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

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)

Interpretations

Hypothesis: There is a significant relationship between consumer perceptions and these attributes.

Positive Coefficient: Has a positive impact on consumer perception. Negative Coefficient: Negatively impacts perceptions

P-value: 0.003 for convenience, suggesting it has a significant effect on perceptions P-value: 0.15 for price, suggesting price does not significantly impact perception

R-square: 0.85, explaining 85% of the variation in consumer perceptions; strong fit

Hypothesis Results Interpretation: If p-value for attributes are < 0.05. Attributes like Yelp Reviews and Convenience significantly affect consumer perception. The model strongly explains consumer perception differences between degree-holding and non-degree-holding consumers.If one group values Price more than Convenience, managers should tailor promotions accordingly.If another group values Yelp Reviews, the store should focus on improving online reputation.

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/