TIME for HR analytics to show value to business

 

As global environment becomes more and more dynamic, just working on gut instincts won't help to make sustainable business decisions.


Large volume of employee data is available, yet leaders are clueless on how to get the best from their people or failing to identify how to enagage and boost workforce performance? Why so?

Because they lack to identfy and understand, how to turn data into information and information into insight. The key is to analyse them to enhance competitive advantage.


Business decision based on facts and patterns always leads to informed decision which can be tracked. Just like by collecting enough facts from your research and based on that plus your gut instincts helps you select a school to send your offspring. Similarly, now the time has come to use & embrace HR analytics for making informed business decisions whether for recruitment or for retention which is the core of business.


What’s driving this shift to analytics?

Certainly, companies today want more from their talent. No matter what industry you are beginning to understand and implement from operational level and steadily moving to advanced and predictive level is pivotal.


Interesting case study on how other organizations use analytics to improve their management of human capital

1. Mostly organsations have communication plans for customers but Best Buy strengthened its internal communciation program (https://hrinsightsforyou.blogspot.com/2021/01/creating-succesful-internal.html) and linked engagement with store income. It focused on using employee social sharing and then precisely identified the value of a 0.1% increase in engagement resulted in over $ 100,000 in annual operating income per store. It also made employee engagement surveys quarterly rather than annually. 

2. Experian was facing high levels of turnover that were 3-4% higher than they wanted it to be. By building a predictive model that included 200 attributes, including team size and structure, supervisor performance, and length of commute, they were able to predict flight risk. An example risk factor was teams of more than 10 to 12 people. The analytics team also identified flight risk triggers: when someone moved further away from the office, this would increase immediate flight risk. The model was rolled out in multiple regions – with slight differences to the predictive algorithm. These insights, combined with good management practices, resulted in a drop in attrition of 2-3% over the past 18 months with an estimated saving of $8,000,000 to $10,000,000.

3. Nielsen created a similar predictive model by including 20 variables, including age, gender, tenure, and manager rating. Over time, more variables were added.This exercise provided multiple insights, including that the first year mattered the most. First-year employees where checked whether they’ve had their critical contact points. For example, the first check-in with their manager had to happen within a certain time span after hiring, otherwise, it would trigger a notification. This was a proven, important condition for first-year retention. Although getting promoted pushed people to stay, lateral moves were also a strong motivator for people to stay. A significant outcome was that the people with the highest flight risk in the next six months were approached and the company was able to move 40% to a new role. Making these lateral moves increased an associate’s chance of staying with the company by 48%.

HR analytics takes the guesswork out of employee management and is, therefore, the future of HR.




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