According to recent studies, the cost to replace an employee could be over 200% of their annual salary . By analyzing and relying on historical data, trends & patterns, managers can identify employees at risk of leaving and the impending reason, with a degree of certainty . A recent survey showed that ML can be up to 17 times more accurate in predicting at-risk employees than any other method . This intelligence can be used for employee-specific insights as well as policy-level insights to accelerate the organization’s journey towards employee satisfaction and retention.
Businesses can analyze data on employee performance to identify consistently high-performing employees and use these insights to accelerate the performance of the organization. These insights can be fed back to the recruitment, talent engagement, goal setting, and performance appraisal processes as well . Over time, the organization can also gain insights on recurring patterns – identifying managers and teams that consistently perform better than others and abstract best practices from them. These analytical insights can highlight the need for specific training and support for managers.
For decades, the recruiting function worked in silos, with little or no feedback on employee performance post joining the organization. Analytics can change the landscape of recruitment in two major ways –
-Identify hard and soft attributes that differentiate employees who stay longer and perform better.
-Design a fool-proof process to recognize the desired employee attributes through experimentation in the recruitment process and extract the same from prospective employees' social media profiles, without invading their privacy .
There is ample empirical evidence suggesting that employee motivation has a direct impact on organizational performance. Realizing this, today, many organizations value employee experience and motivation as much as customer experience. Analytics can be a potent tool to study employee sentiment and provide individual-level recommendations to managers on specific actions that could motivate employees. Conducting automated (chatbot-driven) employee surveys on a continuous basis, analyzing them using NLP techniques, generating actionable insights, and communicating them to the managers are a few benefits AI can enable. Through a close analysis of employees, senior management can also utilize an average employee sentiment score to plan large-scale horizontal initiatives.
Analytics can be used to assess large amounts of employee & market data to stay ahead of the competition in attracting the right talent, by offering competitive compensation packages while not burning through organizational expenses. Given that non-monetary aspects have proved to be a significant employee motivator, analytical experiments can help identify suitable combinations of monetary and non-monetary benefits that best fit each segment of employees. Further, analyzing the data from surveys and employee feedback can help the organization formulate other employee engagement initiatives that can boost employee satisfaction and loyalty.
With vast amounts of data at hand, analytics can automate repetitive tasks & eliminate unnecessary workloads, helping HR professionals focus on human-centric tasks that can enhance the employee experience. For instance, instead of collating multiple spreadsheets & presentations, automated interactive HR reports can deliver up-to-date information. Analytics helps create powerful graphs that aid HR professionals in objectively reading & understanding data to boost organizational growth. With the automation of tasks, organizations are also privy to real-time data, thereby speeding up the decision-making process.
Srinidhi is a well-known thought leader and futurist in the field of Applied-AI. He has led the analytical and digital transformation of large and small organizations, helping them create significant value. Having led organizations of all sizes, Srinidhi has experimented with various Project Management, Knowledge Management, and People Management processes as applied to Analytics/AI initiatives, extracting best practices that have now become the industry norm. Srinidhi is also an avid golfer, nature photographer, and mountain hiker.