# 1. IMPORT LIBRARY
library(plspm)
## Warning: package 'plspm' was built under R version 4.5.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.5.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(psych)
## Warning: package 'psych' was built under R version 4.5.3
##
## Attaching package: 'psych'
## The following objects are masked from 'package:plspm':
##
## alpha, rescale, unidim
library(corrplot)
## corrplot 0.95 loaded
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.3
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
## The following object is masked from 'package:plspm':
##
## alpha
# 2. IMPORT DATASET
data <- read.csv(
"marketing_campaign.csv",
sep = ";"
)
# 3. EKSPLORASI DATASET
# Menampilkan data
head(data)
## ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer
## 1 5524 1957 Graduation Single 58138 0 0 04/09/2012
## 2 2174 1954 Graduation Single 46344 1 1 08/03/2014
## 3 4141 1965 Graduation Together 71613 0 0 21/08/2013
## 4 6182 1984 Graduation Together 26646 1 0 10/02/2014
## 5 5324 1981 PhD Married 58293 1 0 19/01/2014
## 6 7446 1967 Master Together 62513 0 1 09/09/2013
## Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts
## 1 58 635 88 546 172 88
## 2 38 11 1 6 2 1
## 3 26 426 49 127 111 21
## 4 26 11 4 20 10 3
## 5 94 173 43 118 46 27
## 6 16 520 42 98 0 42
## MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases
## 1 88 3 8 10
## 2 6 2 1 1
## 3 42 1 8 2
## 4 5 2 2 0
## 5 15 5 5 3
## 6 14 2 6 4
## NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5
## 1 4 7 0 0 0
## 2 2 5 0 0 0
## 3 10 4 0 0 0
## 4 4 6 0 0 0
## 5 6 5 0 0 0
## 6 10 6 0 0 0
## AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
## 1 0 0 0 3 11 1
## 2 0 0 0 3 11 0
## 3 0 0 0 3 11 0
## 4 0 0 0 3 11 0
## 5 0 0 0 3 11 0
## 6 0 0 0 3 11 0
# Struktur data
str(data)
## 'data.frame': 2240 obs. of 29 variables:
## $ ID : int 5524 2174 4141 6182 5324 7446 965 6177 4855 5899 ...
## $ Year_Birth : int 1957 1954 1965 1984 1981 1967 1971 1985 1974 1950 ...
## $ Education : chr "Graduation" "Graduation" "Graduation" "Graduation" ...
## $ Marital_Status : chr "Single" "Single" "Together" "Together" ...
## $ Income : int 58138 46344 71613 26646 58293 62513 55635 33454 30351 5648 ...
## $ Kidhome : int 0 1 0 1 1 0 0 1 1 1 ...
## $ Teenhome : int 0 1 0 0 0 1 1 0 0 1 ...
## $ Dt_Customer : chr "04/09/2012" "08/03/2014" "21/08/2013" "10/02/2014" ...
## $ Recency : int 58 38 26 26 94 16 34 32 19 68 ...
## $ MntWines : int 635 11 426 11 173 520 235 76 14 28 ...
## $ MntFruits : int 88 1 49 4 43 42 65 10 0 0 ...
## $ MntMeatProducts : int 546 6 127 20 118 98 164 56 24 6 ...
## $ MntFishProducts : int 172 2 111 10 46 0 50 3 3 1 ...
## $ MntSweetProducts : int 88 1 21 3 27 42 49 1 3 1 ...
## $ MntGoldProds : int 88 6 42 5 15 14 27 23 2 13 ...
## $ NumDealsPurchases : int 3 2 1 2 5 2 4 2 1 1 ...
## $ NumWebPurchases : int 8 1 8 2 5 6 7 4 3 1 ...
## $ NumCatalogPurchases: int 10 1 2 0 3 4 3 0 0 0 ...
## $ NumStorePurchases : int 4 2 10 4 6 10 7 4 2 0 ...
## $ NumWebVisitsMonth : int 7 5 4 6 5 6 6 8 9 20 ...
## $ AcceptedCmp3 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ AcceptedCmp4 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ AcceptedCmp5 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ AcceptedCmp1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ AcceptedCmp2 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Complain : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Z_CostContact : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Z_Revenue : int 11 11 11 11 11 11 11 11 11 11 ...
