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“Employee Retention is as critical as Talent Management & Talent Identification, if not more – as there are the people who will launch new businesses, find new ways to strip out costs, build better customer relationships, and drive innovation. Really, the future of our organization is in their hands.” – How to Keep Your Top Talent, HBR, 2010
The Client Organization, a global IT company was facing a challenge in employee retention & managing employee attrition. Some initiatives had been taken internally to control attrition, however, these initiatives were not sufficient and the organization felt the need to be better equipped in order to handle situations of employee attrition.
The Client Organization approached The Brew to develop a data drive solution ( (more than a passing fad, data driven decision making is also one of the key ingredients of building a high performance organization) which would proactively predict attrition risk of employees, and subsequently enable it for improvised & effective decision making and management. (See all of works & applications of HR Analytics here).
The client wanted to minimize attrition by improving its retention strategies by developing a real time solution to target high risk employees and accordingly take better decision. The research was split into specific objectives shown below:
- Develop a model to predict employee membership towards risk categories at an overall as well as at a department level – driving attribution to revenue leaks & turnover costs (also see The Brew’s Turnover Costs Estimator)
- Create clusters bases on risk measured from the risk model
- Identify and target high-risk talents
- Charting a retention plan targeted at specific categories
- Measure retention performance across units
- Provide inputs to reshaping talent sourcing strategies (Take the Performance Culture Assessment to fix leakages across talent value-chain & human capital practices
- Implement a real time solution to indicate high risk category
The approach presented in this case paper, to predict employee retention is based on separated employee’s demographic data for a particular organization. This technique to predict employee retention can be applied to every organization based on employee demographic data.
This predictive technique to define risk attached with each employee should be modified and remodeled bi-yearly to refine coefficients based on current data.
Based on the model results, four levels of employee risk buckets were identified and have been shown below: High Risk Score (Score > 90%), Medium Risk Score ( 90% > Score > 60%), Low Risk Score (60% > Score > 20%), Minimal Risk (Score < 20%)
The following action plan was devised for each of the zones identified above:
- For Safe Zone & Low Risk Zone – No action will be taken. Employees in this zone are engaged
- Medium Risk Zone – A discussion to be scheduled by the manager with the employee. During this discussion, the manager would probe on the employee’s level of engagement by seeking to understand his/ her concern areas
- High Risk Zone – In addition to above, If the employee is a high-performer or a high-potential, a further discussion will be scheduled by the skip-level manager with the employee. The focus of the discussion would be to understand employee’s immediate concerns.
The motive of this approach is to help organizations proactively predict retention & value at risk in real time and therefore take the necessary steps to prevent it, or plan the manpower inventory accordingly. Instead of trying to retain everyone, an organization should identify precisely who needs to be kept on board, and how the company can continue to appeal to the high potential employees. Employee Retention (HBR’s insights on employee retention – link) & Attrition Risk Assessment is receiving significant attention and opening a scope of focused research initiatives. An analytical approach to this assessment aids in prediction of attrition risk, action planning & subsequently, retention.
Employee Risk Attrition Assessment ReportEmployee-Attrition-Risk-Assessment-Report-IT-Case-Study
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