Article: Controlling Attrition through Data Analytics

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Controlling Attrition through Data Analytics

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The need of the hour is to take some proactive measures to understand and analyze the discernible factors which might trigger thoughts of attrition in employees.
Controlling Attrition through Data Analytics

'How many employees quit your organization every year?'

You would probably be able to answer this question but may fail to understand and analyze the interplay of underlying factors to make sound predictions.

Employee attrition is a significant challenge facing organizations, with a large percentage of employees leaving their roles within their first few years. The trend seems to be more conspicuous among millennials who will comprise half of the total global workforce by 2020. A recent study1 suggests that one in two millennials will quit their job in 2 years. This is alarming as retaining employees is less expensive and cumbersome than continuously replacing them, especially if they are your high potentials. A certain amount of attrition will always happen as one cannot unleash all the variables (e.g. human factors). However, by leveraging  BI (Business Intelligence) and using the right statistical modeling and data mining techniques- it is possible to predict and control attrition to a large extent. 

Many companies use data analytics retroactively i.e. acting on the results, however, by just keeping a track of employees who are leaving, recording their exit interviews and rolling out some engagement surveys, this will not serve the purpose. The need of the hour is to take some proactive measures to predict the ‘flight risk’ and take appropriate measures to curb it. And the first step towards this is to understand and analyze the discernible factors or root causes triggering attrition in employees. These factors can be either work-related or personal; some of the work-related factors could be:

Dissatisfaction with compensation: People might simply quit if they get better pay and perks somewhere else.

Inequity or lack of fairness: Most of the people possess a strong sense of justice or fairness, especially at the workplace. Employees may question the fairness of another team member getting a raise or a promotion and the perceived lack of injustice may severely disenchant employees.

Strained Relationship with managers: Unsupportive bosses are one of the biggest reasons of employees leaving their respective organizations.

Lack of growth and needs fulfillment: Employees seek certain things from a given job such as social status, financial security, work-life balance and personal fulfillment in terms of growth, desired role and responsibilities and career progression. If the employees feel those needs are not being met, there will be dissatisfaction and an eventual exit.

Inadequate Facilities: it can be related to a long distance commute, irregular shifts, food and cafeteria and unpleasant working conditions.

Apart from these, some of the personal factors comprise of Demographics (e.g. age, gender, ethnicity etc.), and personal issues related to marriage, spouse transfer, medical reasons, higher education and simply their personality where some people are predisposed to quit more often than others.

There are several instances where employees quit their organizations without giving any previous signals. However, in most cases, the aforementioned factors (coupled with other hidden factors) induce a peculiar behavioural pattern in employees and it is possible to notice indicators or signs, when employees are contemplating their exit, these can be:

Discrepant attendance record: Disengaged employees who are thinking of quitting their organizations do not feel motivated to come to work regularly and hence show inconsistent attendance pattern. Disengaged employees are also more prone to take more unscheduled and longer leaves.

Underperformance: Disengaged employees might also reduce their work effort and quality of deliverables simply because they feel disconnected to their job or organization. Instances of disengaged employees procrastinating and missing out on the deadlines become more rampant. 

Indifference: Disengaged employees don’t seem to be much bothered about current affairs of the organization and they either do not participate or under-participate in organizational events.

Voice: When disengagement happens, employees become more vociferous about their not-so-pleasant experiences and may publically say something about the factor/s causing dissatisfaction. Displeasure can be easily evident during discussions with managers, informal discussions with peers and sometimes also on the social platforms.

After identifying root causes and indicators of attrition, it is possible to design targeted analytics to extract ample insights from the data- for an instance, organizations can build data-driven predictive models for attrition and thereby plan for appropriate remedial actions and retention schemes for the employees with ‘higher flight risk’. They can also go one step further by creating a plan to recruit suitable candidates for lost or vulnerable employees. 

Many organizations have started collating relevant attrition-related data points to build their own attrition models. They are constantly upgrading it by adding relevant data points. They are doing it through one on one discussion with the employees and senior leaders, engagement surveys and day to day observations. They are identifying correlations as well as causation between hundreds of data points and employee engagement, for instance, increasing the number of online modules on leadership skills will improve employee engagement and retention scores (correlation), only when it is backed by exposure to real-time opportunities to practice and demonstrate leadership skills (causation). The need is to dive deeper into the data and look beyond the obvious to make sense of it. Organizations also have the option to explore other vendor-provided expert services and solutions to empower its analytics platform. Some of the tools are highly innovative that even let us simulate the use of different retention tactics, to assess initiatives before we make investments. 

The proactive use of ‘data analytics’ will allow organizations to accurately anticipate rather than react. With the right information derived from the data, organizations will be able to reduce attrition and the costs associated with it, retain good employees and will also be able to reduce the after-effect of attrition, such as the impact on team morale and client satisfaction. 

Topics: HR Ready, Lets Talk Talent, HR Technology, Talent Management

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