Note

This is a special topic I designed for my Marketing Research & Analytics course (MKTG4000). Please use the following reference.

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

Introduction - What is Brand/Product Positioning

A brand’s positioning should tell customers

The selection of benefits to emphasize should be based on

Reference (strategy articles):

Simpson 2017. How The Perception Of A Good Brand Helps Your Company’s Effectiveness https://www.forbes.com/sites/forbesagencycouncil/2017/05/05/how-the-perception-of-a-good-brand-helps-your-companys-effectiveness/?sh=4380e74c67ac

MIT. Strategic Positioning. https://ocw.mit.edu/courses/sloan-school-of-management/15-810-marketing-management-analytics-frameworks-and-applications-fall-2015/lecture-notes/MIT15_810F15_L1_Stratgic.pdf

Knowles, J. (2015). Harvard Business Review. A Better Way to Map Brand Strategy.

Objectives

Elite Cleaning Service (ECS) is a full-time, newly established commercial and office cleaning services located in Bakersfield, California. Our professional cleaning members are specially trained in all aspects of building maintenance and safety protocols. ECS will go above the norm to make sure the image of your company is reflected and maintained in a professional manner for your guests while keeping the environment for employees pleasant, clean, and healthyElite Cleaning Services (ECS) provides a customized cleaning program to meet the specific needs of business and clients through a spectrum of cleaning services including, but no limited to, floor vacuuming, carpet shampoo, desk/chair cleaning, door/window cleaning, cabinet and bathroom cleaning, trash removal, etc. Whether your company needs a daily, weekly, monthly or a one-time cleaning program, Elite Cleaning Service can create a cleaning program to meet your all your needs and budget. You can be assured of a job performed above and beyond your expectations by our trained and experienced staff with state-of-the-art cleaning equipment and all environmentally safe cleaners.

Data Generation Procedures

The first step is to collect your data. In this example, I collected Google iew data for the local Janitorial Services in Bakersfield, CA, and customer review perceptions of service, quality, and price.

See the original Google review data here: https://www.google.com/search?rlz=1C5CHFA_enUS700US700&tbs=lf:1,lf_ui:14&tbm=lcl&sxsrf=AOaemvJKhxV7BHZZLBcAef0aM4zckPwUKw:1632635822114&q=Industrial+Janitorial+services&rflfq=1&num=10&sa=X&ved=2ahUKEwiVzuWx-pvzAhVLs54KHZkhARAQjGp6BAgIEF8&biw=1440&bih=716&dpr=2#rlfi=hd:;si:;mv:[[35.68944171093483,-118.40389161044936],[35.061263638945256,-119.80464844638686]]

What does a Perceptual Map tell you

A perceptual map can be generated using principal component analysis (PCA) or correspondence analysis/multiple correspondence analysis (CA or MCA). See the references below. The graph is displayed in the form of a scatter plot. For ease of interpretation, a four-quadrant graph is best for displaying the output. Each quadrant match information from columns and rows.

I have generated a dataset on local Janitorial Services in Bakersfield, CA, and how they are perceived by my reviewers. The final output shows you how different companies brands have been clustered in four quadrants to reflect how they are perceived in terms of three attributes (Google Reviews, Quality, and Price).

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/

Data Analysis and Reporting

library(plfm)
## Loading required package: sfsmisc
## Loading required package: abind
data <- read.csv("Janitorial_Services.csv", header=TRUE, row.names=1)
#Load the libraries
#library(MASS)
#library(FactoMineR)
library(factoextra)
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library("ggplot2")

# Use the "princomp" function to reduce dimensions

pc.cr <- princomp(data, cor=TRUE)
summary(pc.cr)
## Importance of components:
##                           Comp.1    Comp.2     Comp.3
## Standard deviation     1.3337280 0.9641205 0.54003828
## Proportion of Variance 0.5929434 0.3098428 0.09721378
## Cumulative Proportion  0.5929434 0.9027862 1.00000000
# draw a bi-plot using the biplot function
biplot(pc.cr)