Reference

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

Objectives

After this workshop/exercise, you will better understand how to develop a perceptual map for brand/product positioning purposes using survey data.

Tips

Data Analysis — Consumers WITH a college degree

data1 <- read.csv("Bakersfield_Grocery1.csv", header = TRUE, row.names = 1)

site.groups1 <- c(rep("a", 3), rep("b", 3), rep("c", 3))

data1.pca <- prcomp(data1, scale. = TRUE)

g1 <- ggbiplot(data1.pca,
               obs.scale = 1,
               var.scale  = 1,
               groups     = site.groups1,
               ellipse    = TRUE,
               circle     = TRUE,
               labels     = rownames(data1))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the ggbiplot package.
##   Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
g1 <- g1 + scale_color_discrete(name = "")
g1 <- g1 + theme(legend.direction = "horizontal",
                 legend.position  = "top")
print(g1)

Data Analysis — Consumers WITHOUT a college degree

data2 <- read.csv("Bakersfield_Grocery2.csv", header = TRUE, row.names = 1)

site.groups2 <- c(rep("a", 3), rep("b", 3), rep("c", 3))

data2.pca <- prcomp(data2, scale. = TRUE)

g2 <- ggbiplot(data2.pca,
               obs.scale = 1,
               var.scale  = 1,
               groups     = site.groups2,
               ellipse    = TRUE,
               circle     = TRUE,
               labels     = rownames(data2))

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

Disclaimer

The sample size used for this survey exercise is small. Do not generalize results to the entire population. For generalization, a minimum of 30 respondents per group is typically required.

References

Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in R. Journal of Statistical Software, 20(3). R-bloggers. (2017). Sensographics and mapping consumer perceptions using PCA and FactoMineR. Wickham, H. R for Data Science. https://r4ds.had.co.nz/ R Markdown: The Definitive Guide. https://bookdown.org/yihui/rmarkdown/

##Interpret any differences between these two groups of consumers’ perceptions toward grocery stores in Bakersfield. It looks like graph #1: group a values service and yelp reviews to purchase from a specific grocery store. Group b values smart apps and convenience. Group c values almost all aspects of a grocery store: service, yelp reviews, value, assortment, delivery service, convenience, and smart apps. Compared to graph #2, group a values yelp reviews and service. Group b values yelp reviews and service. Group c values service, value, convenience, assortment, and delivery service. These differences could be attributed to price, education, and accessibility.

##Suggest any other variables that might be included Some other variables that might be included are wait time, checkout speed, store friendliness, and shopping frequency.

##Suggest what a particular grocery store manager should do to make data-informed marketing strategies Depending on the strategy; for example, if the concept was to target a budget/value added store, a strategy would to target the non-college group by emphasizing price, weekly deals, and proximity. Use flyers, in-store signage, and loyalty discount programs.