## $ Response : int 1 0 0 0 0 0 0 0 1 0 ...
# Dimensi data
dim(data)
## [1] 2240 29
# Nama variabel
colnames(data)
## [1] "ID" "Year_Birth" "Education"
## [4] "Marital_Status" "Income" "Kidhome"
## [7] "Teenhome" "Dt_Customer" "Recency"
## [10] "MntWines" "MntFruits" "MntMeatProducts"
## [13] "MntFishProducts" "MntSweetProducts" "MntGoldProds"
## [16] "NumDealsPurchases" "NumWebPurchases" "NumCatalogPurchases"
## [19] "NumStorePurchases" "NumWebVisitsMonth" "AcceptedCmp3"
## [22] "AcceptedCmp4" "AcceptedCmp5" "AcceptedCmp1"
## [25] "AcceptedCmp2" "Complain" "Z_CostContact"
## [28] "Z_Revenue" "Response"
# Statistik deskriptif
summary(data)
## ID Year_Birth Education Marital_Status
## Min. : 0 Min. :1893 Length:2240 Length:2240
## 1st Qu.: 2828 1st Qu.:1959 Class :character Class :character
## Median : 5458 Median :1970 Mode :character Mode :character
## Mean : 5592 Mean :1969
## 3rd Qu.: 8428 3rd Qu.:1977
## Max. :11191 Max. :1996
##
## Income Kidhome Teenhome Dt_Customer
## Min. : 1730 Min. :0.0000 Min. :0.0000 Length:2240
## 1st Qu.: 35303 1st Qu.:0.0000 1st Qu.:0.0000 Class :character
## Median : 51382 Median :0.0000 Median :0.0000 Mode :character
## Mean : 52247 Mean :0.4442 Mean :0.5062
## 3rd Qu.: 68522 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :666666 Max. :2.0000 Max. :2.0000
## NA's :24
## Recency MntWines MntFruits MntMeatProducts
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.0
## 1st Qu.:24.00 1st Qu.: 23.75 1st Qu.: 1.0 1st Qu.: 16.0
## Median :49.00 Median : 173.50 Median : 8.0 Median : 67.0
## Mean :49.11 Mean : 303.94 Mean : 26.3 Mean : 166.9
## 3rd Qu.:74.00 3rd Qu.: 504.25 3rd Qu.: 33.0 3rd Qu.: 232.0
## Max. :99.00 Max. :1493.00 Max. :199.0 Max. :1725.0
##
## MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 3.00 1st Qu.: 1.00 1st Qu.: 9.00 1st Qu.: 1.000
## Median : 12.00 Median : 8.00 Median : 24.00 Median : 2.000
## Mean : 37.53 Mean : 27.06 Mean : 44.02 Mean : 2.325
## 3rd Qu.: 50.00 3rd Qu.: 33.00 3rd Qu.: 56.00 3rd Qu.: 3.000
## Max. :259.00 Max. :263.00 Max. :362.00 Max. :15.000
##
## NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 3.00 1st Qu.: 3.000
## Median : 4.000 Median : 2.000 Median : 5.00 Median : 6.000
## Mean : 4.085 Mean : 2.662 Mean : 5.79 Mean : 5.317
## 3rd Qu.: 6.000 3rd Qu.: 4.000 3rd Qu.: 8.00 3rd Qu.: 7.000
## Max. :27.000 Max. :28.000 Max. :13.00 Max. :20.000
##
## AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.07277 Mean :0.07455 Mean :0.07277 Mean :0.06429
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## AcceptedCmp2 Complain Z_CostContact Z_Revenue
## Min. :0.00000 Min. :0.000000 Min. :3 Min. :11
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:3 1st Qu.:11
## Median :0.00000 Median :0.000000 Median :3 Median :11
## Mean :0.01339 Mean :0.009375 Mean :3 Mean :11
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:3 3rd Qu.:11
## Max. :1.00000 Max. :1.000000 Max. :3 Max. :11
##
## Response
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1491
## 3rd Qu.:0.0000
## Max. :1.0000
##
# 4. PREPROCESSING DATA
# Mengecek missing value
colSums(is.na(data))
