You’ve been told by Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data in your data set to understand this...

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11/10/2021
li
ary(readr)
li
ary(magrittr)
li
ary(dplyr)
li
ary(ggplot2)
li
ary(rmarkdown)
li
ary(MASS)
li
ary(skimr)
li
ary(ggeffects)
li
ary(readxl)
datasetsmark <- read_excel("~/data/datasetsmark.xlsx")
View(datasetsmark)
skim(datasetsmark)
Data summary
    Name
    datasetsmark
    Number of rows
    2240
    Number of columns
    30
    _______________________
    
    Column type frequency:
    
    characte
    4
    numeric
    26
    ________________________
    
    Group variables
    None
Variable type: characte
    skim_variable
    n_missing
    complete_rate
    min
    max
    empty
    n_unique
    whitespace
    Education
    0
    1
    3
    10
    0
    5
    0
    Marital_Status
    0
    1
    4
    8
    0
    8
    0
    Dt_Custome
    0
    1
    10
    10
    0
    663
    0
    Country
    0
    1
    2
    3
    0
    8
    0
Variable type: numeric
    skim_variable
    n_missing
    complete_rate
    mean
    sd
    p0
    p25
    p50
    p75
    p100
    hist
    ID
    0
    1.00
    5592.16
    3246.66
    0
    2828.25
    5458.5
    8427.75
    11191
    ▇▇▇▇▇
    Year_Birth
    0
    1.00
    1968.81
    11.98
    1893
    1959.00
    1970.0
    1977.00
    1996
    ▁▁▂▇▅
    Income
    24
    0.99
    52247.25
    25173.08
    1730
    35303.00
    51381.5
    68522.00
    666666
    ▇▁▁▁▁
    Kidhome
    0
    1.00
    0.44
    0.54
    0
    0.00
    0.0
    1.00
    2
    ▇▁▆▁▁
    Teenhome
    0
    1.00
    0.51
    0.54
    0
    0.00
    0.0
    1.00
    2
    ▇▁▇▁▁
    Recency
    0
    1.00
    49.11
    28.96
    0
    24.00
    49.0
    74.00
    99
    ▇▇▇▇▇
    MntWines
    0
    1.00
    303.94
    336.60
    0
    23.75
    173.5
    504.25
    1493
    ▇▂▂▁▁
    MntFruits
    0
    1.00
    26.30
    39.77
    0
    1.00
    8.0
    33.00
    199
    ▇▁▁▁▁
    MntMeatProducts
    0
    1.00
    166.95
    225.72
    0
    16.00
    67.0
    232.00
    1725
    ▇▁▁▁▁
    MntFishProducts
    0
    1.00
    37.53
    54.63
    0
    3.00
    12.0
    50.00
    259
    ▇▁▁▁▁
    MntSweetProducts
    0
    1.00
    27.06
    41.28
    0
    1.00
    8.0
    33.00
    263
    ▇▁▁▁▁
    MntGoldProds
    0
    1.00
    44.02
    52.17
    0
    9.00
    24.0
    56.00
    362
    ▇▁▁▁▁
    NumDealsPurchases
    0
    1.00
    2.33
    1.93
    0
    1.00
    2.0
    3.00
    15
    ▇▂▁▁▁
    NumWebPurchases
    0
    1.00
    4.08
    2.78
    0
    2.00
    4.0
    6.00
    27
    ▇▃▁▁▁
    NumCatalogPurchases
    0
    1.00
    2.66
    2.92
    0
    0.00
    2.0
    4.00
    28
    ▇▂▁▁▁
    NumStorePurchases
    0
    1.00
    5.79
    3.25
    0
    3.00
    5.0
    8.00
    13
    ▂▇▂▃▂
    NumWebVisitsMonth
    0
    1.00
    5.32
    2.43
    0
    3.00
    6.0
    7.00
    20
    ▅▇▁▁▁
    AcceptedCmp3
    0
    1.00
    0.07
    0.26
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▁
    AcceptedCmp4
    0
    1.00
    0.07
    0.26
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▁
    AcceptedCmp5
    0
    1.00
    0.07
    0.26
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▁
    AcceptedCmp1
    0
    1.00
    0.06
    0.25
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▁
    AcceptedCmp2
    0
    1.00
    0.01
    0.11
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▁
    Complain
    0
    1.00
    0.01
    0.10
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▁
