AI‑powered SaaS predicts who is likely to leave, why, and what actions reduce churn by combining engagement, HRIS, and behavioral data into explainable attrition risk insights for leaders and managers. The strongest stacks pair continuous listening and intent‑to‑stay signals with prescriptive playbooks, internal mobility, and skills analytics to retain critical talent before resignation intent turns into exits.
What it is
- Platforms analyze survey scores, “intent to stay,” lifecycle metrics, and HR events to score populations or cohorts on future turnover risk and surface top drivers to address.
- Models are trained on aggregated turnover outcomes and validated at scale, offering precision thresholds (for example, ≥80% precision on elevated risk flags in Viva Glint).
- Workday Peakon Employee Voice + Illuminate
- Forecasts turnover risk from continuous listening and lifecycle metrics, pinpointing drivers and prescribing actions managers can take in real time.
- Microsoft Viva Glint
- Attrition Risk Index flags cohorts with elevated voluntary attrition risk and ties risk to specific item scores with documented precision and alert controls.
- Culture Amp Retention Insights
- Predicts and prevents turnover with analytics centered on commitment/“intent to stay,” surfacing motivators and early warning signals for high performers.
- Visier People Analytics
- People analytics and “flight risk” modeling to find high‑impact roles at risk and target interventions where churn hurts most.
- Eightfold AI
- Talent intelligence linking skills matching and mobility to retention, with research tying higher match scores to improved post‑hire retention.
- UKG Pro (Workforce Intelligence)
- Predictive analytics in the HCM suite to anticipate attrition, identify at‑risk groups, and guide proactive retention strategies.
How it works
- Sense
- Ingest engagement items (e.g., eSat, recommend, intent‑to‑stay), HRIS events, and demographics to build population‑level risk profiles.
- Decide
- Predictive models flag elevated risk cohorts and rank the top experience drivers to address; some tools prescribe manager actions by team.
- Act
- Trigger alerts, manager dashboards, and targeted programs (coaching, development, workload changes), linking to mobility and learning where relevant.
- Learn
- Track outcomes and refine models and driver maps as engagement and turnover change across cycles.
High‑value use cases
- Early warning for hotspots
- Use alerts and precision‑backed risk flags to prioritize functions, locations, or tenures where voluntary churn is set to spike.
- Driver‑based action planning
- Convert “intent to stay” and driver analysis into manager playbooks and team‑level actions with continuous listening.
- Retaining high performers
- Surface early signals and motivators for top‑rated employees and deploy targeted retention moves and career steps.
- Skills‑based mobility
- Reduce regrettable exits by matching at‑risk employees to internal roles aligned to their skills and adjacencies.
30–60 day rollout
- Weeks 1–2
- Enable cohort‑level attrition risk (Viva Glint or Peakon) and standardize core survey items including “intent to stay.”
- Weeks 3–4
- Launch manager dashboards with top drivers and prescribed actions; pilot quarterly action‑planning in two functions.
- Weeks 5–8
- Add high‑performer and critical‑role lenses, align mobility/learning pathways for at‑risk groups, and define escalation rules for hotspots.
KPIs to track
- Precision and coverage
- Share of headcount covered by risk models and precision of elevated‑risk flags per platform guidance.
- Driver impact
- Movement in key drivers (e.g., growth, manager support) after action plans and corresponding changes in attrition.
- Regrettable turnover
- Reduction in high‑performer or critical‑role churn in pilot groups vs. baseline.
- Save actions and time‑to‑intervention
- Count of targeted interventions (role changes, development, pay adjustments) and time from alert to action.
Governance and trust
- Explainability
- Favor tools that disclose which items and attributes contributed to a cohort’s risk and show the evidence behind alerts.
- Signal design
- Include “intent to stay” given its strong correlation with turnover, and keep item sets concise and validated.
- Privacy and fairness
- Limit attributes in alerts to those necessary (tenure, hierarchy) and avoid over‑granular or sensitive fields; monitor bias in actions taken.
- Manager enablement
- Pair insights with actionable, science‑backed playbooks and track completion to ensure follow‑through.
Buyer checklist
- Proven attrition‑risk modeling with precision metrics and configurable alerts.
- Continuous listening and driver analysis tied to prescriptive actions for managers.
- High‑performer and critical‑role lenses with early warning signals and motivators.
- Integrations to HCM, learning, and mobility to operationalize saves and track outcomes.
Bottom line
- Retention programs work best when validated attrition risk models, driver‑based actioning, and skills‑based mobility operate together—giving leaders early warnings and managers concrete steps that measurably reduce regrettable churn.
Related
Which SaaS platforms offer AI models that predict individual flight risk
How do Workday Peakon and Glint differ in their attrition prediction methods
What input data do these tools require to forecast employee turnover
How accurate are real‑time attrition risk scores like Glint’s 80% precision
How can I integrate AI retention insights with my existing HCM systems