Introduction to marketr

Introduction to marketr

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.

Generate survey response data

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.

library(marketr)
library(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)

head(survey_data)

## # A tibble: 6 x 7
##   needs ease  emotion nps_question grps  months month
##   <chr> <chr> <chr>   <chr>        <chr> <chr>  <dbl>
## 1 5     2     5       4            a     6          6
## 2 5     5     4       3            b     11        11
## 3 4     3     2       6            c     10        10
## 4 4     4     3       9            a     7          7
## 5 2     3     5       4            b     3          3
## 6 3     5     4       10           c     9          9

Calculating CXi

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.

# Overall CXi
cxi_calc(survey_data) %>% knitr::kable()

## CXi by group
cxi_calc(survey_data, grps, cx_high = 4, cx_low = 2) %>% knitr::kable()

# Overall CXi trend
cxi_trend(survey_data, month) %>% knitr::kable() 

# 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"

Calculating NPS

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.

# Overall NPS
nps_calc(survey_data) %>% knitr::kable()

## NPS by group
nps_calc(survey_data, grps) %>% knitr::kable()

# Overall NPS trend
nps_trend(survey_data, month) %>% knitr::kable()

# 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"