promotionImpact: estimate effectiveness of promotions
In promotionImpact: Analysis & Measurement of Promotion Effectiveness

Description Usage Arguments Details Examples

View source: R/promotion_impact.R

promotionImpact

promotionImpact(
  data,
  promotion,
  time.field = "date",
  target.field = "value",
  dummy.field = NULL,
  trend = TRUE,
  period = "auto",
  structural.change = FALSE,
  trend.param = 0.05,
  period.param = 3,
  var.type = "smooth",
  smooth.except.date = NULL,
  smooth.bandwidth = 2,
  smooth.origin = "all",
  smooth.var.sum = TRUE,
  logged = TRUE,
  differencing = TRUE,
  synergy.promotion = FALSE,
  synergy.var = NULL,
  allow.missing = TRUE
)

`data`	Dataframe containing date, target variable, and some additional time dummies that the researcher wants to account for.
`promotion`	Dataframe containing promotion ID, start date, end date, promotion tag(type). Might include daily payments associated with the promotion.
`time.field`	Specify the date field of 'data'.
`target.field`	Specify the target field of 'data'.
`dummy.field`	Specify the additional time dummies of 'data'.
`trend`	TRUE to incorporate trend component, FALSE to exclude the trend component.
`period`	NULL to exclude any periodicity from the model, 'auto' to automatically determine the period, certain numeric value(e.g. '30.5' for month) to manually specify the period
`structural.change`	TRUE to incorporate structural changes in the intercept(baseline)
`trend.param`	Flexibility of trend component. Default is 0.05, and as this value becomes larger, the trend component will be more flexible.
`period.param`	Flexibility of period component. Default is 3, and as this value becomes larger, the period component will be more flexible.
`var.type`	'smooth' to use smoothed promotion variables, 'dummy' to use dummy promotion variables
`smooth.except.date`	Date value that will be excluded from the smoothing process. eg) '01' to exclude every start day of a month
`smooth.bandwidth`	Bandwidth of local polynomial regression used in the smoothing process. Default value is 2.
`smooth.origin`	'all' to estimate a global smoothing function for all promotions. 'tag' to estimate different smoothing functions for different promotion types(tags).
`smooth.var.sum`	If TRUE, the smoothing values for times when multiple promotions in a single tag overlap will be the values from the latest promotion. Otherwise, the values will be added(default).
`logged`	TRUE to take logs to the target variable and the trend/period component
`differencing`	TRUE to first difference the target variable, smoothed regressors, and the trend/period component values
`synergy.promotion`	TRUE to incorporate synergy between promotion tags.
`synergy.var`	Specify the synergy variables. 'names of fields' between each promotion tag and other variables. eg) c('month_start') to incorparate synergy between each promotion tag and 'month_start'.
`allow.missing`	TRUE to allow missing data in promotion sales during the promotion period

promotionImpact is for analysis & measurement of the effectiveness of promotions, controlling for some prespeficied or estimated control variables.

pri1 <- promotionImpact(data=sim.data, promotion=sim.promotion, 
                       time.field = 'dt', target.field = 'simulated_sales', 
                       trend = FALSE, period = NULL, structural.change = FALSE,
                       logged = TRUE, differencing = TRUE, synergy.promotion = FALSE,
                       synergy.var = NULL, allow.missing = TRUE)