Human Resources Analytics: Leveraging Human Resources for Enhancing Business Performance

Abstract

Human resources (HR) analytics enables managers to replace decision-making based on anecdotal experience, hierarchy, and risk avoidance with higher-quality data-driven decisions based on data analysis, prediction, and experimental research. Human Resources analytics underscores the value of HR data by emphasizing how people create value for the organization, so that value can be captured and leveraged. The research discusses transition of HR analytics with a linear three-stage maturity model and explains progression from traditional HR analytics to new age HR analytics. Research provides various key requirements for successful deployment of HR analytics in the organizations. Human Resources analytics focus on various HRM activities including selection, appraisal, compensation, rewards, and development to maximize their impact on organizational performance. Research also provides various illustrations of HR analytics deployment across various industries.

Keywords

human resources analytics big data performance recruitment compensation and rewards employee engagement human resources metrics

Introduction

Organizations today have been experiencing a new set of technologies like Data Analytics, AI (artificial intelligence), Cloud, RPA (robotic process automation), IoT (internet of things), and Blockchain (Madhani, 2022). The rapid advancement of such information technologies (IT) has sparked a digital revolution, with organizations taking advantage of IT for capturing, storing, manipulating, retrieving, and distributing Human Resources (HR) data to address previously unknown opportunities. The deployment of analytics in HR is considered one of the most significant breakthroughs as it allows HR professionals timely access to workforce data that can be used to make more informed and data-driven organizational decision-making. Human resources analytics facilitates the process of transforming workforce data into information and insights as organizations make strategic workforce decisions through its ability to conduct statistical and predictive analysis. Various IT-based tools support HR analytics and include R, Python, Power BI, Tableau, Visier, and SPSS. IT serves as the foundation for HR analytics as it would allow deeper analysis of HR data and hence avoid decisions based only on past experiences and intuition. The global HR analytics market is likely to witness remarkable growth, with a forecasted CAGR of 12.8% between 2019 and 2027 (Credence, 2019).

Human resources analytics can transform data into evidence that is deployed through the organizational capability leading to improved organizational performance. Human resources analytics enables managers to analyze the more complex aspects of the workforce, to make data-driven decisions. Human resources analytics mainly revolve around organizing and utilizing HRM activities (i.e., selection, appraisal, development, compensation, and rewards) to maximize their impact on organizational performance. Compensation is most commonly cited as the HR functional area where HR analytics are most important. As per the “The State of HR Analytics 2021,” survey conducted by Oracle, the highest number of HR professionals (48%) cited compensation as the number one area for the use of HR analytics. HR analytics focuses on understanding, quantifying, managing, and improving the role of HR in the execution of strategy and the creation of value. HR analytics help HR managers to better justify, prioritize, and improve HR investments and thus align them with business performance. HR analytics provide insights to HR managers in recruiting practices and talent-development strategies to address individual performance gaps. According to McKinsey, HR analytics help organizations to increase recruiting efficiency by 80%, decrease up to 50% of attrition rates, and 25% rise in business productivity.

Big Data versus HR Data

Big Data is the massive, real-time structured, semi-structured, and unstructured set of data that are obtained from different as well as unrelated resources. Big Data differs from traditional data sets in terms of its volume, velocity, variety, veracity, and value (i.e., 5 Vs of Big Data). Big Data differs from HR data in terms of volume (the large scale of data), velocity (the need for streaming data analysis), and variety (the use of different forms of data). However, Big Data and HR data are similar in terms of veracity (the quality of data) and value (usefulness of data in creating value) (Table 1).

Table 1.

Big Data versus Human resources (HR) Data: Key Dimensions.

