High employee retention figures are naturally end-objectives for people-centric businesses. Contemporary organisations prioritize employee retention and the reasons are not far behind.
Imagine a scenario where there is a high employee turnover ratio. There is not only a constant state of flux, but a financial impact too.
Hiring and training costs go up exponentially for new resources, productivity suffers and with major positions remaining vacant for longer durations, it becomes excessively strenuous for the existing workforce.
It is not that companies do not make efforts to address the problem. Many of them conduct internal surveys and feedback sessions. They also gather data and insights on welfare measures. Yet, reports that employee survey information may not always be an accurate indicator of the ground reality.
A Harvard Business Review survey even reports that 58% of employees will trust strangers more than their superiors at work.
Also, many entities fail to understand the reasons behind high employee turnover. 89% of organisations (as per the OfficeVibe.com report) feel that this happens due to the desire of employees to earn more. Yet, the same study shows how just 12% leave jobs due to lower salaries.
What is the core problem? Rising employee turnover and for reasons more than financial.
What can be a probable solution?
Integrating technological solutions that are intelligent and nip the issue in the bud. Predicting turnover is the best way to enable preventive measures.
Bad decisions result from inaccurate or wrong data. Companies may enhance retention measures through depending on analytics that bypasses human feelings. How is this possible?
Instead of asking employees about their happiness quotient, companies should account for key variables which determine the same. For predicting turnover, look for values consistently applicable throughout the organisation. They include the following:
This data is already present with the company. You can also calculate variables like estimated time to commute to work, etc (from address information in the payroll system).
Once data is available, you can get it analyzed thereafter. You will then find out the core reasons contributing more towards employee turnover.
Companies already have a lot of data as mentioned. Employee surveys should be carefully analyzed. The data should be accurate and more on the factual side. Relying on facts is better than emotions.
Predictive analytics may be further optimized through integrating a data-based managerial outlook. Managers can optimally respond on the basis of data and variables which influence predictive analytics.
Companies should create personalized analytics-driven strategies and blueprints for every division. Managers should be skilled in data interpretation and learn to respond accordingly. Whenever you can predict employee turnover, you can enhance your capabilities in tackling the issue. You may be successful in reducing the turnover figures through emphasizing on the specific employee types who are more likely to put in their papers.
Your company can also go for a strategy revamp in order to fix these issues. Mainstream policy changes and relaxations may help enhance the chances of more employees staying on at the organisation.
It will help reduce the costs of turnover accordingly. Employees may not always leave jobs for getting higher salaries elsewhere.
Hence, turnover ratios can be lowered without paying higher salaries in most cases. Data and predictive analytics can thus help the company save more money on training, hiring and payroll costs. Survey results can only add to predictive analytics without being the core strategy.
Companies may also look to enhance their overall working environments, cultures and productivity levels. Many a time, companies feel that higher salaries are equivalent to employee wellbeing.
Yet, in such scenarios, they may skip other variables including work-life balance, work-from-home policies, lack of leave options, etc.
This may lead to the same culture at work, leading to employees quitting in spite of earning handsomely.
Integrating predictive analytics into organisational frameworks helps in reducing sudden reactions and decisions, scaling up the chances of successful employee retention across levels.
Hence, if you are looking to positively transform employee retention figures, predictive analytics is the way forward.
About the author: Dipak Singh is a thought leader and data cruncher, currently, he is working as Lead Data Scientist at INT. To know more do checkout his LinkedIn profile here.