## ID Year_Birth Education Marital_Status
## 0 0 0 0
## Income Kidhome Teenhome Dt_Customer
## 24 0 0 0
## Recency MntWines MntFruits MntMeatProducts
## 0 0 0 0
## MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases
## 0 0 0 0
## NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth
## 0 0 0 0
## AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1
## 0 0 0 0
## AcceptedCmp2 Complain Z_CostContact Z_Revenue
## 0 0 0 0
## Response
## 0
# Menghapus missing value
data <- na.omit(data)
# Mengecek kembali missing value
colSums(is.na(data))
## ID Year_Birth Education Marital_Status
## 0 0 0 0
## Income Kidhome Teenhome Dt_Customer
## 0 0 0 0
## Recency MntWines MntFruits MntMeatProducts
## 0 0 0 0
## MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases
## 0 0 0 0
## NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth
## 0 0 0 0
## AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1
## 0 0 0 0
## AcceptedCmp2 Complain Z_CostContact Z_Revenue
## 0 0 0 0
## Response
## 0
# 5. PEMILIHAN VARIABEL
pls_data <- data %>%
select(
# Spending Behavior
MntWines,
MntMeatProducts,
MntFishProducts,
MntSweetProducts,
# Purchase Activity
NumWebPurchases,
NumCatalogPurchases,
NumStorePurchases,
# Customer Engagement
NumWebVisitsMonth,
Response,
# Customer Loyalty
Recency,
Complain
)
# Menampilkan data
head(pls_data)
## MntWines MntMeatProducts MntFishProducts MntSweetProducts NumWebPurchases
## 1 635 546 172 88 8
## 2 11 6 2 1 1
## 3 426 127 111 21 8
## 4 11 20 10 3 2
## 5 173 118 46 27 5
## 6 520 98 0 42 6
## NumCatalogPurchases NumStorePurchases NumWebVisitsMonth Response Recency
## 1 10 4 7 1 58
## 2 1 2 5 0 38
## 3 2 10 4 0 26
## 4 0 4 6 0 26
## 5 3 6 5 0 94
## 6 4 10 6 0 16
## Complain
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
# 6. ANALISIS DESKRIPTIF
describe(pls_data)
## vars n mean sd median trimmed mad min max
## MntWines 1 2216 305.09 337.33 174.5 250.28 245.37 0 1493
## MntMeatProducts 2 2216 167.00 224.28 68.0 119.82 88.96 0 1725
## MntFishProducts 3 2216 37.64 54.75 12.0 25.20 17.79 0 259
## MntSweetProducts 4 2216 27.03 41.07 8.0 17.39 11.86 0 262
## NumWebPurchases 5 2216 4.09 2.74 4.0 3.82 2.97 0 27
## NumCatalogPurchases 6 2216 2.67 2.93 2.0 2.22 2.97 0 28
## NumStorePurchases 7 2216 5.80 3.25 5.0 5.49 2.97 0 13
## NumWebVisitsMonth 8 2216 5.32 2.43 6.0 5.40 2.97 0 20
## Response 9 2216 0.15 0.36 0.0 0.06 0.00 0 1
## Recency 10 2216 49.01 28.95 49.0 48.99 37.06 0 99
## Complain 11 2216 0.01 0.10 0.0 0.00 0.00 0 1
## range skew kurtosis se
## MntWines 1493 1.17 0.58 7.17
## MntMeatProducts 1725 2.02 5.03 4.76
## MntFishProducts 259 1.91 3.06 1.16
## MntSweetProducts 262 2.10 4.09 0.87
## NumWebPurchases 27 1.20 4.05 0.06
## NumCatalogPurchases 28 1.88 8.04 0.06
## NumStorePurchases 13 0.70 -0.63 0.07
## NumWebVisitsMonth 20 0.22 1.84 0.05
## Response 1 1.96 1.83 0.01
## Recency 99 0.00 -1.20 0.61
## Complain 1 10.12 100.44 0.00
# 7. VISUALISASI KORELASI
# Matriks korelasi
cor_matrix <- cor(pls_data)
# Menampilkan korelasi
cor_matrix