S. No.	Dimensions	Description	Big data versus HR data
1	Volume	It refers to the large quantities of data or the sheer dimensions of the typology of data sets that challenge the storage capacity of the traditional device	1. Big Data is anything too large for typical database tools to be able to capture, store, manage and analyze
1	Volume		2. Human resources data does not satisfy the volume condition because the quantity of HR data is rather small by the standards of large unstructured data but big by the standards of the quantitative data sets used in organizations
2	Velocity	It refers to the rate or speed at which data are generated and analyzed and acted upon and hence reflects data collection, process, and analysis speed in real-time	1. The velocity of Big Data is high compared to HR data
			2. The information about HR data (i.e., the employees, training programs, recruitment, etc.) does not change so fast as to need to analyze the data on-the-fly
			3. There are significant issues of privacy, consent, and ethics to address when storing and analyzing HR data
3	Variety	Variety is related to heterogeneous sources (such as the Internet of Things (IoT), Radio Frequency Identification (RFID) tags, sensors, etc.) as well as diverse formats of data	1. The vast data generated from customers, production, and supply chains are usually variable with a lot of variety due to the diverse sources and heterogeneous formats of Big Data
			2. Although HR data use a wide variety of types of data (e.g., emails, spreadsheets, documents, reports, and assessments), it’s not comparable with the variety of Big Data
			3. Human resources data is static in nature, in comparison with customer and production data
4	Veracity	Veracity (or data integrity) refers to the quality of information rather than quantity and represents the quality of generated data as well as the reliability of data sources. It represents the necessary degree of trustworthiness that the sources of data must possess. Data should be verified to get rid of bad data such as noises, inaccurate attributes, etc.	1. The data (Big Data as well as HR data) used for analytics needs to be accurate, consistent, timely, and complete. Inaccurate data could lead to implementing solutions that do not address the business’s actual challenges
4	Veracity		2. There is a need to educate stakeholders and implement governance programs to clean data and maintain its accuracy and consistency
5	Value	It shows the advantages of data as it is linked to the economic value that may be generated by an organization due to processes and technologies that analyze data	1. Value of data (big Data as well as HR data) focuses on actions, not just findings as it starts with the question of how data can be used to create, capture, leverage, and protect value
5	Value		2. Delivering value means translating information into solutions that stakeholders can act upon

HR Metrics versus HR Analytics

Human resources analytics are separate from HR metrics, which are “measures” of key HRM outcomes. HR analytics, however, is not “measures,” but rather represents approaches that can be used to gauge the impact of HR activities. HR analytics not only focuses on metrics (e.g., what was the cost per hire?) but also analytics (e.g., how to increase retention rate?). HR metrics focus only on what to measure about human capital while HR analytics focuses on how to improve the metrics for enhancing business performance. HR analytics is interdisciplinary and strategic and hence deploys data not only from the HR department but also from other functions inside the organization and even from outside the organization. HR Analytics involves a more sophisticated and in-depth analysis of all relevant data.

Human resources analytics represent statistical techniques and experimental approaches that can be used for linking HR practices to organizational performance and show the impact of HR activities on business performance. HR analytics generate reports on key performance indicators (KPIs), predict short- and long-term workforce trends and their impact on business performance, and build strategic capability for implementing the action plan. With HR analytics, HR departments rely on data to execute activities that were traditionally performed in a somewhat intuitive manner. HR needs a detailed understanding of how the organization currently meets the needs of customers and those areas where there is an opportunity, through its people, to enhance the value the organization is delivering.

Illustration

Human resources metrics report employee churn rate (what happened? i.e., descriptive analytics) and describe why employees leave (Why did this happen? i.e., diagnostic analytics) by using exit surveys or termination reason codes. However, HR analytics enables HR managers to predict which employees are most likely to leave the company (What might happen? i.e., predictive analytics) and then provide data-driven insights to implement the action plan and prevent talent loss (i.e., prescriptive analytics).

HR Analytics: Driving Evidence-Based and Data-Driven Decisions

Human resources analytics enable making better HR decisions by using the best available scientific evidence and organizational facts for “evidence-based HR.” HR analytics requires a high degree of analytical competence, that is, ability to apply statistical analysis and techniques to workforce data to transform data into valuable insights. The analytics team needs to frame relevant research questions and answer them by developing causal models and performing statistical analysis. HR analytics team needs to translate the insights gained into compelling analytics or narrative story. HR analytics should have the required managerial support to make decisions and implement solutions based on the data, information, and insight gathered from HR analytics. HR analytics uses descriptive (for better reporting), visual (for better comparison), and statistical (for enhancing strategic capability) analyses of data related to human capital, HR processes, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making in HR.

Human resources analytics not only focuses on understanding and improving elements of human capital but also on applying analytical techniques to people data to improve organizational performance. HR analytics enables HR managers to understand how workforce data can be transformed into actionable insights for decision-making leading to improved business performance. It is often easier to teach business analytics professionals HR than to teach HR professionals statistics and analytics. The HR professionals lack the skills, knowledge, and insight to ask the right questions about the HR data they have at their disposal. The insufficient data also aggravate the problem to ask the right questions. Royal Dutch Shell, an oil and gas organization, built its HR analytics capability by recruiting employees with significant knowledge of statistics, applied mathematics, psychology, and economics, and who could communicate data with storytelling (van der Togt & Rasmussen, 2017). HR analytics facilitates acquiring and translating high-quality workforce data into information, resulting in critical organizational insights. HR analytics enable data-driven decisions in areas such as recruitment and selection, employee engagement, performance measurement, diversity and inclusion, and workforce planning.

