Package 'promotionImpact'

compare several models

Description

compareModels

Usage

compareModels(
  data,
  promotion,
  fix = list(logged = TRUE, differencing = TRUE),
  time.field = "dt",
  target.field = "sales",
  dummy.field = NULL,
  trend.param = 0.05,
  period.param = 3,
  var.type = "smooth",
  smooth.except.date = NULL,
  smooth.bandwidth = 2,
  smooth.var.sum = TRUE,
  allow.missing = TRUE
)

Arguments

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.
fix	A List of constraints to find the best model. Constraints can only be in following list: 'period','trend','logged','synergy.var','differencing','smooth.origin','structural.change','synergy.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.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.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).
allow.missing	TRUE to allow missing data in promotion sales during the promotion period

Details

compareModels compares several models under user-defined conditions and suggests the best options.

Examples

comparison <- compareModels(data = sim.data, promotion = sim.promotion.sales,
                            fix = list(logged = TRUE, differencing = TRUE, smooth.origin='all',
                                       trend = FALSE, period = NULL), 
                            time.field = 'dt', target.field = 'simulated_sales', 
                            trend.param = 0.02, period.param = 2)

---

detect some outliers

Description

detectOutliers

Usage

detectOutliers(
  model,
  threshold = list(cooks.distance = 1, dfbetas = 1, dffits = 2),
  option = 2
)

Arguments

model	Execution result object : promotionImpact
threshold	List of threshold values to be determined as outliers if greater than the written values
option	The number of indicators that must be greater than the threshold values to be outliers.

Details

detectOutliers extracts outliers which affect the average effects of promotions.

Examples

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)
out <- detectOutliers(model = pri1, 
                      threshold = list(cooks.distance=1, dfbetas=1, dffits=2), option = 1)

---

estimate effectiveness of promotions

Description

promotionImpact

Usage

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
)

Arguments

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

Details

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

Examples

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)

---

Daily Total Sales

Description

This data set is simulated daily total sales data contaning 958 observations of 2 variables. 'dt': date with Date format. 'simulated_sales': simulated daily sales with numeric format.

Usage

sim.data

Format

A dataset containing 958 observations of 2 variables.

Source

NCsoft AnalysisModeling Team <[email protected]> <[email protected]> <[email protected]>

---

Promotion Schedule

Description

This data set is promotion schedule data including promotion tag information. 'pro_id': promotion ID. 'start_dt': start date of each promotion 'end_dt': end date of each promotion. 'tag_info': promotion tag information (promotion type).

Usage

sim.promotion

Format

A dataset containing 50 observations of 4 variables.

Source

NCsoft AnalysisModeling Team <[email protected]> <[email protected]> <[email protected]>

---

Daily Promotion Sales with Promotion information

Description

This data set is simulated daily promotion sales data with promotion information. 'pro_id': promotion ID 'start_dt': start date of each promotion 'end_dt': end date of each promotion 'tag_info': promotion tag information (promotion type) 'dt': date 'payment': simulated daily promotion sales

Usage

sim.promotion.sales

Format

A dataset containing 1486 observations of 6 variables.

Source

NCsoft AnalysisModeling Team <[email protected]> <[email protected]> <[email protected]>

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