How Machine Learning Improves Efficiency and Management of Workforce

Imran Ahmed

April 28, 2020

At a time when businesses are experiencing unimaginable disruptions, demands and priorities are continuing to shift. While maintaining functionality and financial health remains at the forefront of concerns, workforce management is an essential piece of successful business operation that must continue to be tended to despite the current climate business leaders are facing.

Companies can look to technology, such as Machine Learning and Artificial Intelligence to help streamline their workforce management processes while leveraging insights to generate stronger HR mechanisms. When pairing immense, empirical data with the power of Machine Learning and Artificial Intelligence, the possibilities are endless for better managing a global workforce—in any situation.

ADP ’s Machine Learning Models Help With Hiring

For many employers, analyzing their own workforce data isn’t enough for a predictive model. ADP DataCloud closes this gap, utilizing 30-million plus employee data points to provide a more holistic view for all employers and tapping into Machine Learning to spot historical trends. ADP DataCloud’s predictive analysis helps employers understand which candidates are most likely to stay with the company. Hiring top talent may be attractive, but not if it increases turnover.

Data can also help identify which skills are most applicable to the position for which you’re hiring. These include declared skills, such as Email Marketing, Content Strategy, and Analytics, as well as implied skills inferred from that position, including leadership skills and lead generation skills.

Further, beyond identifying strong and suitable talent, ADP DataCloud leverages Machine Learning to streamline information across industries to help inform employee contract negotiations. Oftentimes, candidate-facing solutions can be outdated and inaccurate with regard to compensation. ADP’s modeling uses anonymized and aggregated real-time data from thousands of employers to surface accurate salary and benefits data. With more accurate, contextual data, new-hire negotiations become faster and more equitable for both employer and potential employee.

Identifying Which Compensation Package to Offer

Desired compensation can change from role to industry. An engineer working in the manufacturing industry may expect a different package than an engineer in the technology industry. ADP leverages real-time data to helps employers know what industry-standard packages are for unique positions, nationwide and regionally.

Data from thousands of employers help business leaders to frame what’s appropriate for their own workforce and improve existing packages where necessary. Further, a closer examination of real-time trends allows companies to improve stock options and get creative with packages/vesting options.

Hiring and Retaining Talent Without Compromise

In addition to negotiating pay at hiring, ADP’s data can further be leveraged to help understand where pay needs to be shifted. Machine learning models guided by millions of data-points help companies stay competitive across roles, experience levels, and industries. While strong compensation packages are attractive to candidates, demonstrating renegotiation can appeal to internal staff. Optimizing compensation packages routinely can help entice longevity and strengthen retention.

Machine Learning feeds AI recommendations, which help large companies act fast based on algorithmic recommendations. Existing dashboards within ADP’s DataCloud solution allow decisionmakers access to accurate data in real-time in a digestible, understandable format. Data mashups allow cross-referencing of data; comparing sales trends to per-employee benefits spending, for instance. These tools directly feed into tough decisions that need to be made, not only aiding efficiency but also improving quality as decisions are informed and backed by strong data.

For example, identifying behaviors among existing employees related to absence and tardiness can help HR departments prepare to hire for a role where an employee may be exiting.

Customized Data Points Lead to Predictive Analysis at Scale

While data aggregation can sometimes be overcomplicated as not all businesses operate on the same level and not every decision can be determined by empirical data, simpler tasks like tracking overall employee tardiness can be customized at a company level. This granular customization allows businesses to make decisions more contextual to their workforce, and personalized data can be compared against the larger dataset to identify whether a company and staff are over or underachieving in various areas.

Workforce management remains and will continue to remain an area of business that requires a level of attention. Neglecting one’s workforce to prioritize larger business needs can jeopardize the future of the company. As workforce management continues to be a daunting and time-consuming task for some organizations, leveraging Machine Learning and Artificial Intelligence can prove helpful in streamlining and informing human resource operations.

ADP’s DataCloud solution taps into these advanced technology tools help companies identify the right candidates and make them the best competitive offer possible, while simultaneously providing customizable solutions to help companies make the right decision for their needs.

The fact of the matter is, the ability to measure industry-wide data and employment trends—alongside company sales or production goals—ensures companies make the right decisions early and often; and ultimately remain competitive in the hiring and retention landscape.