The examples below illustrate the potential annual savings across our clients by using Cadient Decision Point™ recommendations. For each of these examples, we compiled data for each client consisting of 5 years of their historical applicant records, hired status, and employee tenure.
Using machine learning, we analyzed the dataset to learn which combination of attributes were common across the employees who were considered good hires based on length of tenure. These attributes usually vary from business to business, and they can even vary within a business based on geography and economic climate. Before this analysis is done, we remove any attributes that could introduce bias from the data. Attributes such as name, age, gender, and race are not factored into the hiring model.
For each client, we looked at how many of their actual hires Decision Point would have recommended versus how many they hired that Decision Point would not have recommended. Our analysis estimated the bottom-line impact of hiring fewer employees who lasted longer versus repeatedly hiring ill-suited and short-tenured employees to fill those same positions. We multiplied these saved hires by $1,500 – which is the average cost per hire – to calculate the potential savings.