How do you build a useful customer segmentation?
A useful segmentation connects admissible variables, distance measure, algorithm, stability and decision use. Groups are not natural essences but a conditional representation.
Direct answer
Build an actionable descriptive partition and document its uncertainty.
A useful segmentation connects admissible variables, distance measure, algorithm, stability and decision use. Groups are not natural essences but a conditional representation.
argmin Σk Σi∈Ck ||xi−μk||²ESTIMAND
Estimation profile
- Unit of analysis
- Customer × decision date × horizonControlled scientific terminology
customer × feature window - Exact estimand
Stable, interpretable partition for a declared use- Model or deliverable
argmin Σk Σi∈Ck ||xi−μk||²
01—08
Verifiable analysis framework
- 01Question
How do you build a useful customer segmentation?
- 02Estimand
Stable, interpretable partition for a declared use · customer × feature window
- 03Data
customer_id · feature_window · behavioral_features · eligibility
- 04Model or deliverable
argmin Σk Σi∈Ck ||xi−μk||²
- 05Declared calculation
argmin Σk Σi∈Ck ||xi−μk||² → Stable, interpretable partition for a declared use
- 06Uncertainty checks
Scaling · Missingness · Stability · Actionability · Fairness
- 07Method-specific validation
Check data, estimate stability and interpretation limits. Scaling · Missingness · Stability · Actionability · Fairness
- 08Limitations
Claim that segments are true, permanent or causally distinct.
Required variables
customer_idfeature_windowbehavioral_featureseligibility
Checks · Controlled scientific terminology
- Scaling
- Missingness
- Stability
- Actionability
- Fairness
Build an actionable descriptive partition and document its uncertainty.
Claim that segments are true, permanent or causally distinct.
Scientific sources
3 sources
Method connections