The core idea
A cohort is a group defined by a shared starting event or period, such as joining in a particular quarter. Cohort analysis follows comparable groups over equivalent periods. It can reveal early-tenure problems that a company-wide snapshot hides. A recent cohort is not automatically comparable with an older one because its members may not yet have reached the outcome window. The grouping and outcome definition should be chosen to answer a clear question.
Source and attribution [1]Using it in practice
Define the cohort, starting date, outcome and observation window. Include only fully observed cases in a simple fixed-window comparison, or use an appropriate method for incomplete follow-up. Show exclusions and counts. Check whether the cohorts differ in role, site, season or recruitment route. A change across cohorts is a finding to investigate, not proof that the latest onboarding initiative caused it.
An example, not a reported case
Worked example · illustrative
An invented January cohort has 100 starters, with 80 still employed at day 90: 80% retention. An April cohort has 100 starters and 88 retained at day 90: 88%. The difference is eight percentage points, or a 10% relative increase from 80%. If a July cohort has only 30 days of follow-up, its current 95% retention cannot be compared as a 90-day result.
What to watch for
Define how rehires, transfers, contract endings and missing dates are handled. Excluding incomplete cases can itself select a different population. If follow-up varies substantially, consider survival analysis. Avoid revealing small cohorts or treating a cohort label as a judgement about its members.
Build a cohort table
Use rows for joining periods and columns for elapsed time, such as 30, 60 and 90 days. Leave unobserved cells blank and label them clearly. Do not fill them with zero or carry forward a short-term result as if it were complete.
Interpret the difference
Report the starting number, retained number, rate and time window. Investigate changes in work availability or role mix. If the decision matters, include uncertainty and a suitable comparison design rather than relying on two percentages alone.
Take it into your next conversation
Three useful questions.
- What question can this method answer, and what can it not establish?
- Are the comparison, observation period and assumptions defensible?
- What decision follows, and how will its consequences be reviewed?
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