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marketr
facilitates tidy calculation of popular quantitative marketing metrics (like Customer Experience Index and Net Promoter Score). By “tidy”, I am referring to the usage of the tidyverse packages and methodology for organizing and analyzing data. The package is designed so that beginning R users can calculate these metrics, along many dimensions, without needing to learn much R syntax. It is also helpful for more experienced programmers to do these calculations quickly.
To demonstrate the basic usage I will create simulated survey response data. needs
, ease
and emotion
are the columns that make up CXi; nps_question
is used for NPS; grps
and months
will show how these metrics can be calculated along categorical features and/or trended over time.
## Warning: replacing previous import 'vctrs::data_frame' by 'tibble::data_frame'
## when loading 'dplyr'
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr)
library(ggplot2)
needs <- sample(2:5, 1000, replace = T)
ease <- sample(2:5, 1000, replace = T)
emotion <- sample(2:5, 1000, replace = T)
nps_question <- sample(3:10, 1000, replace = T)
grps <- c("a", "b", "c")
months <- sample(1:12, 1000, replace = T)
survey_data <- tibble::as_tibble(cbind(needs, ease, emotion, nps_question, grps, months)) %>%
mutate(month = as.numeric(months))
## Warning in cbind(needs, ease, emotion, nps_question, grps, months): number of
## rows of result is not a multiple of vector length (arg 5)
## # A tibble: 6 x 7
## needs ease emotion nps_question grps months month
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 3 4 5 9 a 2 2
## 2 5 4 5 10 b 12 12
## 3 3 2 4 5 c 1 1
## 4 3 2 4 6 a 3 3
## 5 4 3 3 10 b 9 9
## 6 5 3 2 5 c 6 6
Customer Experience Index (CXI) was developed by Forrester. Per Forrester, CXi “measures how successfully a company delivers customer experiences that create and sustain loyalty.”
It involves scoring three questions, each with a likert scale response, and then averaging those scores together. Below, four calculations are done using two different functions.
cxi | survey_count |
---|---|
25.03333 | 1000 |
grps | cxi | survey_count |
---|---|---|
a | 25.74850 | 334 |
b | 25.72573 | 333 |
c | 23.62362 | 333 |
avg_survey_ct | min_survey_ct | month | cxi | survey_count |
---|---|---|---|---|
83.33333 | 71 | 1 | 23.52941 | 85 |
83.33333 | 71 | 2 | 23.07692 | 91 |
83.33333 | 71 | 3 | 21.17647 | 85 |
83.33333 | 71 | 4 | 27.10623 | 91 |
83.33333 | 71 | 5 | 22.22222 | 72 |
83.33333 | 71 | 6 | 36.25000 | 80 |
83.33333 | 71 | 7 | 21.46119 | 73 |
83.33333 | 71 | 8 | 32.65993 | 99 |
83.33333 | 71 | 9 | 35.68075 | 71 |
83.33333 | 71 | 10 | 12.76596 | 94 |
83.33333 | 71 | 11 | 15.98174 | 73 |
83.33333 | 71 | 12 | 28.68217 | 86 |
# Overall CXi trend by group - plotted
cxi_trend(survey_data, month, grps, cx_high = 4, cx_low = 2, min_surveys = 1, avg_surveys = 0) %>%
ggplot(aes(x = month, y = cxi)) +
geom_line() +
facet_wrap(grps ~ ., nrow = 3)
## Joining, by = "grps"
## Joining, by = "grps"
Net Promoter Score (NPS) was originally developed by Fred Reichheld and now is owned by Bain Company and Satmetrix Systems. The Wikipedia page is another good source of information. According to Wikipedia it “is a management tool that can be used to gauge the loyalty of a firm’s customer relationships.”
The calculation requires a single question with a ten-point scale. Like CXi it is not difficult to do manually; the package enables deeper analysis.Below, four calculations are done using two different functions.
nps | survey_count |
---|---|
-51.3 | 1000 |
grps | nps | survey_count |
---|---|---|
a | -45.50898 | 334 |
b | -57.35736 | 333 |
c | -51.05105 | 333 |
avg_survey_ct | min_survey_ct | month | nps | survey_count |
---|---|---|---|---|
83.33333 | 71 | 1 | -44.70588 | 85 |
83.33333 | 71 | 2 | -45.05495 | 91 |
83.33333 | 71 | 3 | -50.58824 | 85 |
83.33333 | 71 | 4 | -58.24176 | 91 |
83.33333 | 71 | 5 | -62.50000 | 72 |
83.33333 | 71 | 6 | -52.50000 | 80 |
83.33333 | 71 | 7 | -39.72603 | 73 |
83.33333 | 71 | 8 | -57.57576 | 99 |
83.33333 | 71 | 9 | -29.57746 | 71 |
83.33333 | 71 | 10 | -55.31915 | 94 |
83.33333 | 71 | 11 | -46.57534 | 73 |
83.33333 | 71 | 12 | -67.44186 | 86 |
# Overall NPS trend by group - plotted
nps_trend(survey_data, month, grps, min_surveys = 1, avg_surveys = 0) %>%
ggplot(aes(x = month, y = nps)) +
geom_line() +
facet_wrap(grps ~ ., nrow = 3)
## Joining, by = "grps"
## Joining, by = "grps"
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.