| Title: | Tidy Calculation of Marketing Metrics Plus Quick Analysis |
| Version: | 0.0.2 |
| Description: | Facilitates tidy calculation of popular quantitative marketing metrics. It also includes functions for doing analysis that will help marketers and data analysts better understand the drivers and/or trends of these metrics. These metrics include Customer Experience Index https://go.forrester.com/analytics/cx-index/ and Net Promoter Score https://www.netpromoter.com/know/. |
| Depends: | R (≥ 3.5.0) |
| License: | CC0 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | knitr, rmarkdown, ggplot2 |
| VignetteBuilder: | knitr |
| Imports: | dplyr (≥ 0.8.3), magrittr (≥ 1.5), tidyr (≥ 1.0.0), tibble (≥ 2.1.3) |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-12-17 22:48:13 UTC; c.umphlett |
| Author: | Chris Umphlett [aut, cre] |
| Maintainer: | Chris Umphlett <christopher.umphlett@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-12-18 05:30:02 UTC |
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" |
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.
Value
Data frame with CXi and survey count for each combination of the grouping variables
Examples
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)
Tidy Calculation of Customer Experience Index trends by group
Description
Simplifies the calculation of Customer Experience Index (CXi) trends over time from raw survey data within the tidyverse framework.
Usage
cxi_trend(
survey_data,
trend_var,
...,
cx_high = 4,
cx_low = 2,
min_surveys = 1,
avg_surveys = 0
)
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 |
trend_var |
Column that represents an element of time, eg week number, date, month & year |
... |
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" |
min_surveys |
Minimum surveys found in every period for each group to be included |
avg_surveys |
Average surveys found in every period for each group to be included |
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.
The trend version of the function allows you to specify one column as a date over which to trend the data. This allows quick filtering to eliminate groupings that fail to meet user-specified thresholds for average or minimum survey counts per time period.
The resulting data set is set up for creating faceted line plots using ggplot2.
Value
Data frame with CXi and survey count for each combination of the grouping variables over the time variable.
Examples
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_trend(df, cx_date, cx_group)
Tidy Calculation of Net Promoter Score
Description
Simplifies the calculation of Net Promoter Score (NPS) from raw survey data within the tidyverse framework.
Usage
nps_calc(survey_data, ...)
Arguments
survey_data |
Raw survey data. Needs to be one row per survey with the nps question in a numeric column called nps_question |
... |
Optional columns by which to group the nps calculation. There is no limit to the number of grouping variables chosen. Too many will likely result in nps calculations that are too fragmented / based on very small survey counts. |
Details
Net Promoter Score was originally developed by Fred Reichheld and now is owned by Bain Company and Satmetrix Systems. According to Wikipedia it "is a management tool that can be used to gauge the loyalty of a firm's customer relationships."
Value
Data frame with nps and survey count for each combination of the grouping variables
Examples
nps_question <- sample(10, 100, replace = TRUE)
nps_date <- rep(seq.Date(from = as.Date("2019-01-01"), to = as.Date("2019-01-10"), by = "day"), 10)
nps_group <- rep(c("a", "b", "c", "d"), 25)
df <- data.frame(nps_question, nps_date, nps_group)
nps_calc(df, nps_group)
Tidy Calculation of Net Promoter Score trends by group
Description
Simplifies the calculation of Net Promoter Score (NPS) trends over time from raw survey data within the tidyverse framework.
Usage
nps_trend(survey_data, trend_var, ..., min_surveys = 1, avg_surveys = 0)
Arguments
survey_data |
Raw survey data. Needs to be one row per survey with the nps question in a numeric column called nps_question |
trend_var |
Column that represents an element of time, eg week number, date, month & year |
... |
Optional columns by which to group the nps calculation. There is no limit to the number of grouping variables chosen. Too many will likely result in nps calculations that are too fragmented / based on very small survey counts. |
min_surveys |
Minimum surveys found in every period for each group to be included |
avg_surveys |
Average surveys found in every period for each group to be included |
Details
Net Promoter Score was originally developed by Fred Reichheld and now is owned by Bain Company and Satmetrix Systems. According to Wikipedia it "is a management tool that can be used to gauge the loyalty of a firm's customer relationships."
The trend version of the function allows you to specify one column as a date over which to trend the data. This allows quick filtering to eliminate groupings that fail to meet user-specified thresholds for average or minimum survey counts per time period.
The resulting data set is set up for creating faceted line plots using ggplot2.
Value
Data frame with nps and survey count for each combination of the grouping variables over the time variable.
Examples
nps_question <- sample(10, 100, replace = TRUE)
nps_date <- rep(seq.Date(from = as.Date("2019-01-01"), to = as.Date("2019-01-10"), by = "day"), 10)
nps_group <- rep(c("a", "b", "c", "d"), 25)
df <- data.frame(nps_question, nps_date, nps_group)
nps_trend(df, nps_date, nps_group)