Who's Likely to Quit

James ran HR for a tech company with 250 employees. Every resignation disrupted projects and cost money—recruiting, interviewing, onboarding. But he never saw them coming. People quit suddenly, often his best performers. He needed a way to spot resignation risk before employees walked out the door.

James hired a data analyst to build a prediction model using employee records. The analyst examined tenure, salary, age, experience, location, bench status, education, and gender. The goal was to identify which employees were flight risks so James could intervene before they resigned.

The model was statistically sound. It explained 12.1% of resignation variation—not huge, but acceptable for predicting human behavior. All factors were statistically significant. The patterns made sense: benched employees quit more often, longer tenure meant lower risk, higher pay increased retention. The model reflected reality.

The model's accuracy was 85.67%—good enough to guide real decisions. It correctly identified 36 actual quitters. Precision was 56%, recall 47%, F1 score 51%. The model wasn't perfect, but it gave James a fighting chance to focus retention efforts where they mattered most.

The data revealed stark gender differences. Female employees had a 76% chance of quitting compared to 55% for males—a 21-point gap. James realized he needed gender-specific retention strategies: flexible work options, career development support, better work-life balance for women.

Age mattered too. Employees 25 and under had a 69% quit probability. Older employees dropped to 50%. Young workers were flight risks—they lacked roots, explored options, changed jobs easily. James needed stronger onboarding, mentorship, and clear advancement paths for early-career staff.

Tenure was the strongest predictor. Employees with one year of service had a 72.73% chance of quitting. Those with 15 years dropped to just 15.87%. The first few years were critical. If James could keep someone past year three, they'd likely stay much longer. Retention efforts needed to concentrate on new hires.

Domain experience and salary both mattered. Employees with no domain experience had a 67% quit probability versus 30% for those with 10 years. Tier 3 (lowest paid) employees had a 64% quit risk compared to 40% for Tier 1. Underpaid, inexperienced workers were most likely to leave.

Benched employees—those waiting for project assignments—had a 65.54% quit probability compared to 48.33% for actively assigned workers. Being benched signaled career stagnation. James realized he needed to keep benched employees engaged through training, temporary projects, or faster reassignment.

The model created risk profiles. Highest risk: a young, low-paid employee in Pune with no domain experience and benched status—88% probability of quitting. Lowest risk: a senior female employee in New Delhi with long tenure, high salary, domain expertise, and active assignment—18.73% probability.

James restructured his retention approach. He focused on three groups: early-career employees (mentorship programs), Tier 3 staff (compensation reviews), and benched workers (active engagement). He stopped treating retention as a blanket policy and started targeting interventions at high-risk segments. He couldn't save everyone—the model told him that—but he could focus resources where they'd have the most impact.

James uses the model quarterly to generate risk scores for all employees. Managers get lists of high-risk staff on their teams. HR schedules conversations with at-risk employees to understand concerns and offer solutions. The model doesn't prevent all resignations—people still quit for reasons the data can't see—but James catches more issues early. He's not reacting to resignations anymore. He's anticipating them.