Sleepwear Icon Eberjey Finds Profitable Growth with First-Party Data in Snowflake
“Reactor helps Eberjey unify and model data across our entire business footprint, to better understand our customers and to act on our data. Reactor ensures good data and useful modeling, while we own our data and open data infrastructure.”
The company
- Company profile
- Founded in 1996 Privately Held
- Industry
- Luxury Sleepwear
- Website
- eberjey.com
Data stack
- Pipeline & Transformation
- Reactor
- Destination
- Snowflake
- Data Sources
- Shopify, Klaviyo, Google Analytics, Attentive, Meta (Facebook and Instagram), TikTok, Google, as well as email and SMS
- Business Intelligence
- Sigma Computing
- Data Activation
- Meta (Facebook), Klaviyo via Census Embedded
Overview
Eberjey addresses acquisition and retention marketing, merchandising, and operations — all in a system designed for future-proof interoperability with the modern data stack, and open observability for IT and data teams. Eberjey is now growing omnichannel topline revenue by double digit percentages in 2023 on almost 25% less advertising cost, performing at an ROAS that is almost 30% higher year-over-year. In other words, Eberjey is growing faster while spending less thanks to the insights gained using Reactor.
Challenge
Lifestyle brand Eberjey is known for luxury sleepwear, lounge and daywear for the softer side of life. The company operates a dynamic, high-growth omnichannel business with a modern eCommerce storefront, retail stores in major metro markets like New York and Newport Beach, and seasonal pop-up stores in fashionable spots like the Hamptons.
With growing omnichannel complexity, Eberjey needed a holistic approach to business insights and optimization. Particularly important to leadership was a two-part profitable growth strategy: first, acquire new customers efficiently, turning a positive contribution profit on each first order; second, understand long-term customer value and value potential to build more personalized experiences for loyal customers without compromising profitability.
The brand had outgrown its basic eCommerce analytics approach, lacked unified data infrastructure to unlock shopper awareness and activation, and needed extensible cloud data infrastructure to measure and optimize the entire business end-to-end.
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Solution
After evaluating and rejecting siloed point solutions for retail analytics or a customer data platform (CDP), Eberjey chose Reactor to provide unified data analytics and activation spanning digital marketing and merchandising, store operations, and doorstep deliveries.
For Eberjey, Reactor onboards cross-functional data to Snowflake, an open cloud data warehouse. Data is rendered into useful analytical models specific to key retail concerns like paid media efficiency, shopper lifetime value (CLV), order contribution profitability, operational exceptions, and fulfillment latency. With Reactor, Eberjey has made data its omnichannel advantage.
Impact
What changed for Eberjey.
Create a single source of truth
Granular, cross-functional data hosted on cloud infrastructure that the brand owns and controls
Define data for shared understanding
Data harmonized into shared business concepts and performance metrics for every employee
Unify data models
Built from 5+ years of disparate omnichannel data sources to gain a holistic view of the entire business
Reduce engineering burden
Expand data work from engineering to all employees
Lower software and cloud costs
Prep and map data during onboarding to simplify modeling and reduce data processing costs
Optimize business performance
Of first-party data campaigns, channel, programs and behaviors
Increase decision-making agility
Query and segment data using approachable no-code and generative AI interfaces to answer questions and build campaigns that would otherwise require data engineering
Improve profitability and shopper lifetime value
30% increase in ROAS and 22% customer growth
Eberjey use cases
How they put Reactor to work.
- 01
Data Transformation and Enhancement
Implement data cleansing and standardization processes to ensure data accuracy and consistency. Integrates and transforms disparate data sources into unified data models, enriching the data with derived attributes and metrics for advanced analytics and reporting in Eberjey's data warehouse.
- 02
Semantic Entity Mapping and Normalization
Create shared data models for common business concepts like orders, shoppers, products SKUs, shipments and promotional offers. Dynamically update models as source and destination schemas evolve. Unify entities where desired, as with common product SKUs across sales channels; and define them separately where needed, as with unique customer profiles and performance metrics for consumer shoppers vs. wholesale channel customers.
- 03
Optimize Data for Downstream Use Cases
Shape and label data distinctly for unique downstream outputs like generative AI augmentation, business intelligence, and data activation paths like rETL and campaign automation tools.
- 04
Improve Customer Segmentation
Develop customer segmentation models to identify distinct customer groups based on store and digital purchasing behavior, demographics, and other relevant factors. This could be used to target marketing campaigns or personalize product recommendations.
- 05
Measure the Strategic ROI of Ad Campaigns
Unify deep order detail and new and repeat shopper engagement and behavior with Multi-Touch Attribution (MTA) to better understand the long term value of paid media
