day, similar to a shopping mall, there’s a dozen or two dozen boutiques that we are standing up for the first time, and each boutique has a specific focus. For some customers, certain boutiques would be more desirable than others.”

Understanding Customers’ True Value

The Snowflake platform has finally allowed Rue La La to leverage data to capitalize on its growth trajectory. Rue La La’s “members-only” business model initially built a culture based on word-of-mouth referrals, so as more and more consumers signed up for the email list, the retailer didn’t have to rely on data.

“Our customer base was growing so quickly and we were selling everything we could get our hands on,” Roesch said. “Those were the exciting Wild West days. Fast forward to this year and we’ve now seen a stabilization of the market and in our customer base, and one of the things that has become really critical as we spend more on advertising and try to attract the next set of our customers is understanding their value.”

The Rue La La team is leveraging numerous analysis methods to understand the customer, labeling shoppers with:

  • A Recency Frequency Monetary (RFM) value, which scores members based on purchasing behavioral patterns;

  • A Recency Frequency Engagement (RFE) value, which measures shoppers’ propensity to open emails, browse the site, visit boutiques — essentially everything short of making a purchase; and

  • A total lifetime value, which measures all of their activities.

Marketers can leverage Snowflake to identify the performance of each campaign and fine-tune their efforts. Live site visitation and purchasing data allow planners to assess product interest and customize the site.

Although the Rue La La team wanted to move all its data to a cloud platform, they didn’t want to use a private cloud system such as AWS, where the complexities of supporting the platform would fall strictly on their shoulders.

“The fact that Snowflake is providing the data warehouse as a service has allowed us to focus more on the business objectives and delivery as opposed to the care and feeding of a fairly complex ecosystem,” Roesch said. “Our needs initially had been small, but we recognized that success breeds success, which creates greater needs. We wanted to have a platform that could easily scale both in terms of the diversity and size of the data sets that we’d bring into this ecosystem, as well as from a computing perspective, without having to revisit the investment that we had made in said infrastructure.”

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