## MntWines MntMeatProducts MntFishProducts
## MntWines 1.00000000 0.56886000 0.3977210502
## MntMeatProducts 0.56886000 1.00000000 0.5735740153
## MntFishProducts 0.39772105 0.57357402 1.0000000000
## MntSweetProducts 0.39032580 0.53513611 0.5838669550
## NumWebPurchases 0.55378594 0.30709037 0.2996875104
## NumCatalogPurchases 0.63475274 0.73412660 0.5327567837
## NumStorePurchases 0.64001191 0.48600555 0.4577450432
## NumWebVisitsMonth -0.32197790 -0.53948442 -0.4464232918
## Response 0.24629896 0.23774642 0.1081451099
## Recency 0.01572102 0.02251764 0.0005509232
## Complain -0.03947021 -0.02378194 -0.0212202304
## MntSweetProducts NumWebPurchases NumCatalogPurchases
## MntWines 0.39032580 0.553785939 0.63475274
## MntMeatProducts 0.53513611 0.307090366 0.73412660
## MntFishProducts 0.58386696 0.299687510 0.53275678
## MntSweetProducts 1.00000000 0.333937217 0.49513582
## NumWebPurchases 0.33393722 1.000000000 0.38686764
## NumCatalogPurchases 0.49513582 0.386867640 1.00000000
## NumStorePurchases 0.45522516 0.516240183 0.51784045
## NumWebVisitsMonth -0.42237080 -0.051226263 -0.52200377
## Response 0.11617037 0.151431233 0.21991361
## Recency 0.02510977 -0.005640854 0.02408141
## Complain -0.02264120 -0.016641779 -0.02083919
## NumStorePurchases NumWebVisitsMonth Response
## MntWines 0.6400119079 -0.321977901 0.246298957
## MntMeatProducts 0.4860055453 -0.539484417 0.237746418
## MntFishProducts 0.4577450432 -0.446423292 0.108145110
## MntSweetProducts 0.4552251636 -0.422370804 0.116170373
## NumWebPurchases 0.5162401827 -0.051226263 0.151431233
## NumCatalogPurchases 0.5178404511 -0.522003774 0.219913612
## NumStorePurchases 1.0000000000 -0.432398257 0.036241129
## NumWebVisitsMonth -0.4323982573 1.000000000 -0.002208954
## Response 0.0362411292 -0.002208954 1.000000000
## Recency -0.0004338266 -0.018563643 -0.199766369
## Complain -0.0169407070 0.019785006 -0.002029294
## Recency Complain
## MntWines 0.0157210194 -0.039470211
## MntMeatProducts 0.0225176351 -0.023781944
## MntFishProducts 0.0005509232 -0.021220230
## MntSweetProducts 0.0251097703 -0.022641200
## NumWebPurchases -0.0056408538 -0.016641779
## NumCatalogPurchases 0.0240814076 -0.020839191
## NumStorePurchases -0.0004338266 -0.016940707
## NumWebVisitsMonth -0.0185636434 0.019785006
## Response -0.1997663693 -0.002029294
## Recency 1.0000000000 0.013636670
## Complain 0.0136366703 1.000000000
# Visualisasi korelasi
corrplot(
cor_matrix,
method = "color",
type = "upper",
tl.col = "black",
tl.srt = 45
)
# 8. VISUALISASI DATA
# Histogram Spending Behavior
ggplot(pls_data, aes(x = MntWines)) +
geom_histogram(
bins = 20,
fill = "skyblue",
color = "black"
) +
labs(
title = "Distribusi Pembelian Wine",
x = "MntWines",
y = "Frekuensi"
)
# 9. PEMBENTUKAN INNER MODEL
inner_model <- matrix(c(
0, 0, 0, 0,
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0
), nrow = 4, byrow = TRUE)
# Nama konstruk
colnames(inner_model) <- rownames(inner_model) <- c(
"Spending_Behavior",
"Purchase_Activity",
"Customer_Engagement",
"Customer_Loyalty"
)
# Menampilkan inner model
inner_model