HR Analytics: a Linear Progressive Maturity Model

Human resources Analytics uses descriptive (e.g., visual) as well as statistical (e.g., predictive and prescriptive) analyses of data related to human capital, HR processes, organizational performance, and external performance benchmarks to establish business impact and enable data-driven decision-making. HR Analytics needs to move on from descriptive analytics and build the capability to do strategic analysis.

There are three perspectives of HR analytics:

(1) Descriptive analytics, transform data into meaningful charts and reports with HR metrics (e.g., employee retention rate; hiring costs per employee). It involves using historical data to understand past and current business performance; gain insights and make informed decisions.

(2) Predictive analytics, deploy predictive modeling using statistical and machine learning techniques; analyze historical data and detect patterns or relationships in these data, to predict future scenarios.

(3) Prescriptive analytics is related to causal analysis and seeks to develop answers to the issues being studied through more advanced forms of multivariate modeling. It uses optimization and simulation to identify the best alternative to minimize or maximize some objective.

Human resources Analytics follows a linear three-stage maturity model with Descriptive Analytics at the lowest level and Prescriptive Analytics at the highest level, while Predictive Analytics are placed between these levels (Figure 1).

Figure 1.

Human resources (HR) analytics: a linear three-stage maturity model.

Figure 1 shows a timeline for the evolution of HR analytics from traditional HR analytics (i.e., descriptive analytics) to advanced HR analytics (i.e., prescriptive analytics). Accordingly, HR analytics move beyond descriptive HR metrics (lagging indicators—something that has already occurred e.g., using exit surveys to describe why employees leave) to predictive HR analytics (leading indicators—something that may occur in the future e.g., predict which employees are most likely to leave the company?) and then prescriptive HR analytics (how to implement action plan) to provide directions on how to manage and improve the metrics that contribute to business success. Thus, HR analytics is the systematic identification and quantification of the people drivers of business outcomes.

Descriptive analytics is the most commonly used by organizations as traditional HR analytics focus mainly on HR Metrics. Too often, organizations focus on inputs (such as hours of training completed) rather than outputs and results (such as improvements in workforce performance as a result of training). As per the recent survey “The State of HR Analytics 2021,” conducted by Oracle, the majority of organizations (68%) make at least moderate use of descriptive analytics while only 15% of organizations make high or very high use of prescriptive analytics. A study found that organizations are using analytics for routine tasks such as aggregating and cleansing HR data (45%) and headcount cost analysis (41%) (OrgVue, 2019). A Harvard Business Review analytic study found that more than half use data only on an ad hoc basis or, use no data in workforce decisions making while less than 10% use predictive analytics to inform workforce decisions (HBR, 2016).

Traditional HR Analytics versus Advanced HR Analytics

Traditional HR analytics is often preoccupied with efficiency (i.e., “doing things right”) with an “inside-out” perspective (e.g., do HR use the right recruitment channel? What is the ROI of training programs? How efficient is the onboarding program?). Efficiency measures alone fail to reflect the effectiveness or strategic impact of HR activities. Only efficiency outcomes (e.g., cost savings in the recruitment process) are unlikely to result in business impact. However, it may create more value when HR analytics applies an effectiveness (“does the right things”) approach with an “outside-in” perspective (How does HR help transform the organization’s culture to better deal with market consolidation and expected acquisitions? How can HR grow critical technical talent faster, cheaper, and better than the market to realize organizational growth strategy in a booming market and gain differentiation advantages?).

Traditional HR analytics applies an “inside-out” perspective while advanced HR analytics applies an “outside-in” perspective (Figure 2). HR analytics must transcend HR issues and become part of existing cross-functional business analytics, as all functions must transcend their functional areas to align analytics with business performance. Advanced HR analytics help HR managers ensure they have the right people in the right roles, to solve the most important organizational challenges. Advanced HR analytics integrates with other departments' teams (marketing, finance, operations, etc.), to become part of cross-functional/end-to-end business analytics to discover the roles of human capital elements in the entire value chain (Figure 2). Advanced HR analytics typically yields truly new insights as it combines multiple functional perspectives (customers, technology, safety, investor perspective, etc.).

Figure 2.

Traditional HR analytics versus advanced HR analytics.

