The HR profession has barely scratched the surface in terms of harnessing the power that analytics can provide. But already, the market is galloping along at a tremendous pace, predicted to grow from $1.9 billion in 2019 to $3.6 billion in just the next four years.
And the reason?
It’s because of the huge contribution it can make in turning historical transactional HR data into powerful, future-gazing and predictive models for a whole host of HR purposes.
Below you’ll find our top nine benefits that HR analytics can bring to businesses, and which savvy HRDs cannot afford to miss out on!
Just click a benefit to jump straight to it – and don’t forget to check out and share our HR analytics infographic too:
- Improve retention
- Drive employee performance
- Create better compensation and incentive programmes
- Supercharge recruitment
- Make real change to company culture
- Improve employee engagement
- Enable better workforce planning
- Promote better employee development
- It underpins machine learning
1. It improves retention
“IBM already says AI can predict which people will leave their organisation with 95% accuracy,” says Kevin Green, author Competitive People Strategy.
Not only can employee survey sentiment analysis deduce lulls in engagement and cross-reference these back to the previous attrition rates of different employee groups, but it can also flag interventions that might be needed to avert known points of departure (at a specific time in role for new starters for instance).
Adds Alex Rinke, co-founder and CEO of Celonis: “Analytics-based process mining can track worker milestones but fills in the gaps along the way to ensure the whole journey runs as smoothly as possible.”
When global information firm Nielsen created a predictive model to retain key talent, it found that when it approach at-risk talent with job improvements, it was able to retain 40% of these so-called ‘flight-risk’ employees, and improve their chance of staying by 48%.
2. Improve employee performance
Shoe retailer Clarks recently used analytics to establish a clear link between engagement of employees and its impact on individual and company performance [a 1% improvement in engagement created a 0.4% rise in business performance]. It found length of tenure of a store manager was a major indicator of performance, allowing the company to create a template for high-performing stores and an engagement toolkit for managers to use to boost performance.
Employers can now learn about what motivates their staff members as well as what blocks them from reaching their maximum potential believes Gareth Paine, Associate Partner at EY.
“The insights collected by data & analytics goes further than just tracking performance and evaluating motivation, he says. “It can be used to identify and forecast a low performing employee. The data can be analysed to identify patterns and a plan can be implemented to improve performance”
3. It creates better compensation and incentive programmes
Clever analytics can determine whether each additional percentage of bonus translates into either improved retention or performance, even identifying groups for whom pay rises or other incentives plans make no differences at all (allowing considerable savings from ‘wasted’ payments to be made).
Analytics firm The Brew found that if engineers at one major company it worked with got a 12% raise, it translated to retention rates 15% higher than those who got just a 5% rise.
4. It improves recruitment
Employee retention starts with cultural fit and humans are a poor judge of it, with 66% of CFOs saying they themselves have got it wrong (according to research by Robert Half).
Analytics doesn’t just better assess psychometric profiling, it is now being used in video interviews, establishing a profile around attributes like trustworthiness based on analysing hundreds of split-second facial expressions. The analytics claim to be ethnicity-blind, but characteristics-accurate.
Technology provider Hirevue claims its algorithms are so good they find salespeople who make 15% more sales than typically sourced hires. Charles Hipps, founder, Oleeo says: “Algorithmic techniques like data mining can help eliminate human biases, enabling employers to better understand what drives performance. It also encourages them to move away from familiar tried and tested approaches.”
5. It makes real change to company culture
“By identifying different personalities and using that information to shape tailored approaches, organisations can build the cultures they want, and the recruitment rules to hire against it,” says Megan Barbier, VP of human resources, Wrike.
Analytics can map current organisational culture against intended culture, to see where the gaps lie. Research by Harvard Business Review finds organisations with greater intrapersonal cultural diversity had higher market valuations and produced more and higher-quality intellectual property, through indicators such as patents.
6. It improves employee engagement
“We truly are on the brink of being able to leverage AI and HR analytics to understand how people are feeling to ensure we are adapting work environments to support individual experience and optimise support to drive better performance,” argues Jennifer Frieman, chief talent officer, Momentum Worldwide.
Best Buy attributes a 0.1% increase in employee engagement with contributing to a $100,000 pa improvement in operating income per store – a link that now sees it run engagement surveys quarterly rather than yearly.
7. It enables better workforce planning
Predicting attrition clearly links to improved workforce planning – especially for roles deemed critical.
IBM used its own Watson machine learning capabilities to number crunch data ranging from recruitment, tenure, promotion history, performance, role, salary and location to reduce turnover in critical roles by 25% in a four-year period.
Analytics can also measure over or understaffing – for instance one Zimbabwean mining company found a precise 22.5% increase in activity calls is required to justify each additional hire if workloads are to be maintained and burn-out is to be prevented.
8. It enables better employee development
Which employees suit on-the-job vs classroom learning? Which employees (with what characteristic traits) tend to be most likely to seek additional learning (and can knowing what they’ve picked be used to suggest others?).
Analytics answers all these questions by doing all the legwork for HR and presenting them with simple-to-read dashboards.
Fujitsu used analytics to specifically decide whether to continue with a peer coaching programme. This enabled it to find there was indeed a strong correlation between peer coaching and business results.
9. It underpins machine learning
By necessity, doing analytics promotes analytical model building (or machine learning), because it’s the patterns provided by data that need to be learned and modelled on.
Walmart is currently using machine learning to create better in-store experiences for customers, by experimenting with facial recognition technology to determine whether consumers currently look happy or sad.
Andy Lothian, co-founder of Insights Learning and Development says: “The additional payback is that employees will get the chance to adopt new technologies to help them enhance their own human skills: things like building deep relationships, unfettered creativity, holding genuine empathy.”