marketr (version 0.0.2)

cxi_calc: Tidy Calculation of Customer Experience Index

Description

Simplifies the calculation of Customer Experience Index (CXi) from raw survey data within the tidyverse framework.

Usage

cxi_calc(survey_data, ..., cx_high = 4, cx_low = 2)

Arguments

survey_data

Raw survey data. Needs to be one row per survey with the three CXi question responses having column names of needs, ease and emotion

...

optional columns by which to group the CXi calculation. There is no limit to the number of grouping variables chosen. Too many will likely result in CXi calculations that are too fragmented / based on very small survey counts.

cx_high

Threshold in scale where response at or above is a "high"

cx_low

Threshold in scale where response at or below is a "low"

Value

Data frame with CXi and survey count for each combination of the grouping variables

Details

Customer Experience Index is a metric created by Forrester to help companies systematically measure customer experience in a way that their research has found is connected to improving customer loyalty. More information can be found at https://go.forrester.com/analytics/cx-index/

The calculation across an entire sample of surveys is simple. A customer experience manager may want to calculate CXi across many different dimensions and filtering in different ways; the functions in this package utilize the tidy framework to streamline calculating CXi along as many dimensions as desired.

Examples

# NOT RUN {
needs <- sample(5, 100, replace = TRUE)
ease <- sample(5, 100, replace = TRUE)
emotion <- sample(5, 100, replace = TRUE)
cx_date <- rep(seq.Date(from = as.Date("2019-01-01"), to = as.Date("2019-01-10"), by = "day"), 10)
cx_group <- rep(c("a", "b", "c", "d"), 25)
df <- data.frame(needs, ease, emotion, cx_date, cx_group)
cxi_calc(df, cx_group)
# }