Illustration: Traditional HR Analytics in the Retail Sector

Major retailers use workforce planning software to plan optimum staffing levels in their stores. Such software typically assumes human capital (e.g., retail workforce) is a cost to be minimized (Ton, 2009) and hence it typically led retailers to reduce staffing levels, as stores with higher labor costs are depicted by the modeling as negatively impacting store profitability. However, the assumption in the algorithm that labor or retail workforce was a cost to be controlled fails to take into account that the quality of workforce input has a bearing on the performance outcomes of retailers.

Traditional HR Analytics has a functional orientation and hence mainly focuses on HR metrics. Reducing staffing costs by employing fewer retail associates can also drive down the quality of labor input as associates are spread more thinly, and thus do not have the time to ensure that product displays are effectively organized, the stock is kept moving onto shelves, or that customers received help when requested. There is a positive association between store workforce and profits as store employees are an important contributor to the retail sales process. If the workforce or labor is modeled as a fixed cost that needs to be controlled, it neglects flexibility attributes of human capital as its productivity and performance change with skills and motivation levels (Madhani, 2019). Prior research found that increasing staffing levels increased retail stores’ profitability because the boost to sales from higher-quality labor inputs was greater than the additional labor costs (Ton, 2009; Madhani, 2021). Traditional HR analytics software does not understand the context-specific causality of each organization while HR professionals do not have the knowledge and skills to adapt their environment to the standard model and algorithm proposed in the software. This is the reason why traditional HR analytics failed in the retail sector.

HR Analytics: Successful Deployment in Various Industries

Organizations use HR analytics to segment the workforce and use statistical analyses and predictive modeling to identify important drivers (i.e., factors and variables) and cause-and-effect relationships that enable and inhibit key business outcomes. Thus, HR managers will forecast and extrapolate “what-if’” scenarios for decision-making. HR analytics links the impact of HR practices and policies on organizational performance. Table 2, shows business applications of HR analytics across diverse industries.

Table 2.

Deployment of HR Analytics across Various Industries for Enhancing Business Performance.

S. No.	Organization	Industry	Business issues	Performance improvement on leveraging HR
1)	Convergys	HR services	1) High employee attrition	1) Employee attrition was reduced by 58,000 over 4 years
1)	Convergys	HR services	1) High employee attrition	2) Saving of $57 million on recruiting and training costs
2)	Maersk drilling	Oil and gas drilling industry	2) Performance variation	3) Decrease in performance variation
			3) Skill shortages	4) Decrease in development cost ($967,500 per trainee)
			4) High turnover	5) Increase in retention
3)	Sysco	Foodservice industry	1) Improving work environment	1) Work environment improved
			2) Enhancing employees’ satisfaction	2) $50 million saving in hiring and training costs
			3) Retaining employees	3) Increased retention rate to 85% from 65%
4)	McDonald’s UK	Fast-food chain	1) Improve business performance	1) Employee engagement increased
4)	McDonald’s UK	Fast-food chain	2) Increase customer orientation	2) Restaurant performance improved
5)	Inditex	Fashion retail industry	1) Improve business performance	1) Evidence-based informed decisions for better workforce management
5)	Inditex	Fashion retail industry	1) Improve business performance	2) Higher overall store performance
6)	Lowe’s	Home improvement retail industry	1) Improve business performance	1) Employee engagement increased
6)	Lowe’s	Home improvement retail industry	1) Improve business performance	2) Store performance improved
7)	Bank of America	Financial services industry	1) Enhancing employee performance	1) Team cohesion increased by 18%
				2) A reduction in stress by 19%
				3) Productivity increased by 23%
8)	Google	Internet and information services industry	1) Make the recruitment process effective	1) Improved hiring process
8)	Google	Internet and information services industry	2) Improve the performance of managers	2) Improvement in 75% of low-performing managers

Human resources analytics has proven popular among organizations to help understand, track, and control high-cost areas of HR as discussed in the following illustrations:

Convergys

Convergys, an HR service company that manages billing, compensation, and benefits administration for businesses in 40 countries, faced the biggest employee issue in terms of high employee attrition. To get more insight into the issue, the company adopted a survey-based statistical technique “conjoint analysis” (usually used for consumer marketing to determine how consumers value different combinations of product features) to predict employee preferences and future behavior in the workplace and tailored HR actions accordingly in efforts to support employee retention. When used with employees, the conjoint analysis helped HR managers to figure out a customized mix of benefits (flexible scheduling, tuition aid, employee recognition plan, etc.) most likely to encourage employees to stay with the organization. Consequently, managers were able to determine customized benefits plans for their employees by region. Despite being a considerable investment, the custom benefits adjustments were made possible at a low cost to the company. Convergys deployed HR analytics to “calculate what employees value most” and generated a plan to reduce attrition within the company with significant cost savings. As a result, Convergys estimated its attrition was reduced by 58,000 over four years and saved $57 million on recruiting and training costs (Harris, Creig, & Light, 2011).

