Understanding Consumers’ Perceptions toward Grocery Stores in Bakersfield

Reference: Xu 2021. Brand/Product Positioning & Perceptual Mapping.

Think-pair-share

The final perceptual maps (see Figures 3 and 4) are designed to explain the impact of a college degree on people’s perception of grocery stores.

Figure 3 represents consumers who have a college degree.

Figure 4 represents consumers who do not have a college degree.

Please try to

• Interpret any differences between these two groups of consumers’ perceptions toward grocery stores in Bakersfield.

Based on the first two images I can see that Walmart, Winco, and Aldi customers prefer it for it’s value and convenience.Now for Trader Joe’s, Sprouts, and Carniceria La Carreta customers prefer it based on the values of service and reviews. Lastly Albertsons, FoodMaxx, and Vallarta are perceived differently for their unique offerings to customers.

• Suggest any other variables that might be included

I think that other variable that might be included is also demographic such as age, income, household size, also brand loyalty.

• Suggest what a particular grocery store manager should do to make data-informed marketing strategies.

Based on the first questions answers I believe that Walmart, Winco, and Aldi should promote their low prices and delivery services. Such as for Trader Joe’s, Sprouts, and Carniceria La Carreta they would engage more on their reviews. Lastly for Albertsons, FoodMaxx, and Vallarta they should have loyalty programs.

Objectives

After this workshop/exercise, you will have a better understanding of how to develop a perceptual mapping for brand/product positioning purposes using either survey data or qualitative data.

In addition, you will also try to learn how to design basic perceptual mapping questions for survey studies.

Tips

• You will upload both the following datasets (Bakersfield_Grocery1.csv & Bakersfield_Grocery2.csv) to R Studio Cloud
• However, you only need to run the R Markdown syntax once.

Data Generation Procedures

The first step is to collect your data. In this example, we collected Qualtrics survey data for the local groceries in Bakersfield, CA using a focus group. You may try to evaluate the attributes based on your group members’ perceptions of your project client’s business and the competition.

If you would like to follow my tutorial, I suggest that you think about using at least three attributes and follow the same data format (Columns:attributes; 1st Row: brands or products). Please open my sample data and see how it looks like.

Data Pre-processing

In this exercise, we first calculate consumers’ average ratings for each attribute and then split the original survey dataset into two according to the education variable (1 and 0). The final datasets used for this analysis include those consumers who have at least a college degree (i.e., Bakersfield_Grocery1.csv) and those not (i.e., Bakersfield_Grocery2.csv).

Interpreting the results - What does a Perceptual Map tell you?

The final output shows you how different grocery brands have been clustered in four quadrants to reflect how they are perceived in terms of several service attributes (Yelp Reviews, Convenience, Price or value, etc.).

Data Analysis and Reporting - Understanding the perceptions of consumers who have a college degree

library(devtools) 

## Loading required package: usethis

install_github("vqv/ggbiplot")

## Skipping install of 'ggbiplot' from a github remote, the SHA1 (f7ea76da) has not changed since last install.
##   Use `force = TRUE` to force installation

library(ggbiplot)

## Loading required package: ggplot2

## Loading required package: plyr

## Loading required package: scales

## Loading required package: grid

?ggbiplot  #see the official introduction to this library

data <- read.csv("Bakersfield_Grocery1.csv", header=TRUE, row.names=1)

site.groups <- c(rep("a", 3), rep("b", 3),
                 rep("c", 3))
data.pca <- prcomp(data[,-1], scale. = TRUE)
data.pca <- prcomp(data, scale. = TRUE)
ggbiplot(data.pca, labels =  rownames(data))

g1 <- ggbiplot(data.pca, obs.scale = 1, var.scale = 1, 
              groups = site.groups, 
              ellipse = TRUE, circle = TRUE, labels =  rownames(data))
g1 <- g1 + scale_color_discrete(name = '')
g1 <- g1 + theme(legend.direction = 'horizontal', 
               legend.position = 'top')
print(g1)

Data Analysis and Reporting - Understanding the perceptions of consumers who do not have a college degree

library(devtools) 
install_github("vqv/ggbiplot")

## Skipping install of 'ggbiplot' from a github remote, the SHA1 (f7ea76da) has not changed since last install.
##   Use `force = TRUE` to force installation

library(ggbiplot)

data <- read.csv("Bakersfield_Grocery2.csv", header=TRUE, row.names=1)

site.groups <- c(rep("a", 3), rep("b", 3),
                 rep("c", 3))
data.pca <- prcomp(data[,-1], scale. = TRUE)
data.pca <- prcomp(data, scale. = TRUE)
ggbiplot(data.pca, labels =  rownames(data))

g2 <- ggbiplot(data.pca, obs.scale = 1, var.scale = 1, 
              groups = site.groups, 
              ellipse = TRUE, circle = TRUE, labels =  rownames(data))

g2 <- g2 + scale_color_discrete(name = '')
g2 <- g2 + theme(legend.direction = 'horizontal', 
               legend.position = 'top')
print(g2)