DataRobot Case Studies Speeding up the Predictive Analytics Process with Automated Machine Learning

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	Speeding up the Predictive Analytics Process with Automated Machine Learning DataRobot

Speeding up the Predictive Analytics Process with Automated Machine Learning

DataRobot

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The Evariant/DataRobot collaboration resulted in thousands of validated predictive models, in an automated context.
Impact #2	The Evariant team was able to choose the most statistically reliable, valid, and appropriate model results using a collaborative and sophisticated cross-validation framework.
Impact #3	The results can be seen in the volume of models Evariant is creating and deploying — nearly 10x the previous pace.

Impact #1

The Evariant/DataRobot collaboration resulted in thousands of validated predictive models, in an automated context.

Impact #2

The Evariant team was able to choose the most statistically reliable, valid, and appropriate model results using a collaborative and sophisticated cross-validation framework.

Impact #3

The results can be seen in the volume of models Evariant is creating and deploying — nearly 10x the previous pace.

Benefit #1	The volume of models Evariant is creating and deploying is nearly 10x the previous pace.
Benefit #2	The quality of cross-sell, up-sell, retention, acquisition, re-acquisition, and other strategies has significantly increased.
Benefit #3	These efficiencies lead to increased ROI, not only for Evariant’s clients, but for Evariant as well.

Benefit #1

The volume of models Evariant is creating and deploying is nearly 10x the previous pace.

Benefit #2

The quality of cross-sell, up-sell, retention, acquisition, re-acquisition, and other strategies has significantly increased.

Benefit #3

These efficiencies lead to increased ROI, not only for Evariant’s clients, but for Evariant as well.

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Overview

Speeding up the Predictive Analytics Process with Automated Machine Learning

Operational Impact

Quantitative Benefit