How to Stop Staff Turnover and Solve Workforce Challenges

Bridge the Gap between HR and Ops

To build the workforce that will deliver the outcomes that matter most, leaders from Human Resources and Operations need to get aligned around — you guessed it — outcome data.  Whether challenged by staff shortages, turnover, or engagement, the only way to build the workforce that will take you strongly into the future is through cross-group collaboration, fully supported by data-informed conversations. 

When Ops leaders monitor and measure essential outcomes alongside their HR and talent acquisition colleagues, workforce decisions can incorporate — and generate — the desired outcomes.

The alternative approach is what we have always known: silo-ed departments. HR hires in a vacuum for a theoretical ideal and Ops folks realize later that a new hire is not working out. Or new hires arrive at that realization themselves and, within a few months or weeks, quit the organization.

The Misalignment Problem

Sometimes misalignment between HR and Ops stems from conflicting goals.  Take this global data analytics company: They recently tasked their engineering department with delivering a new product by the end of the quarter. Over that same timeframe, they asked the HR department to increase engineering headcount by 150%. 

From the engineer’s perspective, every minute spent interviewing candidates and onboarding new employees represents one less minute spent on the current project.  From HR’s perspective, every minute spent waiting for an engineer to meet with candidates increases the likelihood that their “in-demand engineering candidate” will move on and accept an offer elsewhere.  

More often, however, misalignment comes from breaking down shared goals along fractured lines.  HR and Ops can agree, for example, that they share responsibility for addressing staff turnover.  HR does their part by finding the ‘best’ person for each position, and Ops does their part by retaining these ‘right’ hires.  “We find ‘em, you bind ‘em,” is the oft-repeated talent acquisition mantra.

However, if the definition of ‘best’ and ‘right’ is based on a theoretical ideal and not actual outcome data, then hiring will miss the mark.

Talent acquisition professionals seek job candidates who will succeed and be retained.  They identify the most qualified, most committed-to-the-craft, and most mission-driven applicants.  But a candidate who may be great (in theory) for a position, may not thrive in the on-the-ground realities of that specific organization. Or they may thrive in one department, under a specific manager, but not in a different department or with a different team.  

When a new hire fails to perform, or show up, for the job within a few months’ of being hired, the operational leaders turn back to their HR counterparts with an expectation that they should ‘do better’ in finding someone. “Find us someone who can actually succeed on the job here.”

To break a turnover cycle, organizations must first break the “We find ’em, You bind ’em” cycle.

To break this turnover cycle, organizations must first break the “We find ’em, You bind ’em” cycle. HR and Ops must come together to look at the outcome data, share their different perspectives, posit explanations and solutions, and agree on new approaches. It’s not a hand-off of idealized job candidates. It’s an examination of the candidates who can succeed and scaling that understanding so people get matched more effectively to specific positions.

Data-Informed Insights: The short cut to cross-team collaboration

Bringing HR and Ops together for ongoing, data-focused conversations can re-direct hiring efforts away from the theoretically good people and toward hiring what’s measurably and actually good.  In-house data analytics teams can create various reports like the ones described below to support these discussions.

Fortunately, a machine learning platform fed by outcome data — like the core Arena Analytics platform — can provide straightforward insights on each person’s likelihood of producing specified outcomes at specific jobs.  Rather than build these models in-house and then work through data reports in long meetings, teams can leverage the actual data insights, and inform their decisions before matching people to positions, promotions, and re-assignments. Contact us to explore how our data insights can bring your teams together so you can reduce turnover and build the workforce that will take you into the future.

Solving New Hire Turnover with Data Analytics

To build the analytics backbone in-house, begin with these reports:

  • Milestone turnover
  • Remaining tenure inflection points
  • Turnover trends by prediction scores

Let’s start with an actionable definition of turnover. The simple employee turnover metric we see every day is far too broad to yield any clear follow-up actions. 

When you unpack turnover data, however, conversation shifts and targeted solutions arise.

Take this one view of turnover data.  Of the total number of people who left in a given time period, what percent were employed less than 3 months, 3-6 months, 2-5 years?  Now imagine this same pie chart broken down by job function, by department, by location.  If the largest percentage of turnover occurs within 3 months of being hired, the problem may lie in the initial hiring decisions.  

Re-hiring repeatedly for the same role, several times over the course of the year costs far more than re-hiring every 5 years, or even every 2 years. A focus on improving the short tenure rates in red may yield the largest savings and have the greatest impact on staff morale. We call this defined tenure timeframe a Milestone-Adjusted Turnover Rate. The image below shows how you calculate a 3-Month (or 90-Day) milestone turnover rate by following a cohort of hires to see how many leave within 3 months of their hire date.

Remaining tenure inflection points

Another view of turnover looks at retention. This chart below shows the Median Remaining Tenure for same position, at different locations at a healthcare system.  The x-axis tracks each day a person works the job, while the y-axis shows the average (median) remaining days this employee stays on the job.  For the red line, the downward trend reveals a steady decrease in tenure. As each day passes, there are fewer days remaining. The blue line shows the opposite, the longer someone stays, the longer they will continue to stay.

Collaborative discussion can explore reasons behind these differences and ways to improve the red line location’s retention. Follow up meetings can continue to examine how these trends shift in response to new policies, procedures, and people.  Below is an example for a different position and place of work where retention does not trend up or down. Instead, there are strong inflection points. Armed with this insight, employers can step in at Day 100 and then again Day 200 to address pending dissatisfaction before it fully develops.

Turnover trends by prediction scores

Arena Analytics’ machine learning platform encapsulates the analytics above and many more data points to create data pictures of each individual.  With 10+ years of outcome data and ongoing data feeds from each client, Arena can predict whether or not new candidates (or existing staff) will fall into the red or green lines below for each open job.  Operational and HR leaders can then look at the prediction scores and move away from gut checks and theoretically ‘good’ behavioral traits.  By closing the gap between HR and Ops with data, organizations can focus on achieving their greater mission – quality care, excellent service, and optimal revenue.