Embrace Home Loans Doubles Its Return on Marketing Investment (ROMI) with DataRobot Zepl
Company Size
1,000+
Region
- America
Country
- United States
Product
- DataRobot Zepl
Tech Stack
- Snowflake
- Python
- SQL
- Jupyter notebooks
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Predictive Quality Analytics
Services
- Data Science Services
About The Customer
Embrace Home Loans is a prominent mortgage lender that provides borrowers and financial institutions with an exceptional mortgage experience. Founded in 1983 and based in Middletown, Rhode Island, the company is licensed in all 50 states and the District of Columbia. Embrace has been recognized seven times as one of the Best Medium-sized Companies to Work for in America by Fortune and five times by Inc. The company has also been recognized fourteen times as one of the Best Places to Work in Rhode Island, as the Most Community Involved Company in Rhode Island, and with the Leadership Excellence Award by Providence Business News.
The Challenge
Embrace Home Loans, a prominent mortgage lender licensed in all 50 states and the District of Columbia, sought to optimize its marketing spend across its digital and direct mail channels. The company wanted to maximize marketing spend and increase revenue across all marketing channels. The challenge was to do so across the scale of Embrace’s operations, which was a significant task. The company needed a solution that could manage hundreds of Jupyter notebooks and run SQL queries on millions of rows of data. The solution also needed to ensure the security of Embrace’s customer data, which included risk-based and standards-based security protocols to protect all data.
The Solution
Embrace Home Loans used DataRobot Zepl to run real-time queries on its Snowflake data. DataRobot Zepl allowed the team to run SQL queries on millions of rows and cut steps in its process. They could build complex models, store them as an object in Python, and then call those objects to score files from Snowflake. The combination of DataRobot Zepl and Snowflake allowed Embrace’s team to develop precise and evolving augmented intelligence models to get an optimized list with tailored, pre-approved offers. The lender does this across millions of records with more than 1,500 features. Embrace develops models based on samples built by a credit bureau, appends data elements, and creates dependent variables. When files come in, Embrace scores and optimizes them, de-identifies names, purchases data, and then sends the names to their execution partners. Customer data is loaded into the Snowflake contact history, allowing them to track conversions.
Operational Impact
Quantitative Benefit
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