## Spending_Behavior Purchase_Activity Customer_Engagement
## Spending_Behavior 0 0 0
## Purchase_Activity 1 0 0
## Customer_Engagement 0 1 0
## Customer_Loyalty 0 0 1
## Customer_Loyalty
## Spending_Behavior 0
## Purchase_Activity 0
## Customer_Engagement 0
## Customer_Loyalty 0
# 10. PEMBENTUKAN OUTER MODEL
outer_model <- list(
c(
"MntWines",
"MntMeatProducts",
"MntFishProducts",
"MntSweetProducts"
),
c(
"NumWebPurchases",
"NumCatalogPurchases",
"NumStorePurchases"
),
c(
"NumWebVisitsMonth",
"Response"
),
c(
"Recency",
"Complain"
)
)
# Tipe indikator
modes <- c("A", "A", "A", "A")
# 11. ANALISIS SEM-PLS
set.seed(123)
pls_model <- plspm(
pls_data,
inner_model,
outer_model,
modes = modes
)
# 12. RINGKASAN HASIL MODEL
summary(pls_model)
## PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
##
## ----------------------------------------------------------
## MODEL SPECIFICATION
## 1 Number of Cases 2216
## 2 Latent Variables 4
## 3 Manifest Variables 11
## 4 Scale of Data Standardized Data
## 5 Non-Metric PLS FALSE
## 6 Weighting Scheme centroid
## 7 Tolerance Crit 1e-06
## 8 Max Num Iters 100
## 9 Convergence Iters 10
## 10 Bootstrapping FALSE
## 11 Bootstrap samples NULL
##
## ----------------------------------------------------------
## BLOCKS DEFINITION
## Block Type Size Mode
## 1 Spending_Behavior Exogenous 4 A
## 2 Purchase_Activity Endogenous 3 A
## 3 Customer_Engagement Endogenous 2 A
## 4 Customer_Loyalty Endogenous 2 A
##
## ----------------------------------------------------------
## BLOCKS UNIDIMENSIONALITY
## Mode MVs C.alpha DG.rho eig.1st eig.2nd
## Spending_Behavior A 4 0.8052 8.73e-01 2.53 0.678
## Purchase_Activity A 3 0.7297 8.48e-01 1.95 0.613
## Customer_Engagement A 2 0.0000 2.72e-23 1.00 0.998
## Customer_Loyalty A 2 0.0269 6.73e-01 1.01 0.986
##
## ----------------------------------------------------------
## OUTER MODEL
## weight loading communality redundancy
## Spending_Behavior
## 1 MntWines 0.374 0.781 0.6107 0.000000
## 1 MntMeatProducts 0.337 0.853 0.7284 0.000000
## 1 MntFishProducts 0.277 0.777 0.6031 0.000000
## 1 MntSweetProducts 0.270 0.758 0.5750 0.000000
## Purchase_Activity
## 2 NumWebPurchases 0.257 0.686 0.4704 0.314553
## 2 NumCatalogPurchases 0.545 0.863 0.7452 0.498247
## 2 NumStorePurchases 0.422 0.837 0.7002 0.468177
## Customer_Engagement
## 3 NumWebVisitsMonth 0.780 -0.782 0.6109 0.142684
## 3 Response -0.624 0.625 0.3912 0.091379
## Customer_Loyalty
## 4 Recency 0.987 0.989 0.9776 0.012080
## 4 Complain 0.150 0.163 0.0266 0.000329
##
## ----------------------------------------------------------
## CROSSLOADINGS
## Spending_Behavior Purchase_Activity
## Spending_Behavior
## 1 MntWines 0.7815 0.7585
## 1 MntMeatProducts 0.8535 0.6843
## 1 MntFishProducts 0.7766 0.5606
## 1 MntSweetProducts 0.7583 0.5479
## Purchase_Activity
## 2 NumWebPurchases 0.4839 0.6859
## 2 NumCatalogPurchases 0.7663 0.8632
## 2 NumStorePurchases 0.6530 0.8368
## Customer_Engagement
## 3 NumWebVisitsMonth -0.5401 -0.4802
## 3 Response 0.2337 0.1742
## Customer_Loyalty
## 4 Recency 0.0204 