- H2O Driverless AI finds the factors most linked to attrition, such as workload, pay, growth, commute and work-life balance, from survey, exit-interview and HR data.
- Results are reported as patterns by role, team and tenure, to help HR improve conditions for groups of people, not to single out individuals.
- Every finding comes with reason codes, so HR leaders can see what drives it and test it against what employees say.
- An agent can turn the analysis into a plain-language summary and a retention action plan for HR leaders to review.
Understand why good people leave, before it's too late
In many organizations, certain roles and teams see disproportionately high turnover. Each departure means recruiting, training and weeks before a new colleague is fully productive, and customers or teammates notice the gap.
The reasons are usually already in your own data. Engagement surveys, exit interviews and HR records hold them, but they sit in different places and get read one report at a time, often after people have already gone.
Employee data also needs care. Any analysis has to respect your privacy policies, employment law and the trust of the people it describes.
How employee retention analysis works
Your HR analytics team builds models on the data your policies already allow you to use, and HR leaders act on the findings, not on the raw scores.
Step 1: agree the data and the guardrails
Start with the data your policies permit: engagement survey scores, exit-interview themes, role, tenure, pay band and working patterns. Agree up front with HR, legal and employee representatives how results will be used, who sees them and the minimum group size for any report.
Step 2: learn what is linked to leaving
H2O Driverless AI trains classification models on who stayed and who left in past periods. Automatic feature engineering tests many combinations of factors, so the analysis reflects your workforce rather than a generic checklist.
Step 3: explain every finding
Reason codes and Shapley values show which factors matter most for each role and team, so a finding such as long commutes and low work-life balance scores in service roles gives HR something concrete to act on.
Step 4: forecast attrition by group
Forecast expected attrition rates by role, location and tenure band for the next quarter and year, so workforce planners can hire and train ahead of need instead of reacting to vacancies.
Step 5: plan and review actions
Use the findings to design changes: schedules, career paths, pay reviews, manager support or training. An agent can turn the analysis into a plain-language summary and action plan for a person to review, and you measure the effect in the next survey cycle.