DataRobot Case Studies Using Explainable AI to Revolutionize the Recruitment Industry and Candidate Experience

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	Using Explainable AI to Revolutionize the Recruitment Industry and Candidate Experience DataRobot

Using Explainable AI to Revolutionize the Recruitment Industry and Candidate Experience

DataRobot

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Professional Service
Applicable Functions	Human Resources
Use Cases	Predictive Quality Analytics Predictive Replenishment
Services	Data Science Services
Challenge	The Adecco Group, UK & Ireland, a part of the Global 500 ranked company, The Adecco Group, was facing an efficiency problem in their recruitment process. The traditional recruitment process involved multiple manual interventions, which were prone to mistakes and human interpretation. Recruiters had to sift through high volumes of CVs, making it difficult to match the right candidates to the right job. With recruiters working full throttle, it was easy for data-driven insights to remain hidden. The company was looking for a solution to reduce time and speed to fill open positions and improve their hiring attraction pipeline for client talent pools. Read More
About Customer	The Adecco Group, UK & Ireland and its brands are part of The Adecco Group, a Global 500 ranked company with annual revenues over 16.8 billion pounds. The Adecco Group is the world’s leading talent advisory and solutions company. The Adecco Group provides services including temporary staffing, permanent placement, career transition, and talent development as well as business process outsourcing and consulting to up to 150,000 clients every day. The company is headquartered in London, UK and operates in the professional services industry. Read More
Solution	The company decided to leverage machine learning (ML) and artificial intelligence (AI) to solve their efficiency problem. They built a data platform to make broader and better use of the data at hand. They then started to look at the advantages to be gained by applying AI to that data. They partnered with DataRobot for an initial project to incorporate MLOps and automate models to reduce the time it took for recruiters to fill jobs, while also building a more appealing pipeline for prospective talent. The project leveraged three metrics to gauge success: productivity, accuracy, and interpretability. The DataRobot models enabled recruiters to sort through resumes more effectively, driving a 37 percent reduction in the number of CVs that had to be reviewed. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Professional Service

Applicable Functions

Human Resources

Use Cases

Predictive Quality Analytics

Predictive Replenishment

Services

Data Science Services

Challenge

The Adecco Group, UK & Ireland, a part of the Global 500 ranked company, The Adecco Group, was facing an efficiency problem in their recruitment process. The traditional recruitment process involved multiple manual interventions, which were prone to mistakes and human interpretation. Recruiters had to sift through high volumes of CVs, making it difficult to match the right candidates to the right job. With recruiters working full throttle, it was easy for data-driven insights to remain hidden. The company was looking for a solution to reduce time and speed to fill open positions and improve their hiring attraction pipeline for client talent pools.

About Customer

The Adecco Group, UK & Ireland and its brands are part of The Adecco Group, a Global 500 ranked company with annual revenues over 16.8 billion pounds. The Adecco Group is the world’s leading talent advisory and solutions company. The Adecco Group provides services including temporary staffing, permanent placement, career transition, and talent development as well as business process outsourcing and consulting to up to 150,000 clients every day. The company is headquartered in London, UK and operates in the professional services industry.

Solution

The company decided to leverage machine learning (ML) and artificial intelligence (AI) to solve their efficiency problem. They built a data platform to make broader and better use of the data at hand. They then started to look at the advantages to be gained by applying AI to that data. They partnered with DataRobot for an initial project to incorporate MLOps and automate models to reduce the time it took for recruiters to fill jobs, while also building a more appealing pipeline for prospective talent. The project leveraged three metrics to gauge success: productivity, accuracy, and interpretability. The DataRobot models enabled recruiters to sort through resumes more effectively, driving a 37 percent reduction in the number of CVs that had to be reviewed.

Impact #1	DataRobot’s best-performing model drove a 37 percent reduction in the number of CVs that had to be reviewed, filtering out those that were not the best match for the job, leading to a 10 percent productivity gain.
Impact #2	In just three weeks, DataRobot enabled the launch of 60 projects utilizing more than 3,000 models, far faster than had been possible using conventional approaches to AI deployment in the past.
Impact #3	The project also met the interpretability goal, with new insights and interactions driving enhancements. For example, the team found that CV length is an important factor in filtering, and shorter resumes should be given positive weight. They also learned quickly to factor in typos and grammatical mistakes as predictive indicators.

Impact #1

DataRobot’s best-performing model drove a 37 percent reduction in the number of CVs that had to be reviewed, filtering out those that were not the best match for the job, leading to a 10 percent productivity gain.

Impact #2

In just three weeks, DataRobot enabled the launch of 60 projects utilizing more than 3,000 models, far faster than had been possible using conventional approaches to AI deployment in the past.

Impact #3

The project also met the interpretability goal, with new insights and interactions driving enhancements. For example, the team found that CV length is an important factor in filtering, and shorter resumes should be given positive weight. They also learned quickly to factor in typos and grammatical mistakes as predictive indicators.

Benefit #1	37 percent reduction in the number of CVs that had to be reviewed.
Benefit #2	10 percent productivity gain.
Benefit #3	Launch of 60 projects utilizing more than 3,000 models in just three weeks.

Benefit #1

37 percent reduction in the number of CVs that had to be reviewed.

Benefit #2

10 percent productivity gain.

Benefit #3

Launch of 60 projects utilizing more than 3,000 models in just three weeks.

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Overview

Using Explainable AI to Revolutionize the Recruitment Industry and Candidate Experience

Operational Impact

Quantitative Benefit