How is NPS calculated?

NPS is the percentage of Promoters minus the percentage of Detractors. Scores of 9 to 10 are Promoters, 7 to 8 are Passives, and 0 to 6 are Detractors. The tool applies this exact formula to the generated rows.

What does the sentiment skew do?

It biases the random score distribution. A positive skew shifts more responses toward 9 and 10, raising NPS; a negative skew pushes toward low scores. Zero gives a roughly even spread.

Are the comments meaningful?

Each comment is drawn from a pool matched to the response category, so promoters leave positive notes and detractors raise complaints. This keeps the dataset internally consistent for demos.

What columns does the CSV have?

id, score, category, segment, and comment. Comments are quoted and any internal quotes are escaped, so the CSV imports cleanly into spreadsheets and analytics tools.

NPS Survey Data Generator

Realistic NPS data for dashboards

NPS analytics tools, customer-feedback platforms, and survey dashboards all need example response data that behaves correctly: scores in the right buckets, a properly computed Net Promoter Score, and comments that match each respondent’s sentiment. This tool generates exactly that, with a sentiment dial and CSV export.

How it works

A seed drives a mulberry32 pseudo-random generator so datasets are reproducible. Each response gets a 0 to 10 score, then a category:

Promoter: score 9 or 10
Passive: score 7 or 8
Detractor: score 0 to 6

The headline metric is the real formula:

NPS = (% Promoters) - (% Detractors)

The sentiment skew nudges the random distribution toward higher or lower scores, and each comment is pulled from a pool matched to its category so the data stays believable.

Tips and notes

Use a positive skew to model a beloved product and a negative skew to model a struggling one, then verify your dashboard renders the NPS sign and color correctly in both cases. The CSV escapes embedded quotes, so it imports cleanly. Keep the seed fixed when you want a deterministic fixture for automated tests.