Estimate a representative sample size for a customer segmentation proportion. Enter the eligible population, expected proportion, margin of error, confidence level, and expected usable-response rate. The calculator applies finite-population correction and reports both the minimum usable sample and the raw collection target, helping teams plan realistic data acquisition.
The result estimates how many independent, representative customer records are needed to estimate a segment proportion. It does not ensure that rare segments receive enough records.
Randomness and representativeness are assumptions. More records do not repair systematic exclusion, correlated observations, or poor measurement.
For 50,000 customers, a 50% expected proportion, ±5% margin of error, 95% confidence, and 85% usable-response rate, the minimum usable sample is 382 and the collection target is 450.
When no prior estimate exists, 50% produces the largest variance and therefore a conservative sample size for a proportion.
No. Use stratified sampling or segment-specific minimums when small but important groups must be analyzed separately.
Examples include missing required features, unresolved duplicates, invalid consent status, or records outside the eligible population.
It is a precision calculation for estimating proportions, not a universal machine-learning training-size rule; model complexity may require more data.
Once the population is much larger than the sample, the finite-population correction approaches one and adds little reduction.
| Variable | Meaning |
|---|---|
| N | Eligible population size |
| p | Expected population proportion |
| e | Desired margin of error |
| z | Critical value for confidence level |
| Usable rate | Expected valid share of collected records |