Gallup’s State of the Global Workplace report shows that 85% of employees around the world are disengaged. If your employees are part of this statistic, your organization is much more likely to experience higher employee turnover, low productivity, and dissatisfied customers. Meanwhile, truly engaged employees have a stronger sense of belonging, have higher job satisfaction, are likely to remain longer at your organization, and produce higher-quality work.
Every year, organizations across the globe spend billions of dollars on programs and events designed to improve morale and boost employee engagement. While these one-off or seasonal initiatives do deliver some results, the effects are often short-lived and may fade completely a few weeks after the program’s completion.
Achieving long-lasting results is a continuous process that requires a deep understanding of employee engagement and effective, ongoing communication with your employees. One objective and trusted approach to accomplish this is through the use of data and analytics (specifically behavioral data) to inform and validate the design and execution of your engagement strategy.
Behavioral data is indispensable to building a truly engaged workforce because it allows you to see the true impacts of your engagement initiatives based on how employees work, as well as how they work together. Below, we’ll discuss three of the most valuable ways behavioral data can help you boost employee engagement.
Measuring what truly matters
With behavioral data, management can better understand what motivates individual employees, identify how employees work best, and find ways to connect naturally with each team member based on their needs and engagement drivers.
Behavioral data can also reveal areas for improvement in the day-to-day work of employees, such as what percentage of them have weekly 1:1 time with their managers (for coaching, mentoring, and collaboration). These insights can help drive better employee support and leadership behaviors by revealing if employees are sufficiently connected with their direct managers and receiving adequate support. In another example, it can also measure after-hours metrics, such as how much time employees spend collaborating with coworkers after work hours via emails, meetings, calls, instant messaging, etc. This helps to inform organizations where there are potential burnout risks and which teams are more likely to experience it.
Designing data-driven engagement strategies
With these behavioral workplace insights, business leaders can better determine where to spend time and money on engagement initiatives to drive improved results. For instance, HR leaders could make changes to improve workplace culture and promote mentoring between management and teams by assigning individual team members recurring time slots with line managers.
They could also employ strategies that boost inter/intra-team collaboration where behavioral data shows this is absent or lacking effectiveness. HR personnel can leverage these analytics to identify the winning combination of skill sets and behaviors that will skyrocket the output or efficiency of teams across the organization, while also improving the workplace experience for employees.
Helping employees achieve a healthier work-life balance
Insights from behavioral data could also help HR personnel identify employees who spend time working and collaborating outside normal work hours and take appropriate steps to dissuade them from the notion that they’re expected to ‘always be on.’ By measuring things like after-hours or weekend work metrics (how much time employees spend collaborating with coworkers after work hours or on weekends via emails, meetings, calls, instant messaging, etc.), behavioral data can inform organizations where there are potential burnout risks and which teams are more likely to experience it. Since continuous work outside normal business hours is a leading indicator burnout, HR can leverage these insights to help such employees create a healthier work-life balance.
One multinational technology company used Humanyze’s workplace analytics solution to measure collaboration data before and after the transition to remote work due to COVID-19. They discovered that workday lengths were significantly longer after shifting to remote work.
However, a closer look at the data revealed that while many employees were working across a longer span of the day, it’s actually because they were working more flexibly throughout the day by taking periodic breaks between blocks of working time to deal with personal matters or be with their family. Although employees weren’t necessarily burning out in this scenario, the company may have misinterpreted these findings and instituted ineffectual engagement strategies that target “employees at high risk of burnout” without examining the data on a more granular and contextual level.
The quality of behavioral data
The success of your data-driven engagement strategy depends on the relevance and quality of the data that’s leveraged. While most organizations do analyze employee survey data, it isn’t ideal to design a data-driven engagement strategy based on things like annual or quarterly surveys alone.
Temporal surveys are inadequate for capturing how employees feel about working for your organization, and should instead be leveraged more regularly alongside quantitative behavioral data. A better approach involves the continuous collection of data via the use of weekly or monthly pulse surveys (without inconveniencing your workforce) or software solutions that capture employee sentiments in real-time.
It’s also a good idea to measure engagement levels by measuring data around the usage of collaboration tools to understand both the quality and quantity of interactions within and outside teams/departments. Of course, approaching this in a way that guarantees employee anonymity and protects data privacy is also an essential ingredient for success.
Business leaders can also leverage HR data around skill sets, performance reviews, retention, salary, experience level, demographics, rewards, leave patterns, etc. Interpolating these with other disparate data sources allows business leaders to draw holistic conclusions about engagement levels for a specific group of employees. Identifying engaged and disengaged employees can then help inform decisions about what and where to spend engagement budgets most effectively.
Arbitrarily spending on rewards programs, managerial training, performance appraisals, annual employee surveys, and a host of engagement activities without objectively informing the decisions with data first will likely result in minimal or no long-term benefits for employees.
Leveraging behavioral data and the right analytics solutions, such as the Humanyze Platform, will help business leaders determine what engagement programs to implement, how best to institute them, and which groups would benefit the most. Such an approach will help organizations obtain the needed insights to make better, data-driven decisions that drive employee engagement, productivity, and success.