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Human Resource and People Analytics

Human capital is one of the largest driving forces of success within any company. Undoubtedly, organizations that strategically attract talent, train their people, manage capacity efficiently and effectively and retain their employees, are at a sizeable advantage for the long term.

 

HR departments are generating increasing amounts of data within their workforce, but many have failed to realize the true value of these insights. HR professionals are turning to analytics to understand which candidates to hire, which employees are currently succeeding in their roles, and how to best compensate employees to maintain the company’s morale and increase employee retention. It ideally is used in unison with intuition and instinct to make decisions.

 

Key performance indicators you should be tracking [i], but not limited to:

Workforce Demographic

  • # of full time employee

  • # of part time employee

  • Total workhours

  • Average full (part) time wage

 

Hiring

  • Application received

  • Actual vs budgeted cost of hire

  • New hire quality

  • Time to fill

  • Qualified applicant pool

  • Part time vs full time hires

 

Performance

  • Revenue/Employee

  • # of sick days

  • # of internal/external complaints received

  • Estimated sick leave cost

  • Total absenteeism rate

 

Training

  • Average headcount/HR employee

  • Time to competence

  • Employee satisfaction with training

  • Training to work hour ratio

 

Turnover and Retention 

  • Annualized voluntary/involuntary employee turnover rate

  • Average headcount/manager

  • # of internal social events

  • Overtime hours as a percentage of all work hours

 

Health and Safety

  • Number of incidents requiring first aid

  • Total incident rate

  • Number of incidents requiring lost time claims

 

Outside of these KPI’s, HR managers have used more complex analytical methods to derive insights. They have used predictive analysis to predict certain outcomes by using tools such as neural nets, and regression models. For example, companies may use such analysis to predict underperforming sales reps within their sales force. It is also used to forecast an issue prior to its occurrence, allowing for capital to be more efficiently allocated on training or reallocating employees and saving the firm money by preventing a negative outcome.

 

Dashboards are also frequently used as they are an excellent visualization tool that allows HR managers to understand the issues that matter most to their organizations. Senior executives will be able to identify concerning trend deviations in KPI’s such as attrition rates, employee costs, and to better understand their employees through employee engagement profiles. 

Analytical tools are being leveraged to assess qualitative entities such as Corporate Culture[ii]. Given the intangible nature of culture, it is often difficult to assess whether candidates and current employees fit within the management culture.

 

Analytics can be used to better understand corporate culture and if there are any deviations from it. By combining the qualitative nature of traditional HR analysis, such as onboarding surveys for potential employees, culture surveys for current employees, and exit interviews [iii] with former employees, with quantitative methods, organizations can understand their current cultural situation as well as the drivers of cultural satisfaction. This, combined with visualization tools such as dashboards, allows HR managers and professionals to ensure a cohesive, productive, and satisfied workforce.

Industry Example

Hewlett-Packard (HP), a company employing over 300,000 employees, has always been at the forefront of predictive analytics. They started to utilize analytics within their HR division to address turnover[iv], and found out that their 20% internal turnover rate can potentially lead to a replacement cost of over 150% of the current employees’ annual salaries due to lost productivity and training. For a company of HP’s size, any reduction in turnover would lead to millions of dollars in annual savings.

 

HP let two of their data scientist create a predictive model that would generate a “Flight Risk” score for each employee, a score that predicts the likelihood of a certain employee’s leaving. They discovered intricate relationships in their findings, such as someone who received a promotion that was not accompanied with a substantial raise was more likely to quit than someone who did not receive a promotion.

 

Only senior managers were given access to the data due to practical and privacy-related concerns, and they received training on interpreting Flight Risk scores and dealing with the implications.

The combination of being aware of the employees who are at high risk of attrition and understanding the leading drivers of turnover leads to sizeable improvements in HP’s HR decision making. Managers were able to intervene to prevent attrition or at least have a mitigation strategy if turnover was unavoidable. This led to over $300 million in savings for the organization, along with improved employee morale.

 

The typical journey for an team to adopt data and analytics-driven insights looks like the following. There are two ways to view analytics services, one way to is to look at the technicality of the quantitative analysis. The other is to look at the dimensions and specific use cases that are challenge focused. See the two dimensions below.

 

 

[i] KPI Examples. Scoreboard. Retrieved from here

[ii]The 8 HR Analytisc Every Manager Should Know About. Forbes. Retrieved from here

[iii]Culture Analytics. CultureFactors. Retrieved from here

[iv] Predictive Analytics in Human Resources: Tutorial and 7 case. AnalyticsinHR. Retrieved from here