shares.segm: Segmentation of market areas by a criterion

In MCI: Multiplicative Competitive Interaction (MCI) Model

Description

This function segments the contents of an interaction matrix based on a criterion, such as distance or market penetration.

Usage

shares.segm(mcidataset, submarkets, suppliers, segmentation, observations, 
..., check_df = TRUE)

Arguments

mcidataset	an interaction matrix which is a data.frame containing the submarkets, suppliers, the total values (e.g. shopping trips, expenditures) and a variable containing the criterion for the segmentation (e.g. distance, travel time, market penetration)
submarkets	the column in the interaction matrix mcidataset containing the submarkets
suppliers	the column in the interaction matrix mcidataset containing the suppliers
segmentation	The column in the interaction matrix mcidataset containing the variable which is to be used for segmentation
observations	The column in the interaction matrix mcidataset containing the total values which have to segmented
...	The stated limits of class segments (e.g. 0, 10, 20, 30)
check_df	logical argument that indicates if the input (dataset, column names) is checked (default: check_df = TRUE (should not be changed, only for internal use))

Details

For practical reasons, a market/market area can be zoned into segments based on a criterion (such as distance or travel time zones, zones of market penetration). Based on an existing interaction matrix, this function returns zones of a market/market area.

Value

Returns a new data.frame with the classification segments, the sum of the total observed values with respect to each class and the corresponding percentage.

Author(s)

Thomas Wieland

References

Berman, B. R./Evans, J. R. (2013): “Retail Management: A Strategic Approach”. Pearson, 12 edition, 2013.

See Also

shares.total

Examples

# Market area segmentation based on the POS survey in shopping1 #

data(shopping1)
# The survey dataset
data(shopping2)
# Dataset with distances and travel times

shopping1_adj <- shopping1[(shopping1$weekday != 3) & (shopping1$holiday != 1) 
& (shopping1$survey != "pretest"),]
# Removing every case from tuesday, holidays and the ones belonging to the pretest

ijmatrix_POS <- ijmatrix.create(shopping1_adj, "resid_code", "POS", "POS_expen")
# Creates an interaction matrix based on the observed frequencies (automatically)
# and the POS expenditures (Variable "POS_expen" separately stated)

ijmatrix_POS_data <- merge(ijmatrix_POS, shopping2, by.x="interaction", by.y="route", 
all.x = TRUE)
# Adding the distances and travel times

ijmatrix_POS_data_segm_visit <- shares.segm(ijmatrix_POS_data, "resid_code", "POS",
"d_time", "freq_ij_abs", 0,10,20,30)
# Segmentation by travel time using the number of customers/visitors
# Parameters: interaction matrix (data frame), columns with origins and destinations,
# variable to divide in classes, absolute frequencies/expenditures, class segments

ijmatrix_POS_data_segm_exp <- shares.segm(ijmatrix_POS_data, "resid_code", "POS",
"d_time", "freq_ij_abs_POS_expen", 0,10,20,30)
# Segmentation by travel time using the POS expenditures

Example output

MCI documentation built on May 2, 2019, 6:02 a.m.