Maersk Drilling—Oil and Gas Drilling Industry

Maersk Drilling, headquartered in Denmark; is a leading offshore drilling company demerged from the A.P. Moller–Maersk Group. Maersk Drilling deployed HR analytics to link HR activities to business performance (Rasmussen & Ulrich, 2015). Maersk Drilling faced the challenge of growing 40% within four years while managing drilling rigs’ performance. With HR Analytics, HR professionals at Maersk Drilling tackled various business issues such as:

(1) High turnover—impacting the business growth

(2) Skill shortages—require more employee training to improve business performance

(3) High-performance variation—requires analysis of leadership in different business units

Maersk Drilling: Managing Offshore Drilling Rigs Performance

At Maersk Drilling, HR analytics was successfully integrated with a business performance by using rich experience from the drilling business, and offshore drilling managers' intuition about what drives offshore drilling performance.

With help of HR analytics, the HR department was interested in identifying:

(1) How is the performance between similar drilling rigs operating under similar conditions compared? (descriptive analytics).

(2) What explains the variance in operating performance between rigs? (diagnostic analytics)

(3) How can that existing knowledge and expertise effectively be deployed to new rigs brought into operation? (prescriptive analytics), and

(4) How can the operating results be used to help convince prospective clients that the company will deliver on promised performance standards while growing considerably in a hot market? (predictive analytics)

Maersk Drilling demonstrated with qualitative and quantitative measures of HR analytics, the impact of the offshore drilling team’s performance on the performance of the organization by establishing clear links among aspects of employee engagement, safety, and performance.

By using HR Analytics, Maersk Drilling found strong and significant links between leadership quality (measured via a yearly people survey), crew competence (documented according to the industry standards and requirements), safety performance (from the company’s safety system), and environmental performance (documented in the company’s health, safety, and environment (HSE) system according to the offshore industry standards). Maersk Drilling established a relationship between leadership quality and lower turnover levels, which resulted in higher levels of operator competence which in turn fed through to fewer accidents, less maintenance time, and higher customer satisfaction.

At Maersk Drilling, customer satisfaction was found to depend on the operational performance of business units (i.e., drilling performance/uptime). However, other factors also matter for company success: managers assessed more positively (on various standard leadership tasks) by their subordinates or direct reports have lower crew turnover, and lower turnover is associated with higher crew competence (fewer new people to train), which in turn is related to better safety performance, fewer spills, and fewer maintenance hours outstanding (i.e., the time it takes to fix stuff) which impacts customer satisfaction.

Leadership (or managerial) quality has a positive impact on retention (i.e., employee turnover). Turnover has a negative impact on crew competence. Crew competence has a positive impact on safety as it reduces spills. Also, spills (poor safety performance) have a negative impact on customer satisfaction. Operational performance (measured in uptime) has a direct positive impact on customer satisfaction. Thus, as the final outcome leadership quality also has a positive impact on customer satisfaction as shown in the diagram (Figure 3).

Figure 3.

Human resources analytics: leveraging human resources for higher business performance.

HR analytics creates linkage by using statistics and research methodology to generate new insights and translate these into recommendations. The findings of the analytics project were integrated into an end-to-end value chain analysis and compiled into overall firm performance. Based on the findings of the HR analytics project, the following action plan was recommended:

(1) Enhancing leadership quality with proper training and an effective selection process,

(2) Building crew competence with proper training budget and controls,

(3) Using scorecards for maintenance hours outstanding across the fleet

(4) Effectively communicating the findings throughout the company to all managers and employees and existing and prospective clients.

Maersk Drilling: Trainee Acceleration Program for Lead Specialist Positions

Maersk Drilling faced challenges in filling lead specialist positions due to talent shortage in the job market and high industry growth. The company initiated a strategic initiative program to develop a technical talent pool for the senior specialist target positions. HR analytics was used to identify that the “company graduate program (for Specialist Trainees)” experimented by Maersk Drilling, showed desirable results on key business outcomes compared with their peer group. The strategic impact of the technical trainee acceleration program was positive as it increased the retention rate, reduced the overall time required to develop into a lead specialist, and reduced total cost prior to the Lead Specialist position ($967,500 per trainee) (Table 3). Based on these findings enabled by HR analytics, Maersk Drilling decided to double the “company graduate program” intake to sustain its future growth plans.

