Understanding Consumers’ Perceptions toward Grocery Stores in Bakersfield

Reference: Xu 2021. Brand/Product Positioning & Perceptual Mapping. Rpubs.com/utjimmyx/brand_positioning

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.
• Suggest any other variables that might be included
• Suggest what a particular grocery store manager should do to make data-informed marketing strategies.
• Suggest any changes you would like to make to your own survey questionnaire. How?

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 (7325e880) 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 (7325e880) 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)

Questions

1. Interpret any differences between these two groups of consumers’ perceptions toward grocery stores in Bakersfield. This report shows two different sets of data about understanding the perceptions of consumers who have a college degree and those who do not have a college degree. Disclaimer that the sample size is small consisting of the town Bakersfield, therefore these results cannot be generalized for the entire population. Noticeable differences between the two is that they prioritize things differently. Those with a college degree are more inclined to go to grocery stores that have their own app, convenience, whether they deliver and value rather than by their yelp reviews.

2. Suggest any other variables that might be included Other variables that should be considered are products that are offered such as organic, homemade an or natural, cleanliness, and aesthetic.

3. Suggest what a particular grocery store manager should do to make data-informed marketing strategies. A grocery store manager can use this data to determine and prioritize the factors that consumers find most important. The aesthetic and brand perception should be considered.

Summarize the article and describe two to three things you found to be relevant to your career! (100 words minimum)

The article “How the perception of a good brand helps your company’s effectiveness” brings a valid point in how times have changed and companies cannot solely rely on the quality of their product; that a company is only as good as their customers perceives them to be. Companies know how to participate in social responsibility because consumers want to know what these companies are doing in terms of giving back to the community. On top of all this, consumers also rely on online reviews to see if the product is worth purchasing, all these factors are relevant to my career as I am majoring in marketing and these are important factors that are considered when marketing a product.

Disclaimer

The sample size used for this survey exercise is small. That said, please do not try to generalize the results to the entire population.

Please keep in mind that for generalization purposes, we would usually need a survey from at least 30 customers for most statistical analysis.

References (methods):

Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in R, with two-and three-dimensional graphics: the ca package. Journal of statistical software, 20(3). https://goedoc.uni-goettingen.de/bitstream/handle/1/5892/Nenadic.pdf?sequence=1

Sensographics and Mapping Consumer Perceptions Using PCA and FactoMineR. https://www.r-bloggers.com/2017/09/sensographics-and-mapping-consumer-perceptions-using-pca-and-factominer/

The Unavoidable Instability of Brand Image. https://www.r-bloggers.com/2014/06/the-unavoidable-instability-of-brand-image/

The R Project for Statistical Computing. https://www.r-project.org/

R for Data Science - Hadley Wickham. https://r4ds.had.co.nz/ R Markdown. https://rmarkdown.rstudio.com/

R Markdown: The Definitive Guide - Bookdown. https://bookdown.org/yihui/rmarkdown/