Employee turnover refers to the percentage of workers who leave an organization and are replaced by new employees. It is very costly for organizations, where costs include but not limited to: separation, vacancy, recruitment, training and replacement. On average, organizations invest between four weeks and three months training new employees. This investment would be a loss for the company if the new employee decided to leave the first year. Furthermore, organizations such as consulting firms would suffer from deterioration in customer satisfaction due to regular changes in Account Reps and/or consultants that would lead to loss of businesses with clients.
Are pre-hire predictors of turnover also effective indicators of work performance? Several indicators, such as biodata (biographical data) and pre-hire attitudes, have been explored for the purpose of answering that very question. In particular, three types of information are especially strong indicators of job performance and turnover.
- Biodata: Predictors that represent pre-hire embeddedness in the organization (employee referral; number of friends and family) and habitual commitment (tenure in prior job; number of jobs in last five years)
- Pre-hire attitudes: Includes the applicant’s self-confidence and confidence with decisions, as well as the applicant’s desire for a job and pre-hire intent to quit
- Personality traits: Conscientiousness (being dependable and reliable) and Emotional Stability (ex. Individuals who have low emotional stability tend to have negative perceptions of themselves and their environment.)
Turnover Decisions and Job Performance
Some notable indicators of which employees are likely to remain working for a company six months after hire include: pre-hire embeddedness, habitual commitment, personal confidence, motivation for employment, conscientiousness, and emotional stability. Further, beyond the period of six months post hire, up to two years later, the remaining two indicators for voluntary, avoidable turnover are conscientiousness and emotional stability. The number of jobs held over the previous five years was a better indicator of early turnover, whereas tenure on the most recent job was more predictive of early job performance. The good news is that most turnover decisions are “functional,” meaning that those employees who tend to stay in an organization tend to be the better performers.
Analytics is one such tool that can help organizations predict employees’ performance based on historical and real-time data. It provides both retrospective as well as forward-looking analysis.
Predictive analytics can be applied to the workforce to identify traits/patterns that account for bad or good performance on an individual and team basis. Since analytics is an amalgamation of powerful mathematical algorithms, it also gives objective insight into their work preferences and the factors that drive their performance.
There are many approaches to predict program ‘s performance on computers. They can be roughly divided into three major categories:
- Simulation-based prediction
- Profile-based prediction
- Analytical modelling
Predicting employee turnover rates with data
Thanks to advancements and innovations surrounding modern technology, the average HR employee can actually glean a great deal of insight into the likelihood of employee turnover.
What was once relegated to guessing games can now be laid out clearly on a spreadsheet for any set of curious eyes to peruse.
Provided you’re willing to tackle the slight learning curve that comes along with any new tech skill, you’ll be well-poised to make data-driven predictions about retention and turnover rates. Through the use of predictive analytics, you’ll be able to gain valuable insight into employee flight risk and manage potential issues before they arise.
Predictive analytics rely on the use of data, machine learning techniques, and statistical algorithms. These three factors, when combined, can do tremendous work in identifying the likelihood of future outcomes– in this case, those outcomes will centre around turnover and retention rates.
The adage about history repeating itself is at the centre of predictive analytics. Calculations and predictions made through the use of predictive analytics are based on factual and historical data; that data is used to assess what’s likely to happen in the future based on what’s happened in the past.
Any project that relies on predictive analytics is only as good as the data that’s fed into it. Make sure that the data you collect is entirely factual, valid, and useful to your purpose. One commonly-used phrase in the realm of data analysis as a whole goes something like “garbage in equals garbage out.” Luckily for you, it follows that quality in translates to quality out.
Implementing data analysation
Practically enough, most predictive employee flight risk models rely on employee data that’s already stored in the human resource system of an organisation. It’s easy to pull out the stats you need to get insight and results into what the future could look like. In many cases, the following pieces of information are critical to determining future employee turnover based on predictive analytics:
- Duration of employment
- Time elapsed since previous promotion (if any)
- Compensation level
- Job performance scores and ratings
- Commute time