Table 3.

Trainee Acceleration Program: Improvement of KPI.

The outcome of the specialist trainee program at Maersk drilling (Compared to peer group)
Sr. No.	Key performance indicator (KPI)	Specialist trainees program	Peer group	Difference (Positive impact)	Improvement (%)
1)	Retention rate (%) (After 5 years)	63	60	3 (Better retention)	5
2)	Time to develop into a specialist from assistant specialist (Years)	2.7	3.8	1.1 (Less development time)	29
3)	Time to develop into a lead specialist from a specialist (Years)	3.9	6.4	2.5 (Less development time)	39
4)	Total time to develop into lead specialist (Years)	6.6	10.2	3.6 (Less development time)	35
5)	Total cost per person - prior to lead specialist position (US $)	1,882,500	2,850,000	967,500 (Huge savings per trainee)	34
6)	Average performance in lead specialist position	3.5	3.2	0.3 (Better performance)	6

With HR analytics, Maersk Drilling identified the critical role of off-shore drilling managers in managing their teams. Managers who received more positive evaluations from their employees managed off-shore drilling teams more effectively with lower levels of turnover and absenteeism. Such better-trained teams operated their drilling rigs more efficiently with fewer accidents and maintenance time. The analysis led Maersk to focus more attention on the selection, training, and development of its key managerial talent. HR analytics was treated like a change management process that paved the way for the results to have a positive business impact.

Sysco—Foodservice Industry

Sysco (an acronym for Systems and Services Company) is the global leader in selling, marketing, and distributing food products to restaurants, healthcare, educational facilities, and lodging establishments. It has 350 autonomous operating units and 65,000 full-time employees serving 650,000 customers. The company’s customers include restaurants, nursing homes, hospitals, hotels, motels, schools, colleges, cruise ships, sports parks, and summer camps. The key to Sysco’s success is strong relationships between Sysco delivery people and the customers they serve. Thus, HR’s ability to retain skilled, satisfied employees for the delivery function is critical to the company’s business results.

Sysco uses HR Analytics to establish causal links between work climate surveys, delivery employee satisfaction, customer loyalty, and revenue. Sysco tracks three HR metrics: work climate and employee satisfaction, productivity (measured as employees per 100,000 cases of food sold), and retention. By measuring and analyzing these key metrics, Sysco was able to link effective HRM practices to business results (Harris, Craig, & Light, 2011). With HR Analytics, Sysco found that operating units (or companies) with highly satisfied employees have higher revenues, lower costs, superior customer loyalty, and increased employee retention. Sysco identified underperforming units and provided support to improve their performance. Sysco built and developed a best-practices database to provide directions to the operating companies. To maintain high levels of employee satisfaction, Sysco measures and manages seven dimensions of the work environment:

(1) Rewards for performance,

(2) Leadership support of employees,

(3) The effectiveness of front-line supervisors,

(4) Quality of life,

(5) Employee engagement,

(6) Diversity and

(7) Customer focus.

With HR Analytics, Sysco connected HRM processes to employee attitudes and behaviors and ultimately to organizational outcomes. Sysco realized that with improvements in employee satisfaction, customer satisfaction, and loyalty also increased resulting in higher revenue. At the same time, costs associated with employee turnover are reduced significantly. By measuring and managing the link between HR processes and business results, Sysco improved the retention rate for delivery associates from 65% to 85% in 6 years. A higher retention rate also resulted in the saving of hiring and training costs of associates to the tune of $50 million in the process (Cascio, 2005).

McDonald’s UK—Fast Food Chain

McDonald’s is a multinational fast-food chain operating in an environment where demand is driven by demographics, consumer tastes, and personal income, and hence its profitability depended on efficient operations and high-volume sales. McDonald’s UK has built HR analytics capability to apply service-profit chain (Heskett, Jones, Loveman, Sasser, & Schlesinger, 1994; Madhani, 2019) thinking in the more transactional service environment of fast food. McDonald’s UK identified relationships among management behaviors, staff demographics, and employee attitudes to optimize restaurant performance. It also found that the demographic mix of their teams changed the store dynamics and ultimately the customer experience. By harnessing the power of HR analytics, it deployed its successful employee engagement strategy as engaged staff creates a simple, easy, and enjoyable restaurant experience. McDonald’s UK found that where the staff was engaged, customer visits were 66% higher and sales 28% higher (Sparrow, Hird, & Cooper, 2015).

