Net Promoter Score

Computes NPS from a 0-10 recommendation question. The score is % promoters - % detractors, equivalently 100 * mean(nps_group(x)) over the signed -1/0/1 coding (see nps_group()).

Usage

calc_nps(data = NULL, value, by = NULL, weights = NULL)

Arguments

data

A data frame.

value

The 0-10 recommendation column (unquoted).

by

Optional grouping column(s); see calc_percentage().

weights

Survey weighting: NULL (default) uses the session scheme from set_weights() if set; FALSE forces unweighted; or pass an ad-hoc scheme. When weighting is active nps is the weighted score (n stays the unweighted base).

Value

A tibble with n (valid responses), nps (an integer from -100 to 100), the three group shares (pct_detractors, pct_passives, pct_promoters) and the counts behind them (detractors, passives, promoters), one row per group when by is given. The shares come first because they are what a report quotes.

Details

Respondents are bucketed by nps_group() into detractors (0–6), passives (7–8) and promoters (9–10); the score is the percentage of promoters minus the percentage of detractors, which equals 100 * mean() of the signed -1/0/1 coding. Missing or out-of-range scores are dropped before the mean. Text answers are coerced with ensure_numeric(). Use plot_nps() for the full 0–10 distribution chart and plot_nps_gauge() for a single-number gauge.

The three group counts and shares are reported alongside the score because a single NPS hides how it was reached: +20 from 40% promoters and 20% detractors is a different picture from +20 with 25% and 5%. Counts are always unweighted respondent counts; the shares follow nps, so they are weighted when a weighting scheme is active. All three shares and nps are rounded to whole numbers independently, so pct_promoters - pct_detractors can land one point away from nps; the identity holds exactly before rounding.

See also

nps_group(), plot_nps(), plot_nps_gauge().

Other modelling: calc_importance(), ipm_model()

Examples

calc_nps(podracing_survey, nps_value)
#> # A tibble: 1 × 8
#>       n   nps pct_detractors pct_passives pct_promoters detractors passives
#>   <int> <dbl>          <dbl>        <dbl>         <dbl>      <int>    <int>
#> 1  1000     7             25           43            32        252      426
#> # ℹ 1 more variable: promoters <int>

calc_nps(podracing_survey, nps_value, by = region)
#> # A tibble: 5 × 9
#>   region            n   nps pct_detractors pct_passives pct_promoters detractors
#>   <chr>         <int> <dbl>          <dbl>        <dbl>         <dbl>      <int>
#> 1 Asia             59    25             19           37            44         11
#> 2 Europe          314     7             23           46            30         73
#> 3 Latin America    82    12             21           46            33         17
#> 4 North America   503     4             28           40            32        141
#> 5 Oceania          42    10             24           43            33         10
#> # ℹ 2 more variables: passives <int>, promoters <int>