Description

The cohort analyzer answers a question that comes up repeatedly in steering meetings: are our variants meaningfully different, or are we counting noise? It clusters variants by structural similarity, exposes the differences in their performance, and supports a clean before-and-after view when interventions are introduced. The output supports decisions about consolidation, standardisation, or accepted divergence.

What is included

Structural clustering of variants with adjustable threshold
Side-by-side cohort comparison views
Before / after intervention comparison with audit trail
Statistical significance flag on every difference shown
Export of cohort definitions for reuse in BI tools

What you can expect from this engagement

01 A defensible decision on which variants to standardise versus accept
02 A measurable record of intervention impact, separated from baseline drift
03 A cohort taxonomy that downstream teams can reuse in their own analyses

Three questions buyers actually ask

How do you handle very small cohorts?

Cohorts with fewer than thirty cases are flagged as low-confidence and the comparison views display a warning rather than a confident verdict.

Can the clustering threshold be tuned per process?

Yes, and we recommend tuning it once with our consultant during onboarding rather than leaving it on default for every process.

What is the limit of the before / after comparison?

It cannot prove causation. It will show that performance changed; attribution to a specific intervention requires your own change control records.

What clients have said

The significance flag stopped two of my colleagues from announcing wins that were within noise. Cheap insurance.

Process improvement lead
Setup was straightforward. The cohort export is in a slightly awkward shape for Power BI but workable.

Bo-ram Jung , Senior Analyst · 4.3/5