Speeding up the Predictive Analytics Process with Automated Machine Learning
Customer Company Size
Mid-size Company
Region
- America
Country
- United States
Product
- Evariant CRM solutions
- DataRobot platform
Tech Stack
- Amazon Web Services (AWS)
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
- Sales & Marketing
Use Cases
- Predictive Quality Analytics
- Predictive Maintenance
Services
- Data Science Services
About The Customer
Evariant, founded in 2008, has emerged as one of the fastest-growing SaaS companies in the healthcare provider market. The company delivers a suite of innovative CRM solutions that help healthcare systems identify and execute on the most important strategic growth initiatives, including patient engagement, physician alignment, and optimized and innovative marketing. Evariant believes that technology and innovation can empower the connection between patients and healthcare providers, and can ultimately revolutionize the way healthcare service delivery and marketing are practiced.
The Challenge
Evariant, a rapidly growing SaaS company in the healthcare provider market, delivers a suite of innovative CRM solutions that help healthcare systems identify and execute on the most important strategic growth initiatives. However, the company faced a challenge in building and deploying predictive analytics, which can be costly and time-consuming. The complexity of their healthcare data demanded a high level of hands-on data preparation, making their existing solution adequate, but not optimal. They needed high-quality predictive analytics that could be generated both automated and semi-automated — and with an extremely high degree of reliability and validity.
The Solution
Evariant collaborated with DataRobot to automate and semi-automate the predictive analytic processes, speeding model building and extending the deployment lifecycle. The DataRobot platform, hosted on Amazon Web Services (AWS), allowed for this automation. This collaboration resulted in thousands of validated predictive models, in an automated context. The Evariant team was able to choose the most statistically reliable, valid, and appropriate model results using a collaborative and sophisticated cross-validation framework. The results can be seen in the volume of models Evariant is creating and deploying — nearly 10x the previous pace.
Operational Impact
Quantitative Benefit
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