2.9 HR Analytics Application
We have discussed the exponential increase of HR data in organizations. In this section, we will tackle what to do with this data. Basically, HR analytics is the process of analyzing and using data to make informed business decisions. For example, talent acquisition managers will tell you that the most challenging part of the recruitment process is determining which applicants will make the best hires.
Analytics can help you determine which qualities are most important for a certain position, then sift through applications and find the candidates that best match those qualities. Analytics can also tell you when a certain quality or data point actually has little to do with an employee’s success. For example, imagine if your company found out, after analyzing the profile of your top salespeople, that college grade point average is not a strong indicator of future sales performance. You can now expand your recruitment pool to anyone with a university degree instead of restricting it to those with high GPA’s!
Job Design
Job design helps to structure jobs to make them more motivating and to increase the performance of employees. Data, and more specifically data analytics, can allow HR Managers to pinpoint areas that facilitate or impede motivation or performance.
Recruitment
The objective of recruitment is to generate the maximum number of quality applications possible; recruitment analytics borrows heavily from marketing science. Recruiters that use analytical tools rely on segmentation, statistical analysis, and the development of optimal people models (i.e., ideal candidates). Since an increasing majority of recruitment occurs electronically, there is a vast amount of data available to recruiters to seek to optimize their processes.
An example of this is the segmentation in job advertising and the deployment of programmatic advertisement. In programmatic advertisements, target groups for a job opening are defined and then targeted through multiple online sources. In this case, the ad spent (per click or per thousand impressions) is closely monitored, and when needed, adjusted. Because of the segmentation, different advertisements can be tested against different job-seeker segments in an effort to optimize conversion and lower cost.
The virtual nature of recruitment also allows for very innovative and ‘out-of-the-box’ recruiting techniques. For example, in the world of motorsport, for example, Nissan is recruiting through an unusual channel: racing video games! Nissan joined forces with Sony to create the GT Academy, a global annual contest designed to find the best gaming racers and turn them into real-life racing drivers (Richards, 2014). Hundreds of thousands of hopefuls now enter the contest each year. And all of the winners selected in the past few years are still racing, proving what a useful recruitment channel this has been for Nissan.
Talent Acquisition
The objective of talent acquisition is to find the best employee for a specific job. This can be a daunting task. People are complex and evaluating them is fraught with obstacles such as biases. HR analytics allows the HR department to cut through this complexity. For example, HR managers can analyze the profiles of their top performers, identify their characteristics (e.g., they have MBA’s, were involved in high-level athletics, or are introverts) and align their staffing processes accordingly.
Training
With the rise of online learning, corporate learning and development is becoming increasingly personalized to individual learners. Fuelled by data and analytics, ‘adaptive’ learning technology allows courses, course segments, activities, and test questions to be personalized to suit the learner’s preference, in terms of pace and method of learning. Individual, self-paced online learning is also arguably more cost effective than pulling employees out of their job for a day or week to send them on expensive training courses. Importantly, self-directed learning helps integrate ongoing development into employees’ everyday routines.
Compensation
Whether it is managing job candidate salary expectations or looking for evidence of pay equity, data allows HR managers to make decisions based on facts. For example, when an employee receives a competing offer, their manager’s first instinct may be to match it. The key word here is instinct, which can lead to costly mistakes: intuition can cause even the best managers and HR professionals to make poor judgment calls. The way to mitigate this risk is to look to the data: to find out how the employee compares to the rest of their team and what the market is paying for a similar role.
Performance Management
UPS has taken the use of data and analytics in performance management to an entirely new level. For example, the handheld computer that drivers have been carrying for years (those electronic boxes you sign to say you received your parcel) is actually a sophisticated device that helps drivers make better decisions, such as which order to deliver parcels in for the most efficient route. In addition, UPS trucks are fitted with more than 200 sensors that gather data on everything from whether the driver is wearing a seatbelt to how many times the driver has to reverse or make a U-turn.
By monitoring their drivers and providing feedback and training where needed, UPS has achieved a reduction of 8.5 million gallons of fuel and 85 million miles per year (Dix, 2014). Plus, drivers now make an average of 120 stops a day. That number used to be less than 100 – meaning the same drivers with the same trucks are now able to deliver more packages than before. One might think that monitoring employees so closely might cause a backlash among staff. But enhanced performance means the company can pay its drivers some of the highest wages in the industry, which no doubt helps support employee buy-in for data-driven performance. The company has also taken other steps to ensure they don’t face a driver backlash; for example, under the terms of drivers’ contracts, UPS cannot collect data without informing drivers of what they’re gathering. Nor can they discipline a driver based only on what the data has told them.
Health and Safety
Safety-oriented companies rely on analysis of historical safety incidents to identify potential trends. They rely on lagging information and is limited to data related to the incident. It can tell what happened, but not why it happened. Companies need information that helps them predict future incidents, and predict the likelihood of the incident happening. This information is not captured in individual incident reporting. Through advanced analytics, companies can use predictive modelling techniques to identify the factors of incidents. The goal is effective prevention. Analytics makes it easy to sift through the data to find clues. Companies can analysis things like the weather, the job site, maintenance scheduling, and production measurements that affect workers. This allows them to take prevention action to reduce risk. An example is adjusting equipment maintenance scheduling, where machines are place, or scheduling of different tasks at different times.
In another example, the Australian company Seeing Machines has developed technology for mining trucks that tracks the driver’s eyes in order to spot driver fatigue (Nothling, 2018). The system uses a camera, GPS and accelerometer to track eye and eyelid movement, such as how often a driver blinks, how long those blinks last, and how slowly the eyelids are moving – and it can do all this even if the driver is wearing sunglasses. When a driver closes their eyes for longer than 1.6 seconds, an alarm is triggered inside the truck – both a noise and a vibration within the seat. Then, if the alarm is triggered for a second time, a dispatcher or supervisor is alerted so that they can make contact with the driver via radio.
“2.9 HR Analytics Application” from Human Resources Management – 3rd Edition by Debra Patterson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.
