1_6

Using Big Data To Prevent Employee Turnover

Using Big Data To Prevent Employee Turnover

 

According to the Bureau of Labor Statistics (BLS), employment in the wholesale trade industry is projected to grow 0.8 percent annually until 2022, and employment in the transportation and warehousing industry is projected to grow 0.7 percent annually within the same time period. This means that distributors will look to hire more new employees year-over-year for nearly a decade, while also working to replace retiring baby boomers.

Hiring a new member of the workforce is taxing on resources, requiring a significant amount of time from HR departments and monetary investment in terms of providing both salary and benefits. Decision-makers are essentially "investing" in this new individual, and just like any other investment, maximizing the return is critical to promote the organization’s success. Employees that are committed and a good match for their role stand to provide the greatest return for the company. In contrast, a lack of commitment to both the position and organization detracts from the employee’s ability to reach optimal performance and tenure, thereby diminishing the overall ROI.

Capitalizing on the Human Investment

An organization is only as good as its products and services, and superior service is delivered by dedicated, knowledgeable employees. Building a workforce comprised of individuals who will remain with the company long-term, retain institutional information and do their best to represent the brand all begins with the hiring process. While most distributors utilize standardized steps for candidate selection, these processes are often lacking in several ways, and independently do not help to prevent turnover.

Read Also:
The Top 5 Data Lake Capabilities Required to Deliver Business Value

Initially, candidates for interviews are chosen based on their resume or cover letter, which outlines past experience and education. These criteria are important, however, they are not the most reliable indicators of success, as most skills can be taught over a period of time. During the second phase of screening, hiring managers interview candidates face-to-face to determine whether they are a fit. Making a decision after this brief time together essentially calls for "gut instinct" hiring, where the HR manager is making a selection based on only a few short hours with the candidate. While processes such as these certainly have a place in hiring, they do not guarantee that the right applicant is selected for the right position.

Technology now offers a viable resource to help distributors better select and retain top talent, and maximize the return on their investment in employees. By supplementing or altering existing hiring processes with a talent science platform, organizations are positioned to select the applicant that is most likely to succeed within the role and unique company culture. Technology allows distributors to leverage the value of objective data, helping to pinpoint individuals with the highest probability of commitment and success using a behavioral-based selection system.

Read Also:
Could Your Social Media Profile Be Your New Credit Score?

Talent science is a cloud-based technology that combines big data and analytics to assess a candidate’s behavioral attributes such as "ambition" or "attention to  detail." Distributors must first create a behavioral model by assessing the incumbent population and gathering performance data to identify the combination that seems to deliver the highest productivity and desired results for a particular job. Subsequently, this information can then be used to identify behavioral patterns that are most likely to predict success in a given role.

Benefits of a Tenured Workforce

By objectively meeting staffing needs using talent science technology, distributors can realize significant long-term benefits and gain competitive advantage. Relying on science, in addition to traditional hiring methods, allows organizations to eliminate high-risk candidates from the onset and assess applicants outside the scope of their standard skills and job experience prior to meeting in person. By helping HR managers to identify the best fit candidate, talent science allows distributors to:

Consider this illustration. A wholesale distributor of heating and air conditioning components asks its HR manager to hire the most sociable, ambitious people to manage front counter sales, as this position requires frequent face-to-face interaction with customers.While this seems like a logical plan, upon hiring individuals of this nature, the company experiences high turnover because employees with very high ambition often became dissatisfied with the role fairly quickly. If this distributor had utilized a talent science application,the HR manager would have recognized from the onset that more modest ambition was characteristic of its existing,successful counter sales employees. Therefore, the organization would have sought similar candidates, which would have reduced turnover, saving the distributor time and money.

Read Also:
Internet of Things Turns New York's Lake George into Smartest Lake


Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
An essential comparison between In-Memory Database vs. In-Memory Data Grid

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
Match CEO Explains The Algorithms Of Love

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
An essential comparison between In-Memory Database vs. In-Memory Data Grid

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Predictive Analytics in Higher Education

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Demystifying the Role of Artificial Intelligence in Marketing and Advertising

Leave a Reply

Your email address will not be published. Required fields are marked *