Marketing_Mix_Brands
Partial R Code
# Marketing Mix - Brands
# Customer ratings for - perceptual adjectives
and 4 brands.
# Within marketing analytics - perceptual
adjectives to be linked to perceptual maps.
d <- read.csv("C:/Users/Rohit/Desktop/Marketing Mix - Brands/d.csv")
head(d)
## cheap snappy effective luxurious artistic
bold caring casual charming
## 1
9 7 2 10 7
5 9 1
3
## 2
4 2 2 8
5 4
9 7 2
## 3
5 6 10 10 1
8 10 10
8
## 4
8 10 5 5
10 2 9
2 1
## 5
9 5 3 10 3
8 10 5
3
## 6
10 10 4 9 9
3 5 1
10
##
contemporary creative daring elegant energetic exciting festive fresh
## 1
2 10 5
4 10 10
2 10
## 2
2 9 1
2 8 8
3 8
## 3
10 9 4
1 8 8
3 8
## 4
5 9 9
9 8 8
4 8
## 5
3 9 6
6 8 8
5 8
## 6
4 10 5
4 10 10
7 10
## fun
graceful hip brand.name
## 1
10 9 10
1
## 2
9 9 9
3
## 3
8 9 8
3
## 4
10 4 10
4
## 5
9 7 9
4
## 6
8 1 8
3
summary(d)
## cheap snappy effective luxurious
##
Min. : 1.000 Min.
: 1.000 Min. : 1.000
Min. : 1.000
## 1st
Qu.: 4.000 1st Qu.: 4.000 1st Qu.: 4.000 1st Qu.: 3.000
##
Median : 8.000 Median :
8.000 Median : 6.000 Median : 6.000
##
Mean : 7.079 Mean
: 7.074 Mean : 5.786
Mean : 5.642
## 3rd
Qu.:10.000 3rd Qu.:10.000 3rd Qu.: 8.000 3rd Qu.: 8.000
## Max. :10.000
Max. :10.000 Max.
:10.000 Max. :10.000
##
artistic bold caring casual
##
Min. : 1.000 Min.
: 1.000 Min. : 1.000
Min. : 1.00
## 1st
Qu.: 3.000 1st Qu.: 3.000 1st Qu.: 3.000 1st Qu.: 3.00
##
Median : 6.000 Median :
5.000 Median : 6.000 Median : 6.00
##
Mean : 5.529 Mean
: 5.486 Mean : 5.505
Mean : 5.54
## 3rd
Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 8.00
##
Max. :10.000 Max.
:10.000 Max. :10.000
Max. :10.00
##
charming contemporary creative daring
##
Min. : 1.000 Min.
: 1.0 Min. : 1.000
Min. : 1.000
## 1st
Qu.: 3.000 1st Qu.: 3.0 1st Qu.: 4.000 1st Qu.: 3.000
##
Median : 6.000 Median : 6.0 Median : 7.000 Median : 5.000
##
Mean : 5.637 Mean
: 5.8 Mean : 6.085
Mean : 5.473
## 3rd
Qu.: 8.000 3rd Qu.: 8.0 3rd Qu.: 9.000 3rd Qu.: 8.000
##
Max. :10.000 Max.
:10.0 Max. :10.000
Max. :10.000
##
elegant energetic exciting festive
##
Min. : 1.000 Min.
: 1.000 Min. : 1.000
Min. : 1.000
## 1st
Qu.: 3.000 1st Qu.: 4.000 1st Qu.: 4.000 1st Qu.: 3.000
##
Median : 5.000 Median :
7.000 Median : 7.000 Median : 5.500
##
Mean : 5.502 Mean
: 6.082 Mean : 6.072
Mean : 5.478
## 3rd
Qu.: 8.000 3rd Qu.: 9.000 3rd Qu.: 9.000 3rd Qu.: 8.000
##
Max. :10.000 Max.
:10.000 Max. :10.000
Max. :10.000
##
fresh fun graceful hip
##
Min. : 1.000 Min.
: 1.000 Min. : 1.000
Min. : 1.000
## 1st
Qu.: 4.000 1st Qu.: 3.000 1st Qu.: 3.000 1st Qu.: 3.000
##
Median : 7.000 Median :
6.000 Median : 5.000 Median : 6.000
##
Mean : 6.103 Mean
: 5.961 Mean : 5.494
Mean : 6.005
## 3rd
Qu.: 9.000 3rd Qu.: 9.000 3rd Qu.: 8.000 3rd Qu.: 9.000
##
Max. :10.000 Max.
