How to Use Data for Employee Retention

The hordes of organizational data are being treated by leaders as a valuable asset, one that can help make objective, data-backed decisions that are highly accurate. HR data or talent data is no exception, with a number of talent systems and processes throwing up vast data. Whether it is the performance management system, or the learning management system, or mere communication records, talent data can provide HR professionals a number of useful insights to base talent decisions on. Hiring decisions, appraisal and promotion decisions, rewards and recognition, customized way of engaging employees, learning and development inputs, succession planning — all these areas are becoming more evidence-based and objective, thanks to the data deluge that HR is tapping into.

A critical pain point for HR professionals is retaining employees, since an employee attrition entails a significant cost to the company—not to mention the loss of top talent, which is scarce these days. The interesting aspect is that retention is not an HR intervention in itself, but draws from the various HR processes. This means that data from other HR processes like L&D, engagement, compensation etc., can be drawn and analyzed to create better retention. Here’s how you should approach this, from pre-hire to exit.

Hire: The recruitment process involves several data points related to candidate profile. Most organizations receive a lot of applications and recruiters are left deciding which are the best profiles that match vacant positions. This should be treated as a data issue rather than resorting to tedious manual means of CV screening. This means having ready reference data for vacant positions, and applying analytics to select the right “fit” or matches. Recruiters must understand that having the right fit in terms of skills, culture, role preference etc. goes a long way in onboarding employees who last and perform well. Incoming candidate data must also be analyzed to create recruiting trends i.e. knowing where the underperformers come from, knowing the data profile of an ideal candidate for the role, and so on. This data approach will help select the best match and increase chances of retention.

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What Is Employee Retention? Definition, Strategies, and Ideas, With Examples

Employer branding: Key wording is an important component of data analytics. For example, analyze which keywords represent your organizational culture and ethos, which ones resonate with your type of talent, and include these in the employer branding. This data-backed approach to employer branding will help your company create an attractive and authentic employee value proposition, increasing the chances of the “ideal candidate” applying to the company. This smart employer branding is a great way to create great retention by funneling the right fit.

Design interview probes: Use data to create ideal profiles and design interview probes to assess talent for these desirable skills, knowledge and behaviors. For example, study the performance outcomes of a high achiever, look at what he or she is doing differently by looking at the performance forms and talking to the manager. Incorporate these data points in interview questions to be able to look for those in candidates. This can be done not just for immediate performance, but for long-term potential as well, and hence can help long term retention.

Identify learning potential: Data on performance and learning inputs can be used to identify long-term learning and growth potential of employees. This can then be translated to HR interventions, the employee can be supported by special training, mentoring and coaching etc. to help him/her meet career aspirations. Such smart use of talent data helps cater to the individual needs of every employee, building trust and transparency. This leads to higher retention.

Drive fairness in rewards: Rewards and recognition are important for retention. Organizations must aim to know what motivates employees and cater to their needs, to be able to egg them on to outperform. Data is the touchpoint to identify high performers, high potentials, and make rewards and recognition customized.

Data is the mere starting point and must be converted to useful insights to be able to make sense of it. HR must look at setting up a team of data scientists to be able to make the most of existing data.