0.0115
## 4 Complain -0.0348 -0.0228
## Customer_Engagement Customer_Loyalty
## Spending_Behavior
## 1 MntWines 0.4049 0.00960
## 1 MntMeatProducts 0.5692 0.01866
## 1 MntFishProducts 0.4158 -0.00263
## 1 MntSweetProducts 0.4020 0.02139
## Purchase_Activity
## 2 NumWebPurchases 0.1344 -0.00806
## 2 NumCatalogPurchases 0.5445 0.02064
## 2 NumStorePurchases 0.3600 -0.00296
## Customer_Engagement
## 3 NumWebVisitsMonth -0.7816 -0.01535
## 3 Response 0.6255 -0.19741
## Customer_Loyalty
## 4 Recency -0.1101 0.98873
## 4 Complain -0.0167 0.16317
##
## ----------------------------------------------------------
## INNER MODEL
## $Purchase_Activity
## Estimate Std. Error t value Pr(>|t|)
## Intercept -1.98e-16 0.0122 -1.62e-14 1
## Spending_Behavior 8.18e-01 0.0122 6.68e+01 0
##
## $Customer_Engagement
## Estimate Std. Error t value Pr(>|t|)
## Intercept -6.63e-16 0.0186 -3.56e-14 1.00e+00
## Purchase_Activity 4.83e-01 0.0186 2.60e+01 4.59e-130
##
## $Customer_Loyalty
## Estimate Std. Error t value Pr(>|t|)
## Intercept -1.39e-17 0.0211 -6.59e-16 1.00e+00
## Customer_Engagement -1.11e-01 0.0211 -5.26e+00 1.55e-07
##
## ----------------------------------------------------------
## CORRELATIONS BETWEEN LVs
## Spending_Behavior Purchase_Activity Customer_Engagement
## Spending_Behavior 1.0000 0.8177 0.567
## Purchase_Activity 0.8177 1.0000 0.483
## Customer_Engagement 0.5671 0.4833 1.000
## Customer_Loyalty 0.0149 0.0079 -0.111
## Customer_Loyalty
## Spending_Behavior 0.0149
## Purchase_Activity 0.0079
## Customer_Engagement -0.1112
## Customer_Loyalty 1.0000
##
## ----------------------------------------------------------
## SUMMARY INNER MODEL
## Type R2 Block_Communality Mean_Redundancy
## Spending_Behavior Exogenous 0.0000 0.629 0.0000
## Purchase_Activity Endogenous 0.6686 0.639 0.4270
## Customer_Engagement Endogenous 0.2336 0.501 0.1170
## Customer_Loyalty Endogenous 0.0124 0.502 0.0062
## AVE
## Spending_Behavior 0.629
## Purchase_Activity 0.639
## Customer_Engagement 0.501
## Customer_Loyalty 0.502
##
## ----------------------------------------------------------
## GOODNESS-OF-FIT
## [1] 0.4224
##
## ----------------------------------------------------------
## TOTAL EFFECTS
## relationships direct indirect total
## 1 Spending_Behavior -> Purchase_Activity 0.818 0.0000 0.8177
## 2 Spending_Behavior -> Customer_Engagement 0.000 0.3952 0.3952
## 3 Spending_Behavior -> Customer_Loyalty 0.000 -0.0439 -0.0439
## 4 Purchase_Activity -> Customer_Engagement 0.483 0.0000 0.4833
## 5 Purchase_Activity -> Customer_Loyalty 0.000 -0.0537 -0.0537
## 6 Customer_Engagement -> Customer_Loyalty -0.111 0.0000 -0.1112
# 13. EVALUASI OUTER MODEL
# Outer loading
pls_model$outer_model