:10.000 Max. :10.000
Max. :10.000
##
brand.name
##
Min. :1.000
## 1st
Qu.:2.000
##
Median :3.000
##
Mean :2.508
## 3rd
Qu.:4.000
##
Max. :4.000
# scaling the raw data - creating a scaled matrix
dsc<-scale(d[,1:20])
# creating a Data Frame of same name -
"dsc" from scaled matrix
dsc<-as.data.frame(dsc)
summary(dsc)
## cheap snappy effective luxurious
##
Min. :-1.9334 Min.
:-1.9356 Min. :-1.77078
Min. :-1.5973
## 1st
Qu.:-0.9793 1st Qu.:-0.9797 1st Qu.:-0.66072 1st Qu.:-0.9092
##
Median : 0.2929 Median :
0.2950 Median : 0.07933 Median : 0.1230
##
Mean : 0.0000 Mean
: 0.0000 Mean : 0.00000
Mean : 0.0000
## 3rd
Qu.: 0.9290 3rd Qu.: 0.9323 3rd Qu.: 0.81937 3rd Qu.: 0.8112
##
Max. : 0.9290 Max.
: 0.9323 Max. : 1.55941
Max. : 1.4993
##
artistic bold caring casual
##
Min. :-1.5710 Min.
:-1.5652 Min. :-1.5577
Min. :-1.5768
## 1st
Qu.:-0.8772 1st Qu.:-0.8674 1st Qu.:-0.8662 1st Qu.:-0.8821
##
Median : 0.1634 Median
:-0.1696 Median : 0.1711 Median : 0.1599
##
Mean : 0.0000 Mean
: 0.0000 Mean : 0.0000
Mean : 0.0000
## 3rd
Qu.: 0.8571 3rd Qu.: 0.8771 3rd Qu.: 0.8626 3rd Qu.: 0.8545
##
Max. : 1.5509 Max.
: 1.5749 Max. : 1.5541
Max. : 1.5492
##
charming contemporary creative daring
## Min. :-1.5899
Min. :-1.76217 Min.
:-1.7559 Min. :-1.5594
## 1st
Qu.:-0.9042 1st Qu.:-1.02798 1st Qu.:-0.7200 1st Qu.:-0.8622
##
Median : 0.1243 Median :
0.07331 Median : 0.3159 Median :-0.1649
##
Mean : 0.0000 Mean
: 0.00000 Mean : 0.0000
Mean : 0.0000
## 3rd
Qu.: 0.8100 3rd Qu.: 0.80750 3rd Qu.: 1.0065 3rd Qu.: 0.8811
##
Max. : 1.4957 Max.
: 1.54170 Max. : 1.3518
Max. : 1.5783
##
elegant energetic exciting festive
##
Min. :-1.5707 Min.
:-1.7515 Min. :-1.7434
Min. :-1.556096
## 1st
Qu.:-0.8729 1st Qu.:-0.7176 1st Qu.:-0.7122 1st Qu.:-0.861158
##
Median :-0.1750 Median :
0.3162 Median : 0.3190 Median : 0.007514
##
Mean : 0.0000 Mean
: 0.0000 Mean : 0.0000
Mean : 0.000000
## 3rd
Qu.: 0.8717 3rd Qu.: 1.0055 3rd Qu.: 1.0064 3rd Qu.: 0.876186
##
Max. : 1.5695 Max.
: 1.3501 Max. : 1.3502
Max. : 1.571123
##
fresh fun graceful hip
##
Min. :-1.7679 Min.
:-1.66693 Min. :-1.564
Min. :-1.691154
## 1st
Qu.:-0.7286 1st Qu.:-0.99490 1st Qu.:-0.868 1st Qu.:-1.015407
##
Median : 0.3107 Median :
0.01315 Median :-0.172 Median :-0.001785
##
Mean : 0.0000 Mean
: 0.00000 Mean : 0.000
Mean : 0.000000
## 3rd
Qu.: 1.0035 3rd Qu.: 1.02120 3rd Qu.: 0.872 3rd Qu.: 1.011836
##
Max. : 1.3499 Max.
