Predictive Workforce Analytics – The Missing Link in Your HR Strategy

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Employee-manager relationship is the glue that binds an organization. Hence estranging this vital relationship results in attrition and negative comments trending on social media platforms in no time.

Companies invest up to 6 to 9 months’ salary in replacing a salaried employee. Most important are the below costs:

Due to these factors, an average employee spends 3 to 4 months in a company before her/his contribution starts to outweigh the hiring cost.

The Center for American Progress estimates that companies invest 20% of the annual salary of a mid-senior employee to get a new candidate on board. Apart from the above-mentioned figure, the overall cost involves several intangible and untracked components.

Companies can bear the financial costs, but the reputational damage due to poor review and bad word of mouth still tarnishes their image.

So what is the way ahead? How can organizations counter this problem?

How to reduce employee turnover? Can it be analyzed and predicted?

Often the management is left clueless as to why the company’s top performers resign unwarrantedly. The question that frequently comes to their mind is “Could we have spotted anything before the employee resigned?” or “Could we have taken necessary steps to prevent such an unforeseen situation?”.

The answer is yes – it can be done through the implementation of predictive workforce analytics.

Predictive analytics is the use of statistical and algebraic techniques to identify the likelihood of future outcomes based on historical data. Predictive workforce analytics uses employee related data, allowing leaders to improve the effectiveness of people-related decisions and HR strategies. This helps the management predict unforeseen situations, and adopt suitable workforce strategies to reduce attrition risk, resulting in longer-tenured high performing employees.

A 2015 EIU survey found that 82% of all organizations plan to either begin or increase their use of “big data” in HR over the next three years. Many companies have already adopted this trend:

CEOs are gradually recognizing the importance of using employee-related data to manage recruitment, retention, turnover and other processes. The SHRM Foundation has identified the increased use of workforce analytics as the future trend for all types of businesses. As Elissa Tucker, Reseach Program Manager of Human Capital Management, APQC rightly puts it:

“It’s no longer a question of ‘if’ companies will utilize predictive analytics for workforce challenges, the question is, when”

 

Read more about the challenges of implementing analytics projects in
Change Management for Effective Data Analytics!

Key applications of predictive workforce analytics

Some key applications of predictive modeling in HR include:

Forecasting recruitment needs

Forecasting recruitment needs optimizes resource utilization and sustains appropriate growth and margins, by predicting requirements for HR capacity. This helps HR managers to develop plans for recruitment, training, and infrastructure development.

Loyalty and attrition analysis of employees

Loyalty and attrition analysis increases employee retention, by calculating an attrition risk score for individual employees, thus helping organizations to prevent the potential attrition of high performing employees and ensure business continuation.

Employee segmentation and profiling

Accurately segmenting and profiling workforce helps in talent management. Organizations can understand the workforce better and take initiatives tailored to fit employee requirements by segmentation of existing employee base.

Suitable recruitment profile selection

High-cost employee attrition leads to significant losses for the organization. The HR decision makers can arrive at the right profile for each potential employee after analyzing the data for current employees, including performance and productivity indices, attrition details, and lifetime value.

Employee sentiments analysis

Employee sentiment analysis is far more effective than the results yielded in annual employee satisfaction surveys because it tracks, and analyzes topics that are most relevant to employee sentiments over a period. The analysis can later be extended to a near real-time process and promote understanding of employees perception of an HR initiative, policy, organizational change, or event. Internal data, as well as external data from social media platforms such as Facebook, Twitter, and LinkedIn can be used for this analysis.

Widely used predictive workforce analytics tools

Companies see analytics tools as the way forward, as they help mitigate potential workforce troubles. Many firms make use of an assessment tool called Predictive Index (PI) that generates the behavioral profile of an employee and provides an accurate depiction of her/his work preferences among others. This helps HR departments predict, plan, justify and formulate decisions to improvise the bottom line.

It is always advisable to act rather than react. Due to predictive workforce analytics tools, organizations can easily read attrition signs in employees and gain full control on retention.

Predict accurately: Retain your employees

It is imperative for the HRs to adopt analytics and predictive techniques to retain its employees, who are an organization’s biggest asset. Predictive analytics helps in two ways. On one hand, it limits HR costs, while on the other hand, it helps develop a high performing workforce.

As a result, in the coming times, it will be of vital importance to base HR decisions on analytics.

“The HR organization in the future will not be about administrative work;  self-service and automation will take care of that. HR will be business partners that consult with the business, all based on analytics. HR will make the link between HR analytics and profitability.”

Lynn Taper, Worldwide Director, Human Resource Operations, Global HR, Colgate-Palmolive

Tripti Rastogi Vishnoi
Tripti Rastogi Vishnoi
About the Author

Tripti leads a team of research analysts at SG Analytics. She has over 7 years experience in supporting clients from financial, corporate and professional services. Before joining SG Analytics, she has been associated with companies such as KPMG, Evalueserve, and Datamonitor. She holds a MBA-degree in Marketing.