generateRetailData: Generate a marketing mix modeling data set

In DoktorMike/dammmdatagen: Marketing Mix Modeling Data Generator

Description

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()
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")

DoktorMike/dammmdatagen documentation built on Dec. 29, 2021, 6:46 a.m.