Brownspeed Health Care: Employee Retention Using Predictive Analytics
(4 pages of text)
Case (Gen Exp)
Social and political issues in the US health care system, along with near-record employment levels, had led to a high employee turnover rate throughout the industry in 2019. In October, after Brownspeed Health Care, a consultative service, received numerous requests from its clients for assistance, the chief analytical officer was asked to prepare a report on how the company could help its clients address the problem. The first phase of the plan was to use the Consumer Price Index for urban wage earners and clerical workers as part of a low-risk strategy to enhance employee compensation and deploy wide-scale retention strategies and forecasting services.
This case is suitable for core human resources, economics, and analytics courses at both undergraduate and graduate levels. The case develops a time series forecast of the national Consumer Price Index for urban wage earners and clerical workers and identifies employee retention plans for the health care industry, including salary adjustments based on the cost of living adjustment estimates. After working through the case and assignment questions, students will be able to
- appreciate the growing importance of employee retention;
- identify factors that contribute to employee retention;
- recognize the basic elements of time series forecasting;
- develop a monthly time series forecast of the national Consumer Price Index for urban wage earners and clerical workers using the US Bureau of Labor Statistics database; and
- designate some specific employee retention strategies.
Health Care Services
United States, Medium, 2019
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