We have seen how Amazon harnesses data to get smarter about customer behaviour and leveraging analytics to suggest options like “frequently bought together” and “customers who bought this item also bought”. It doesn’t just allow them to sell more but also provides value to the customers. It’s a win-win for the company and the buyers.
Today companies are increasingly using insight to improve decisions and business outcomes. The idea behind this approach is to leverage data and analytics to understand the organisation’s health and make data-driven decisions to thwart potential issues and improve employee engagement.
However, most business leaders face many challenges especially unavailability of right insight at right time to be able to intervene when required.
Most companies wait till the year-end to conduct employee feedback surveys and gather data. This process is replete with errors since most data is forgotten or lost over time. So it is impossible to get deep or real-time employee insights with these obsolete processes.
To make matters worse, when companies send across annual employee satisfaction surveys, employees face “survey fatigue” because of the question overload. Employers want to address every possible question in a single survey. But it is impractical to expect that employees will share correct data with such overwhelming surveys.
Finally, the irony of today’s mature systems is that the data lies in multi-fold, siloed systems that work well independently but do not talk to each other intelligently. Companies have human resource management systems (HRMS), performance management systems and more, sitting disconnected from each other. Analytics, however, needs a cross-functional approach because most business problems derive data from and influence more than one function of the company.
Given the challenges shared above, a lot needs to be changed in our modern system to draw smart, actionable insights and improve employee engagement. The answer to the problem is integrated AI-powered workforce analytics.
Let’s first understand the building blocks of the Employee Engagement Analytics to be able to connect the dots between AI and employee engagement.
These real-time small but frequent surveys measure how motivated your workforce is to perform their best in their role. Frequent employee pulse surveys can help understand employees’ feelings and perform “temperature checks” on employee engagement. They help leaders hear employee opinions and sense issues before they become too big to tackle and gather likes/ dislikes about its culture, goals, and work environment. They can also help point out areas that might be hampering employee productivity.
These surveys are not just engagement surveys but can cover a wide range of requirements from onboarding, well-being to exit surveys.
For instance, onboarding surveys can help you reduce early attrition, which happens within a year of joining and is incredibly expensive for your company. Strategic employee pulse conducted as early as 90 days from joining can help gather feedback about their onboarding experience and their sentiments about the company, people and culture. Through AI-powered analysis or predictive analytics of the new-hire data, you can identify early indicators of quitting tendencies, which managers can intervene early on to avert attrition.
AI is helping organisations gauge employee engagement and sentiments in real-time. AI-powered systems can help sift through pulse survey data, draw actionable insights and recommend meaningful changes to improve employee engagement and reduce attrition. AI can also help employees in real-time by creating personalised learning recommendations and growth paths based on their goals and patterns. This can drastically improve engagement on the job and performance while moving towards their long-term career goals.
There is a regular listening process throughout the employee lifecycle to get real-time data. And in an integrated system, this data, collected at different stages of the employee lifecycle, can be intelligently combined to make powerful decisions and take preventive measures in time.
For instance, employee turnover is a big challenge for every company today. But more often than not, leaders act on it only when the symptoms of the problem start to show and employees that you had invested in have already moved out. These high-risk issues can be predicted with a holistic system that continuously gathers impulse, gauges sentiments intelligently to identify risk points, and suggests improvements accordingly.
In a system that integrates learning into the process, the skill development requirements are addressed, once a need has been identified through impulse data analysis. Such timely interventions are possible when the system is able to predict risks and provide learning recommendations according to the workforce’s performance needs. An integrated system thus takes care of the entire process from listening to taking action.
Predictive Analytics empowers companies to course correct and drive future events towards more favourable outcomes.
Such predictive tools can provide real-time feedback to employees, directing them towards steps they can undertake to improve their performance right away. Another way predictive analytics can play a big role in organisations is in improving manager effectiveness. It defines tenets to measure effectiveness, identify areas of improvement and, based on the insights, fill the gaps well in time by recommending personalised learning.
The multi-pronged benefits of real-time engagement and workforce analytics include-
· Increased performance
· Improved employee engagement
· Reduced attrition
· Reduced talent acquisition cost
· Continuous career development
· Immediate intervention and smarter course correction
· Real-time pulse check