The core idea
Segmentation groups observations to answer a question: for example by role, location, contract arrangement, tenure or relevant experience. It can reveal a pattern hidden by a company-wide average. The grouping is an analytical choice, not a statement that all people in a group are alike. Define segments before looking for a desired result where possible, and distinguish legitimate analysis of unequal outcomes from using sensitive characteristics to determine individual opportunities.
Source and attribution [1]Using it in practice
Start with the decision and select a small number of plausible groupings. Show group sizes, numerator and denominator alongside rates. Check whether differences reflect job mix, time at risk or data quality. Avoid publishing small or easily identifiable groups; a single fixed minimum group size is not a complete disclosure-risk assessment. Review the purpose, access and lawful handling of personal data with appropriate specialists.
An example, not a reported case
Worked example · illustrative
In an invented organisation, Site A has eight voluntary leavers from an average workforce of 80 and Site B has six from 30: 10% and 20% respectively for the same period and definition. Raw counts alone would point to Site A. HR then checks whether contract mix and tenure differ before blaming Site B’s manager. Interviews and scheduling evidence help identify which explanation deserves testing.
What to watch for
Many comparisons increase the chance of finding an apparently unusual group by accident. Segments can act as proxies for protected characteristics. Performance ratings may themselves contain bias. Do not label a small team a hotspot on the basis of one unstable percentage or publish combinations of filters that reveal individual records.
Choose groups for a reason
Write a sentence explaining why each grouping could help answer the question. Role and tenure may be relevant to onboarding; they may be irrelevant to another problem. Resist adding every available field. A narrower analysis is easier to interpret and easier to govern.
Check the denominator
A group with more people usually has more events. Compare rates over equivalent periods and report counts. If exposure differs substantially, consider an appropriate person-time measure or a time-to-event approach. Explain the choice in language the decision-maker can understand.
Investigate composition
A site’s overall rate may change because its mix of roles or tenure changed. Compare like groups and inspect whether a company-wide pattern reverses within groups. Do not assume that adjusting for a few recorded variables removes every alternative explanation.
Turn the finding into a fair action
Use a pattern to improve a process or investigate a hypothesis, not to attach a negative label to everyone in a segment. Agree how the intervention will be evaluated and check whether it creates unequal access or unintended burdens elsewhere.
Take it into your next conversation
Three useful questions.
- Which decision will this analysis change?
- Are the population, denominator, time period and assumptions explicit?
- What alternative explanation or unintended effect must we check?
Related terms
Go to the evidence
Sources & attribution
[1] CIPD: Employee turnover and retention ↗
The core idea is an original summary of the cited work. Application notes, examples and sketches are our interpretations, not quotations or reproductions of the authors’ figures. Publisher records may require access to read the full original work.
Published 2026-09-20 · Reviewed 2026-09-20. Editorial approach