Attrition patterns
Where turnover concentrates — by team, tenure, manager, role or location — and how that has moved.
AI Capabilities & Services
Human Resources Analytics
Workforce insights on attrition, performance, and planning.
The problem
HR data is usually complete and almost never used. Turnover is reported as a rate long after the people have gone, hiring decisions are made on gut feel because the evidence takes a week to assemble, and nobody can answer which teams are one resignation away from a delivery problem. The analysis is not technically hard. What makes it hard is doing it without drifting into surveillance or automated judgement about individuals.
Who this is for
- HR directors and people teamsEvidence for the workforce decisions they are already being asked to justify
- Business-unit leadersTo see a retention risk in their own team while it can still be addressed
- Finance and workforce planningHeadcount and cost planning grounded in patterns rather than in last year plus ten per cent
What people use it for
Hiring funnel analysis
Where candidates are lost, how long each stage really takes, and which sources produce people who stay.
Capability and skills mapping
What the organisation can actually do today against what it has committed to deliver.
Workforce planning
Headcount, cost and capability projected from observed patterns, with the assumptions visible.
What it needs to work
- HRIS records: role, tenure, movement, structure — the fields you already maintain
- Recruitment data from your ATS, including outcomes rather than only applications
- Engagement or survey results where they exist, at aggregate level
- A clear position from HR and legal on what may be analysed and at what granularity
How it works
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Consolidate
HRIS, ATS and survey data joined on a consistent definition of a person, a role and a period.
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Analyse at group level
Patterns by team, tenure band, role family and location — never as a score attached to a named individual.
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Surface the drivers
Which factors travel with turnover in your organisation, stated as association rather than as proven cause.
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Test for fairness
Checking that a pattern is not simply reproducing a bias already present in past decisions, before anyone acts on it.
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Put it in front of the manager
Team-level views for the person who can change something, with guidance on what the number does and does not support.
How we deliver it
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Scope and ethics review
What questions are in scope, at what granularity, and what is explicitly excluded — agreed with HR and legal before data moves.
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Data consolidation
One model of people, roles and movement, which is usually the first time the organisation has had one.
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Analysis and fairness testing
The questions answered, with the fairness checks run and reported alongside rather than afterwards.
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Dashboards and enablement
Views for HR and for line managers, with training on interpretation — this is where misuse is prevented.
Where it runs
- On-premises or private cloud — HR data rarely leaves the organisation
- Role-restricted access, with managers seeing only their own structure
- Aggregation floors, so a small team cannot be de-anonymised by filtering
Security and governance
- No automated employment decisions — this informs people, it does not decide about them
- Minimum group sizes enforced before any figure is displayed
- Fairness testing across protected characteristics, reported not buried
- Purpose limitation agreed in writing, and access reviewed periodically
Timeline
Consolidation is the bulk of the work, because HRIS, ATS and survey systems rarely agree on what a role or a start date is. The analysis that follows is fast. The ethics scope review comes first and is not a formality — it is what keeps the project on the right side of a line that is easy to cross by accident.
What you receive
- A consolidated people data model with documented definitions
- Attrition, hiring and capability analysis at group level
- Fairness testing results, reported alongside the findings
- Dashboards for HR and line managers, with aggregation floors enforced
- An interpretation guide stating what each measure does not support
What this does not do
We do not build individual attrition scores, performance predictions or automated screening. Those are the applications most likely to be unfair, hardest to defend, and most damaging to trust when staff discover them — and staff always discover them. Analysis here is at group level and informs human decisions; where you want something else, we will say no and explain why.
Questions we are asked
Can you predict who will resign?
We can show you where resignation concentrates and what travels with it. We will not produce a per-person flight-risk score: it is unreliable at the individual level, it changes how managers treat people, and it is very hard to justify if it becomes public.
Will individuals be identifiable?
Not through the analytics. Aggregation floors are enforced in the data layer, so a manager cannot filter a team of four down to one person.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
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From the floor to the boardroom
Human Resources Analytics
Workforce insights on attrition, performance, and planning.