Inditex—Fashion Retail Industry

Inditex is a large Spanish multinational fashion retail group and also one of the world’s largest fashion retailers. It has developed and implemented an HR analytics program to improve business performance. For the effective deployment of HR analytics, the company hired HR analysts with a background in engineering and quantitative analysis. Such initiatives helped Inditex, in effectively collecting, analyzing, and reporting data linked to key business performance indicators. As a result, the insights gathered by the HR Analysts allow HR managers at Inditex to effectively monitor and make more evidence-based informed decisions around their workforce. Thus, with HR analytics, Inditex achieved higher overall store performance (Simon & Ferreiro, 2018).

Lowe’s—Home Improvement Retail Industry

Lowe’s, a home improvement retail chain, used HR Analytics to establish a link between HR processes, employee engagement, and store performance. HR Analytics team went to great lengths to build trust and buy-in for their HR Analytics project from senior managers and those outside the HR function. Through the use of HR Analytics, Lowe’s found that highly engaged employees lead to 4% higher average customer ticket sales per store (Coco, Jamison, & Black, 2011).

Bank of America—Financial Services Industry

Bank of America, has begun to analyze data from various mediums such as cell phones, emails, social media, and motion sensors to aid in making data-driven workforce decisions. Bank of America, in collaboration with Humanyze (an HR analytics software provider), used HR analytics to improve HR and business outcomes. Humanyze designed and developed ID badges for Bank of America employees, adding microphones, Bluetooth, and infrared technology to facilitate workforce data collection. Their findings determined that how employees interacted with their coworkers was the most significant factor in predicting productivity. Based on this evidence, Bank of America implemented solutions to the working environment that led to increased team cohesion by 18%, a reduction in stress by 19%, and a 23% increase in productivity (Kane, 2015).

Google—Internet and Information Services Industry

Google is an American multinational technology company that focuses on search engine technology, artificial intelligence, and online advertising. Google has a highly analytical culture and practices to support HR analytics in the organization. Google has deployed HR analytics to systematically and rigorously identify critical talents such as key recruiting targets, high-potential employees, and top performers. Google is well-known for its tough screening process of new job candidates. But as the company grows, it has become ever more challenging to find enough people who are likely to perform well at Google. A few years ago, as it waded through some 100,000 applications per month, Google was not sure it was identifying the best candidates with traditional hiring and recruitment techniques such as grade point average (GPA) to screen for attractive candidates. The company’s initial analysis of two million data points from the survey confirmed that Google’s reliance on GPA as a simple screening metric had indeed caused them to overlook great candidates in the past. The company found that it needed a more effective way to manage the task of identifying those people who would be successful employees (Hansell, 2007).

Google has created an HR analytics group (i.e., The People and Innovation Lab or PiLab) that studies employee-related decisions and issues to gain new insights and identify best HR practices. PiLab has determined what backgrounds and capabilities are associated with high performance and what factors are likely to lead to attrition. The PiLab team analyzed annual employee surveys, performance management scores, and other data to divide managers into four groups according to their quality. It then interviewed high- and low-scoring managers to determine their managerial practices. Google’s Project Oxygen was established to determine the attributes of successful managers (Shrivastava, Nagdev, & Rajesh, 2018). This insight has been incorporated into the managerial training and development programs at Google and has become a part of the day-to-day working style of managers. With HR analytics, Google was eventually able to identify eight behaviors that characterized good managers and five behaviors that all managers should avoid. A year after the HR analytics team shared its findings, Google found significant improvement in 75% of low-performing managers (Davenport & Harris, 2017).

Google created a comprehensive database that captures and stores employees’ attitudes, behaviors, personalities, and biographical information, as well as job performance. Google collects the same information from potential job candidates. Google uses HR Analytics to predict employee performance using its large database. By using HR analytics, the data for individual job seekers are matched against a list of the best predictors of performance generated from the current employees’ data. Google then applies an algorithm that calculates a score to predict the likelihood that applicants will succeed at the company. HR analytics has helped Google manage the rapid growth of its workforce. It also ensured that the company does not overlook potential employees who may not have made it through the previous process. Google’s HR analytics team has developed an evidence-based data-driven approach to improve its recruitment and selection process. Google identified several attributes of high performers that could predict a candidate’s likelihood of success and accordingly screened such applicants for recruitment and thus helping Google to find and hire the talent it needs (Hansell, 2007).

