8/9/2019 Boosting Employee Retention With Predictive Analytics

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Reprinted from the J anuary 27, 2014 e-newsletter. 2014 Quirks Marketing Research Review (www.quirks.com).This document is for Web posting and electronic distribution only. Any editing or alteration is a violation of copyright.

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Boosting employee retentionwith predictive analytics

| By Michael Lieberman

employees at risk and how employee surveys, segmentati onand predictive analytics can help.

An important introductory note is that not all employeesare worth saving. Companies should not dedicate resources totry to retain each and every employee. Many employees arenot at risk for leaving, particularly given the current econom-ic climate where jobs are not easy to come by. There are alsoemployees who will leave no matter what the company offers.And finall y, there are employees the company wi ll not wantto keep (e.g., those with lackluster performance, negative at-titudes or who are not team players, etc.).

I t i s far easier for a proactive organi zation t o tr y toretain t he 2,000 employees that may be at r isk t han toatt empt the same for all 10,000 employees. This is donethr ough segment ing the employees and making the size ofthe task more manageable. I t also increases the chancesthat the organization will do something, which is alwaysbetter than doing nothing.

Attitudes are relatedThe example below shows the results of an employee satisfac-

tion study run for a large corporation. After running regressionsof the individual questions on the employee survey, the resultsshow attitudes that are positively and negatively related to em-ployee retention.

Below are the employee attitudes that are significantly re-lated to job satisfaction:

Employee turnover is a natural part of business in anyindustry. But excessive turnover decreases the overallefficiency of a company and hurts the bottom line. Under-standing the effects of a hi gh employee-chur n rate servesas a motivator for l arge corporati ons - or even small busi-nesses - to reduce turnover and make the company a moreappealing place to work. High employee-chur n costs money- money that was invested in that employee via training,educati on and benefi ts.

High turnover rates cost the company in other ways aswell . Exit intervi ews, advert ising for the job, recrui ti ngcandidates and interviewing for open positions are time-consuming tasks. Supervisors or other employees oftenhave to cover unti l the job is fi l led and i t wil l usual ly takethe new employee at least a few months to get up to speed.Finally, employee-churn hurts the group dynamic, particu-larl y i n l arge companies where high employee-churn cansignificantly affect, say, a brand teams efficiency.

Two key componentsAll of these factors make understanding and addressingemployee churn a high priority. An effective employee reten-tion strategy has two key components: 1) ident i fying thoseemployees at risk for leaving and 2) targeting those at riskwith appropriate incentives.

I t is beyond the scope of t his art icle to go into specif ic in-tervent ions. However, I wil l address a strategy of i dent i fying

8/9/2019 Boosting Employee Retention With Predictive Analytics

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To purchase paper reprints of this article, please contact Quirks Editor Joe Rydholmat 651-379-6200 x204 or at joe@quirks.com.

is a record of some sort . The predicti ve analyt ics techniquesmay include logistic regression, discriminant analysis, neuralnetworks and tree analysis, among many others. These functionsare fed into the software and patterns are produced. Typically,a pattern is only detectable by a software application. Oncethe pattern is defined, the software can scan the pattern for a

whole database of employees and make a prediction (read: score)regarding the propensity of that employee to leave. As a deliv-erable, we would produce this score in a sorted list so that theemployees who are most likely to leave are at the top when it ispresented to HR.

In other words, we: 1) consoli date company data about em-ployee status and behavior; 2) in some cases conduct employeesatisfaction surveys; 3) all ow software to examine data for pat-terns and how they relate to employee turnover; 4) produce apredictive model that can be applied and updated in real time;and 5) apply the model to statistically predict employee churn.

In addit ion, we provide a list of discriminators (i.e., factorsthat are significantly different for those at risk than for those

not at r isk). This li st is not unl ike the aforementioned resultsof regression. For example, i f a valued employee feels that herbenefits are inadequate or that her boss is unfair, this would tripa wire for HR to contact that employee. In addition, companydatabase variables may show that materni ty leave, change inmarital status or moving further from the office may triggerchurn. Perhaps a valued employee is not happy with hi s subur-ban commute and would appreciate telecommuti ng for a portionof the week.

Scalable solutionEmployee churn is just one of the many examples that predictiveanalytics can be applied to help business efficiency. I n the end,

this scalable solution could save companies the time and moneyassociated with high turnover and could even boost employeesatisfaction company-wide. After all, the better you know youremployees, the better you can work together.

Michael Lieberman is founder and president of Multivariate Solutions,a New York research firm. He can be reached at 646-257-3794 or atmichael@mvsolution.com.

It is fun to come to work. I would recommend a career with the company to a friend. I am proud to be associated with the company. The majority of the staff is happy working at the company. I receive regular feedback about my performance. At the company, we have high standards for operati on.

There were also att itudes that were negatively related to anemployees intent to stay. Employees with l ow ratings of thesemay be at r isk to leave the company (NOT is placed in f ront ofthe att itude if it is negatively significant):

Communication wi th my boss is NOT open and honest. My boss does NOT treat me fairly. At the company, the managers do NOT work well together as ateam.

Relative to the industry, our health and welfare benefits areNOT adequate.

I have NOT been well-trained to do my job.

Rank them statisticallyCorporati ons have a clear business case for ident ifying whichemployees are most at r isk for l eaving. Our mission, then, isto employ predictive analytics and score which employees arelikely churn candidates versus those who are stable. I deall y,we could take the ent ire database of corporate employees andrank them statistically. The benefit of doing this is to provideHR departments with a sorted list of employees wi th a scorethat r ef lects employee risk of churn. Believe i t or not, oncethese models are in place, running them takes less time thanit does to eat a meal.

Most companies have a fair amount of data on their em-

ployees, i ncluding role in the company; years employed at thecompany; age; income; marital status; maternity leave; number ofsick days taken; and performance reviews. In addition, with socialmedia, there is debate surrounding the reasonable expectati on toprivacy an employee should have for his private accounts.

Without going too much into the math, the data is matchedfor employees where each row is an employee and each column