: 1.35722 Max. : 1.568
Max. : 1.349710
# as seen in summary -
"mean" for all - dimensions - is now "0.00"
# Now we create a merged data frame
"dsc1" - adding the "brand.name" variable to the
# scaled data frame created earlier
dsc1<-cbind(dsc,d$brand.name)
# change the - "d$brand.name" to just
"brand.name"
names(dsc1)
[21]
<- "brand.name"
library(corrplot)
# varied permutations of the bi variable
correlation plot....
# too many variables thus creating seperate
Correlation Plots
c1<-cor(dsc1[, 1:5])
c2<-cor(dsc1[, 6:10])
c3<-cor(dsc1[, 10:15])
c4<-cor(dsc1[, 16:20])
layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))
corrplot.mixed(c1,lower="number",
upper="circle")
corrplot.mixed(c2,lower="number",
upper="circle")
corrplot.mixed(c3,lower="number",
upper="circle")
corrplot.mixed(c4,lower="number", upper="circle")
corrplot(c3,type="upper", method="number",order="AOE")
# "AOE" for the angular order of the
eigenvectors
corrplot(c4,type="upper", method="shade",order="hclust")
# "hclust" for the
hierarchical clustering order.
corrplot(c3,type="upper", method="shade",order="FPC")
# "FPC" for the first principal
component order.
corrplot(c4,type="upper", method="ellipse",order="FPC")
# "FPC" for the first principal
component order.
#
method="ellipse"
corrplot(c4,type="upper", method="color",order="hclust")
# "FPC" for the
first principal component order.
#
method="color"
corrplot(c3,type="upper", method="shade",order="hclust")
# "FPC" for the first principal
component order.
#
method="shade" - not
very different from "color"
###
# Whats the average or the "mean"
rating for each brand
# for all given - 20 - perceptual adjectives ?
###
avg.ratings <- aggregate(.~ brand.name , data=dsc1 , mean)
View(avg.ratings)
library(gplots)
##
## Attaching package: 'gplots'
##
## The following object is masked from
'package:stats':
##
##
lowess
library("RColorBrewer", lib.loc="~/R/win-library/3.1")
heatmap.2(as.matrix(c3),col=brewer.pal(9, "GnBu"),
xlab=" Perceptual Adjectives ", ylab= "Coded Brand Names",trace="none",
key=T,
dend="none",main="\n\n\n Heat Map \n\n Perceptual Adj. Avg Ratings")
heatmap.2(as.matrix(c4),col=brewer.pal(7,"Greens"),
xlab=" Perceptual Adjectives ", ylab= "Coded Brand Names",trace="none",
key=T,
dend="none",main="\n\n\n Heat Map \n\n Perceptual Adj. Avg Ratings")
#
heatmap.2(as.matrix(c4),col=brewer.pal(9,"BrBG"),
xlab=" Perceptual Adjectives ", ylab= "Coded Brand Names",trace="none",
key=T,
dend="none",main="\n\n\n Heat Map \n\n Perceptual Adj. Avg Ratings")
#
heatmap.2(as.matrix(avg.ratings),col=brewer.pal(9,"BrBG"),
xlab=" Perceptual Adjectives ", ylab= "Coded Brand Names",trace="none",
key=T,
dend="none",main="\n\n\n Heat Map \n\n Perceptual Adj. Avg Ratings")
#
# As the average ratings are mostly in the
"0.000" range we need to
# multiply with "1000" across to get
a decent data viz with the HeatMap .
getwd()
## [1]
"C:/Users/Rohit/Desktop/Marketing Mix - Brands"
write.table(avg.ratings,"C:/Users/Rohit/Desktop/Marketing Mix -
Brands/avg.csv",sep=",")
# Excel calc - external to R - now import
avg1.csv
avg1 <- read.csv("C:/Users/Rohit/Desktop/Marketing Mix - Brands/avg1.csv")
View(avg1)
#
heatmap.2(as.matrix(avg1),col=brewer.pal(9,"BrBG"),
xlab=" Perceptual Adjectives ", ylab= "Coded Brand Names",trace="none",
key=T,
dend="none",main="\n\n\n Heat Map \n\n Perceptual Adj. Avg Ratings")
#
heatmap.2(as.matrix(avg1),col=brewer.pal(9, "GnBu"),
xlab=" Perceptual Adjectives ", ylab= "Coded Brand Names",trace="none",
key=T,
dend="none",main="\n\n\n Heat Map \n\n Perceptual Adj. Avg Ratings")
#to be completed – watch this
space for more …Rohit Dhankar
