Generate a marketing mix modeling data set

This function generates a marketing mix modeling data set based on the parameters given for a Retail case with Revenue as it's response. It uses many different stochastical processes to accomplish this and the dynamics behind them are not available to the user to manipulate.

Usage

generateRetailData(
  fromDate = Sys.Date() - 3 * 365,
  toDate = Sys.Date(),
  kpi = "units",
  sector = "retail",
  onlineInsertionNames = c("display", "facebook", "search_branded"),
  offlineInsertionNames = c("tv", "radio", "ooh", "print"),
  priceNames = c("price_product_a", "price_product_b", "price_product_c"),
  distributionNames = c("dist_product_a", "dist_product_b", "dist_product_c"),
  weatherNames = c("sunshine", "precipitation", "temperature"),
  competitorNames = c("competitor_a", "competitor_b", "competitor_c"),
  macroNames = c("cpi", "cci", "gdp"),
  eventNames = c("event_a", "event_b")
)

Arguments

fromDate

the beginning of the time series

toDate

the end of the time series

kpi

the name of the kpi (response) to simulate

sector

the name of the sector to simulate (currently not used)

onlineInsertionNames

the names of each online media insertion you wish to use

offlineInsertionNames

the names of each offline media insertion you wish to use

priceNames

the names of the different product prices

distributionNames

the names of the different product distributions

weatherNames

the names of the weather data measurements to use

competitorNames

the names of each competitor considered

macroNames

the names of the macroeconomical factors to simulate

eventNames

the names of events to add

Value

a list of tibble containing each data mentioned

Examples

library(ggplot2)
library(nord)
ret <- generateRetailData()
#> Joining, by = "date"
#> Joining, by = "date"
#> Joining, by = "date"
#> New names:
#> * product_a -> product_a...7
#> * product_b -> product_b...8
#> * product_c -> product_c...9
#> * product_a -> product_a...10
#> * product_b -> product_b...11
#> * ...
dates <- ret[["covariates"]][["Macro"]][["date"]]
qplot(dates, ret[["response"]]) + geom_line() + ylim(0, NA)

# entrytocolname <- function(x) a <- ret[["effects"]][[x]] %>% setNames(c(tolower(paste0(x, "_", names(.)))))
entrytocolname <- function(x) tibble::tibble(rowSums(ret[["effects"]][[x]])) %>% setNames(x)
Reduce(dplyr::bind_cols, lapply(names(ret[["effects"]]), entrytocolname)) %>%
        dplyr::mutate(date = dates) %>%
        tidyr::pivot_longer(-date, names_to = "variable", values_to = "value") %>%
        ggplot2::ggplot(ggplot2::aes(x = date, y = value, fill = variable)) +
        ggplot2::geom_bar(stat = "identity")