Workforce & HR · Attrition prediction
You find out who is leaving when they hand in notice
Attrition prediction turns the resignation letter into a problem you saw coming. Banao builds models that score your whole workforce weekly — drawing on tenure, compensation history, performance, survey responses, and role-change patterns — and return a ranked flight-risk list with a reason against each name.
HR and line managers get the signal while intervention is still possible, not after the headcount gap is open and the three-month backfill clock has started.
The first call is free · 45 minutes · no obligation
What we build
What a Banao attrition model delivers
A risk score without context sends a manager nowhere. Every deliverable below is built to produce an action, not a number.
Weekly flight-risk scoring across the whole workforce
The model runs on a cadence against your full headcount — not a quarterly survey, not a manual HR review — so emerging risk shows up before it crystallises into an intent to leave.
Driver attribution on every flagged employee
Each risk score carries the dominant driver: compensation lag, tenure milestone, recent role change, manager transition, disengagement signal. Managers see why, not just who.
HRIS and engagement data integration
Banao integrates with your existing HRIS, payroll history, performance records, pulse surveys, and calendar data — the model is built on signals you already hold, not a new data-collection project.
Manager-facing risk digest
Direct line managers receive a weekly digest of their at-risk reports, with the driver and a suggested intervention category — so the conversation starts before the resignation, not after.
HR threshold alerting and escalation
HR receives an escalation when a high-tenure or business-critical employee crosses the risk threshold — with enough lead time to act on it, not just to record it.
Cohort and team analysis for CHRO reporting
Which teams, tenure bands, and role families carry the highest aggregate risk, and how that is trending — the view that makes attrition a board-level metric rather than a series of individual surprises.
Dogfooding
We run workforce AI on our own operation before it reaches yours
Banao is a ~300-person engineering company with a continuous hiring pipeline, competitive talent markets across Bangalore and Chandigarh, and a retention problem every engineering organisation recognises. We design workforce AI against our own real constraints before asking a client to depend on it.
InterviewGod screens our own engineering hires every week. Workforce analytics tools run on our own headcount data during development. A model that has to survive our own people operations is not the same as one tested on a sanitised demo dataset.
Screens Banao's own engineering hires before any human panel time is spent.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When attrition prediction is not the right move
Prediction is only useful if it produces actions. We will tell you before you build one if the conditions are not right:
- Thin or unreliable HR data: if your HRIS is incomplete, survey participation is below forty per cent, or performance records are patchy, the model will score noise. We map data quality before we commit to a build.
- No intervention capacity: a risk list handed to managers who have no budget, time, or authority to act is an anxiety generator, not a retention tool. The intervention workflow is part of the project scope, or we do not build the prediction layer.
- High-churn roles by design: in roles where thirty-per-cent annual turnover is budgeted and expected, prediction adds little — the cost of retention exceeds the cost of replacement. We will tell you which roles are worth modelling.
How we start
How we start — with your data, not a pitch deck
We look at your actual HR data quality and turnover patterns first, before we commit to a scope.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your HRIS completeness, turnover history, and available signal sources, identify which employee cohorts carry predictable versus random attrition, and return a data-quality assessment, model feasibility verdict, and ROI estimate — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
HRIS integration, feature engineering from your real signal sources, model training on your historical turnover, and the manager and HR-facing interfaces — all built and tested on your data before go-live.
- 03
Production & monitoring
Weekly scoring runs, alert routing to managers and HR, a CHRO-level cohort dashboard, and model monitoring so accuracy stays calibrated as your workforce changes rather than drifting.
FAQ
Frequently asked questions
What data does the model need to get started?
The core inputs are HRIS records (hire date, role history, compensation changes, manager history), performance data, and any engagement or survey signals you collect. The Discovery Sprint maps what you have against what the model needs — we build with available signal and flag where data gaps limit accuracy.
Can the model explain why someone is flagged, not just that they are?
Yes. Driver attribution is part of every score: the model identifies whether the dominant signal is compensation lag, a tenure milestone, a recent manager change, a performance pattern, or an engagement drop. Managers need a reason to have a conversation — a number alone produces no action.
How do we handle the ethical and legal concerns around scoring employees for flight risk?
Governance is part of the build, not an afterthought. Scores are used only for proactive retention conversations, not disciplinary or redundancy decisions. We build the access model, audit log, and policy framework in from week one — and ensure the system can be explained to any employee who asks.
How long until predictions are accurate enough to act on?
That depends on your historical turnover volume and data quality — which is exactly what the Discovery Sprint establishes. As a benchmark: with two or more years of clean HRIS history and reasonable survey coverage, a first model run is typically actionable within the build window.
Does this work for companies outside India?
Yes. Banao works with clients across India, the GCC, and the UK. Data residency, privacy law (GDPR, PDPA, DPDP), and local labour regulations are scoped at project start — not retrofitted after build.
Get started
Find out whether your attrition is predictable before you build a model
In 45 minutes we will map your HR data against what attrition prediction needs — and tell you whether the signal is there to build on.
Book a Discovery Sprint