## name block weight loading communality
## 1 MntWines Spending_Behavior 0.3740758 0.7814980 0.61073911
## 2 MntMeatProducts Spending_Behavior 0.3374870 0.8534756 0.72842060
## 3 MntFishProducts Spending_Behavior 0.2765034 0.7766190 0.60313706
## 4 MntSweetProducts Spending_Behavior 0.2702054 0.7582595 0.57495747
## 5 NumWebPurchases Purchase_Activity 0.2572733 0.6858841 0.47043705
## 6 NumCatalogPurchases Purchase_Activity 0.5454190 0.8632302 0.74516636
## 7 NumStorePurchases Purchase_Activity 0.4215207 0.8367755 0.70019325
## 8 NumWebVisitsMonth Customer_Engagement 0.7802291 -0.7816070 0.61090953
## 9 Response Customer_Engagement -0.6237727 0.6254962 0.39124548
## 10 Recency Customer_Loyalty 0.9866889 0.9887306 0.97758821
## 11 Complain Customer_Loyalty 0.1497196 0.1631747 0.02662599
## redundancy
## 1 0.0000000000
## 2 0.0000000000
## 3 0.0000000000
## 4 0.0000000000
## 5 0.3145525919
## 6 0.4982473352
## 7 0.4681765629
## 8 0.1426836772
## 9 0.0913790679
## 10 0.0120795580
## 11 0.0003290038
# Cross loading
pls_model$crossloadings
## name block Spending_Behavior Purchase_Activity
## 1 MntWines Spending_Behavior 0.78149799 0.75845879
## 2 MntMeatProducts Spending_Behavior 0.85347560 0.68427414
## 3 MntFishProducts Spending_Behavior 0.77661899 0.56062627
## 4 MntSweetProducts Spending_Behavior 0.75825950 0.54785643
## 5 NumWebPurchases Purchase_Activity 0.48389316 0.68588414
## 6 NumCatalogPurchases Purchase_Activity 0.76630125 0.86323019
## 7 NumStorePurchases Purchase_Activity 0.65300586 0.83677551
## 8 NumWebVisitsMonth Customer_Engagement -0.54007755 -0.48015475
## 9 Response Customer_Engagement 0.23366316 0.17418066
## 10 Recency Customer_Loyalty 0.02041739 0.01150035
## 11 Complain Customer_Loyalty -0.03477619 -0.02278843
## Customer_Engagement Customer_Loyalty
## 1 0.40485110 0.009602292
## 2 0.56922118 0.018657279
## 3 0.41577042 -0.002633494
## 4 0.40200991 0.021385701
## 5 0.13442689 -0.008057368
## 6 0.54445866 0.020640823
## 7 0.35997594 -0.002964407
## 8 -0.78160702 -0.015354339
## 9 0.62549618 -0.197411090
## 10 -0.11012491 0.988730608
## 11 -0.01670266 0.163174735
# 14. EVALUASI INNER MODEL
# Path coefficient
pls_model$path_coefs
## Spending_Behavior Purchase_Activity Customer_Engagement
## Spending_Behavior 0.0000000 0.0000000 0.0000000
## Purchase_Activity 0.8177035 0.0000000 0.0000000
## Customer_Engagement 0.0000000 0.4832799 0.0000000
## Customer_Loyalty 0.0000000 0.0000000 -0.1111597
## Customer_Loyalty
## Spending_Behavior 0
## Purchase_Activity 0
## Customer_Engagement 0
## Customer_Loyalty 0
# Nilai R-Square
pls_model$inner_model
## $Purchase_Activity
## Estimate Std. Error t value Pr(>|t|)
## Intercept -1.980099e-16 0.01223381 -1.618546e-14 1
## Spending_Behavior 8.177035e-01 0.01223381 6.683963e+01 0
##
## $Customer_Engagement
## Estimate Std. Error t value Pr(>|t|)
## Intercept -6.631956e-16 0.01860589 -3.564439e-14 1.000000e+00
## Purchase_Activity 4.832799e-01 0.01860589 2.597456e+01 4.593561e-130
##
## $Customer_Loyalty
## Estimate Std. Error t value Pr(>|t|)
## Intercept -1.392153e-17 0.02112085 -6.591368e-16 1.000000e+00
## Customer_Engagement -1.111597e-01 0.02112085 -5.263035e+00 1.553593e-07
# 15. GOODNESS OF FIT
pls_model$gof
## [1] 0.4224461
# 16. VISUALISASI MODEL
plot(pls_model)
# 17. KESIMPULAN
# Interpretasi dilakukan berdasarkan:
# 1. Outer Loading
# 2. Cross Loading
# 3. Path Coefficient
# 4. R-Square
# 5. Goodness of Fit