Managers are aware that they are being held accountable for their team’s performance. How well their teams do is a direct reflection of how well they are functioning in their own roles. But for leaders and the companies they work for, finding ways to effectively measure performance can become complicated. It’s also tricky to pinpoint which characteristics will turn a new hire or existing employee into a top performer.
Analytics technology backed by artificial intelligence and machine learning stands to change those dynamics. The ability to identify successful qualities and techniques for specific roles is just the beginning.
Analytics technology designed to boost employee performance can also match workers with the right career paths. As companies look for ways to maximize work quality, here are four things to know about analytics technology.
1. Managers Can Personalize Suggestions for Improvement
When it comes to job performance, leaders sometimes see evidence that employees are not meeting the mark. Subpar results are obvious if the position’s responsibilities include specific key performance indicators. Yet, it is not always clear why an individual is not meeting their goals.
Falling short can be frustrating for everyone involved, especially when an employee tries different remedies with little luck. Performance analytics programs use four dimensions to identify why someone is not achieving their targets: descriptive, diagnostic, predictive, and prescriptive analytics.
AI analyzes historical information and identifies relationships to make recommendations for improvement; for example, if two sales reps aren’t converting enough leads. Analytics technology can pinpoint why this is the case for each of them. Perhaps one rep isn’t contacting a sufficient number of prospects, while the other employee is failing to follow up sufficiently. Managers can offer more beneficial coaching by identifying individual reasons for performance problems.
2. Tech Centralizes Objective Sources of Truth
KPIs are intended to establish objective measurements of performance; however, supervisors are not immune to subjectivity when evaluating their direct reports. Leaders are also known to make hiring decisions based on biases.
For example, four in 10 hiring managers confessed to age bias in a 2022 survey. Consequently, subjective opinions may marginalize potential high performers or, conversely, confer what is known as a “halo effect.”
If a leader has a cognitive bias toward a direct report, they are more likely to positively assess the person’s work. With the halo effect, a supervisor may also dismiss the individual’s negative behaviour. Bias, such as the halo effect, can be more problematic in organizations that lack objective targets; however, even in companies with set KPIs, biases may cause supervisors to overlook their importance.
Data-driven technology makes non-subjective performance factors less deniable, streamlining the information. The information becomes centralized, more precise, and consistent. Analytics establish a single source of truth, from which supervisors can assess work outcomes at a glance. In addition, reporting features make it easy to track objective progress toward applicable goals.
3. Analytics Can Make Predictions About Job Candidates
Hiring the right person for a job is a complex decision. Leaders tend to be more familiar with internal candidates than external ones. Even so, hiring managers can’t always predict how successful a specific candidate will perform in various roles.
That’s a problem, as identifying people who are apt to excel in a position impacts more than just their performance in that one job. It could also determine how soon managers will have to go through the hiring process again.
That’s because turnover rates are lower among high-achieving employees. A 2021 Bureau of Labor Statistics report reveals (PDF) an average turnover rate of only 3% among top performers. That figure pales in comparison to the 57.3% overall turnover rate. Aggregate voluntary turnover is about 25%, but 29% comes from terminations. The wrong hire can lead to poor results, which may prompt those separations.
Fortunately, analytics programs incorporate predictive models to guide candidate selection processes. These models predict job fit based on data about a company’s current top performers. Hiring managers can be more confident in their decisions and more likely to avoid turnover expenses. Preventing mismatches between jobs and candidates also prevents losses from substandard work quality.
4. Companies Keep Control of Benchmarks
Many professionals have fears about AI taking over; they are anxious that the technology will replace their jobs. As with most solutions backed by artificial intelligence, though, analytics platforms won’t replace people per see.
Instead, the software will transform how companies function; it’s still up to humans to figure out what goals they want to strive toward. Leaders will continue to set the benchmarks, modifying them as the business needs of companies change.
For instance, say performance analytics technology reveals that employees’ productivity is dropping below acceptable levels. The platform shows that the decline is happening due to too much variability in workers’ shifts. One day, employees come in from 8 a.m. to 5 p.m., and the next from 5 p.m. to 11 p.m. To get productivity back on track, managers can rely on the guidance the data provides.
In response to the new intelligence, managers can adjust employees’ schedules so they are working more consistent shifts. Over the next few months, reports may reveal that productivity levels are climbing past pre-established benchmarks. AI may direct companies toward potential solutions to performance issues, but it won’t create the goals.
Taking Advantage of Data-Driven Tech
What drives individual employees to perform might be unique, but managers are responsible for the results their direct reports produce. Good or bad, employee performance impacts a company’s ability. Falling short of business objectives is something neither workers nor leaders want to see.
At the same time, pinpointing why someone did not meet expectations can be a mystery. Data-driven technology can help provide the answers. Whether the issue is insufficient training or a poor job fit, analytics reveal why. With this guidance, leaders can increase the accuracy of their recommendations, decisions, and adjustments.
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