HR Analytics: Major Challenges and Key Requirements for Successful Deployment

Human resources costs or the cost of the workforce can amount to nearly 40–70% of operating expenses. Although HR costs may vary, they remain the single biggest organizational expense. Given their fluid and rapidly changing nature, HR costs are extremely difficult to manage and control. Although deploying HR analytics, it is necessary to focus on high-cost areas of HR (e.g., recruitment, employee turnover, and employee engagement challenges) to identify where there may be room for savings and then perform a cost–benefit analysis to select HR projects for improvement. A study undertaken by Deloitte found that although 75% of surveyed companies believed that using HR analytics is important for business performance, only 8% viewed their organizational capabilities in this area as “strong” (Deloitte, 2015). Hence, organizations need to build capabilities for HR analytics.

Key Requirements

The following are key requirements for the successful deployment of HR analytics:

(1) The HR department should have the right knowledge and skills to collect the correct data, perform the right statistical analyses, and then communicate the results in a meaningful way. HR does not understand analytics or data science, while analytics teams do not understand HR. As a result, the analytics capabilities provided by the HR analytics software are failing to deliver strategic HR capabilities.

(2) The basic skills required for the successful deployment of HR analytics are data analyses, multivariate models, root cause analysis, research design, survey design, and quantitative data collection and analysis.

(3) Analytics should start with business challenges and not with the data that is readily available to yield new insights. When HR analytics required data that are stored in multiple silos unconnected to each other, it creates challenges in combining data possessed by disparate parts of the organization. Some of the inefficiencies related to it can be corrected by creating and maintaining integrated database systems.

(4) HR professionals need to gain access to the organizational cross-functional data (spread across functions, geographies, or divisions) to perform their analyses, and other functions must be willing to provide access and also be involved in the process.

(5) HR Analytics team must involve key stakeholders in the process ahead of conducting the analyses to get acceptance of the outcome. Silo mentalities within organizations prevent HR-related data from being combined with data on other determinants of business performance.

(6) HR Analytics implementation involves acknowledging the role of resistance to change and resistance to abandoning the predominant role of intuition in managerial decision-making instead of data-driven decisions.

Major Challenges

The following are key challenges in the successful deployment of HR analytics:

(1) There are various challenges in collecting required data for HR analytics as it requires training in areas of data gathering, reporting, analytical tools, analytical thinking, business analytics and statistics, effective communication, business and financial acumen, tactical and strategic planning, and storytelling.

(2) It is often easier to teach business analytics professionals HR than to teach HR professionals statistics and analytics. The HR professionals lack the skills, knowledge, and insight to ask the right questions about the HR data they have at their disposal. The insufficient data also aggravate the problem to ask the right questions.

(3) Human resources analytics requires professionals with IT acumen (how to use analytics software tools) and financial skills (how to access and use measures of business results).

(4) A successful HR analytics program requires senior executive support, IT resources and technical support, and a strong, business-focused leader.

Conclusion

Human resources analytics enables managers to replace decision-making based on anecdotal experience, hierarchy, and risk avoidance with higher-quality data-driven decisions based on data analysis, prediction, and experimental research. HR analytics can help organizations in areas of industry analysis, workforce planning, job analysis, recruitment and selection, training and development, compensation and benefits, and performance management. HR analytics underscores the value of HR data by emphasizing how people create value for the organization, so that value can be captured and leveraged. Due to the sensitive nature of HR data, organizations need to become more concerned about data confidentiality, local regulations regarding the use of employee data, and the risk of public disclosure. The research discusses the transition of HR analytics with a linear three-stage maturity model and explains the progression from traditional HR analytics to advanced HR analytics.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Pankaj M. Madhani

Author Biography

Pankaj M. Madhani earned bachelor’s degrees in chemical engineering and law, and an MBA from Northern Illinois University, a master’s degree in computer science from the Illinois Institute of Technology in Chicago and a Ph.D. in management from CEPT University. He has more than 34 years of corporate and academic experience in India and the United States. During his tenure in the corporate sector, he was recognized with the “Outstanding Young Managers Award”. Madhani worked as Dean (academics) and Professor at ICFAI Business School (IBS) Hyderabad (AACSB Accredited), ICFAI Foundation for Higher Education (IFHE) University, Hyderabad, Telangana, India. Since, October 2022, he is working as a management consultant. He has also received the “IBSAF Best Teacher Award” twice (2013 and 2017). Madhani received the “Best Research Paper Award” at the 2016 International Management Convention. He is conferred as the ‘Best Professor in Strategy and Finance’ by ‘AMP Academic Excellence Award 2019’ for his academic and research contributions. He also served as editor of the IUP Journal of Corporate Governance. His main research areas include compensation, corporate governance and business strategy. He is a frequent contributor to “Compensation and Benefits Review” and has published more than 26 articles on compensation strategy.

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