    Z_CostContact
    0
    1.00
    3.00
    0.00
    3
    3.00
    3.0
    3.00
    3
    ▁▁▇▁▁
    Z_Revenue
    0
    1.00
    11.00
    0.00
    11
    11.00
    11.0
    11.00
    11
    ▁▁▇▁▁
    Response
    0
    1.00
    0.15
    0.36
    0
    0.00
    0.0
    0.00
    1
    ▇▁▁▁▂
You’ve been told by Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data in your data set to understand this problem. Use appropriate graphs and charts to present your analysis to the CMO. Some items to consider are below. Take some time to familiarize yourself with the data set before answering your questions. Please provide a two part analysis for the case as follows:
Q1. What factors seem to drive web purchases?
mark_df<-subset(datasetsmark,select = -Dt_Customer)
mod_we
-lm(NumWebVisitsMonth~.,data=mark_df)
summary(mod_web)
##
## Call:
## lm(formula = NumWebVisitsMonth ~ ., data = mark_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.1215 -0.9668 -0.0001 0.9005 15.4410
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. E
or t value Pr(>|t|)
## (Intercept) -1.349e+01 6.447e+00 -2.092 0.03652 *
## ID 6.554e-06 1.045e-05 0.627 0.53060
## Year_Birth 9.737e-03 3.202e-03 3.041 0.00239 **
## EducationBasic 1.327e-01 2.485e-01 0.534 0.59339
## EducationGraduation -2.310e-02 1.234e-01 -0.187 0.85155
## EducationMaster -2.929e-01 1.439e-01 -2.035 0.04194 *
## EducationPhD -2.334e-01 1.416e-01 -1.648 0.09957 .
## Marital_StatusAlone -2.280e-01 1.466e+00 -0.156 0.87642
## Marital_StatusDivorced 8.752e-01 1.146e+00 0.764 0.44499
## Marital_StatusMa
ied 8.380e-01 1.142e+00 0.734 0.46307
## Marital_StatusSingle 7.611e-01 1.143e+00 0.666 0.50552
## Marital_StatusTogether 8.254e-01 1.143e+00 0.722 0.47009
## Marital_StatusWidow 8.733e-01 1.155e+00 0.756 0.44973
## Marital_StatusYOLO 1.616e+00 1.605e+00 1.007 0.31410
## Income -2.648e-05 1.924e-06 -13.764 < 2e-16 ***
## Kidhome 4.763e-01 8.630e-02 5.519 3.81e-08 ***
## Teenhome -1.929e-01 7.909e-02 -2.439 0.01480 *
## Recency 1.537e-04 1.205e-03 0.128 0.89849
## MntWines 1.533e-03 1.845e-04 8.310 < 2e-16 ***
## MntFruits -1.562e-03 1.188e-03 -1.314 0.18884
## MntMeatProducts -1.426e-03 2.634e-04 -5.414 6.85e-08 ***
## MntFishProducts -2.179e-03 9.063e-04 -2.404 0.01629 *
## MntSweetProducts -2.821e-03 1.146e-03 -2.461 0.01393 *
## MntGoldProds -1.087e-03 8.125e-04 -1.337 0.18124
## NumDealsPurchases 2.865e-01 2.189e-02 13.090 < 2e-16 ***
## NumWebPurchases 2.045e-01 1.683e-02 12.154 < 2e-16 ***
## NumCatalogPurchases -1.926e-01 1.995e-02 -9.657 < 2e-16 ***
## NumStorePurchases -1.728e-01 1.581e-02 -10.932 < 2e-16 ***
## AcceptedCmp3 3.361e-01 1.391e-01 2.415 0.01580 *
## AcceptedCmp4 2.341e-01 1.517e-01 1.543 0.12286
## AcceptedCmp5 -9.165e-01 1.671e-01 -5.485 4.61e-08 ***
## AcceptedCmp1 2.329e-02 1.610e-01 0.145 0.88498
## AcceptedCmp2 6.613e-01 3.148e-01 2.101 0.03578 *
## Complain 1.240e-01 3.501e-01 0.354 0.72335
## Z_CostContact NA NA NA NA
## Z_Revenue NA NA NA NA
## Response 4.885e-01 1.138e-01 4.295 1.83e-05 ***
## CountryCA 1.108e-01 1.643e-01 0.675 0.49992
## CountryGER 1.022e-01 1.986e-01 0.514 0.60704
## CountryIND 8.785e-02 1.870e-01 0.470 0.63857
## CountryME 8.917e-01 9.308e-01 0.958 0.33816
## CountrySA 1.251e-01 1.579e-01 0.792 0.42839
## CountrySP 1.177e-01 1.406e-01 0.838 0.40232
## CountryUS 4.325e-01 2.030e-01 2.130 0.03328 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard e
or: 1.585 on 2174 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.5809, Adjusted R-squared: 0.573
## F-statistic: 73.51 on 41 and...