1 Introduction

Google Analytics is a well known Web Analytics Platform that allows businesses to track the consumer acquisition, engagement, beahvior and conversion through their website. Even though it might be considered as a basic platform, it follows the standard Data Science process where it collects data from website user’s browser, transforms and cleans based on business input and provides analysis of the data via the Google Analytics User Interface. In my final project, I decided to leverage Google Analytics Data to solve one of the most commont problems Marketing Departments within businesses solve everyday. The high level problem statement is “How should our business allocate their Marketing Budget on weekly, daily, yearly or even daily basis?” . In order to explore this problem further, we would need to look at the Google Analytics Data in detail for Unique visitors, Sessions, Channel Grouping, Transaction Revenue and New vs Returning Consumer. Our purpose of this analysis is to provide actionable insights to the marketing team so they can use their budget based on the business conversion goals. Instead of using a Power Point Presentation, we will build an engaging analysis and shiny app that works with our Google Analytics Data.

2 About the Data

In this case, since I wasnt able to access any real business data, I decided to leverage a Google Analytics Customer Revenue Prediction Competion data available by Kaggle. The full Data set can be found here: ( https://www.kaggle.com/c/ga-customer-revenue-prediction/data ) . The raw Toogle Analytics data between the dates of August 1st 2016 and April 30th 2018, for the Google Merchandise store. YOu can access to the Google Merchandise Store data through the Google Analytics Demo Account (https://analytics.google.com/analytics/web/?utm_source=demoaccount&utm_medium=demoaccount&utm_campaign=demoaccount#/report-home/a54516992w87479473p92320289).

##Data Fields

fullVisitorId- A unique identifier for each user of the Google Merchandise Store.

channelGrouping - The channel via which the user came to the Store.

date - The date on which the user visited the Store.

device - The specifications for the device used to access the Store.

geoNetwork - This section contains information about the geography of the user.

socialEngagementType - Engagement type, either “Socially Engaged” or “Not Socially Engaged”.

totals - This section contains aggregate values across the session.

trafficSource - This section contains information about the Traffic Source from which the session originated.

**visitId - An identifier for this session. This is part of the value usually stored as the _utmb cookie. This is only unique to the user. For a completely unique ID, you should use a combination of fullVisitorId and visitId.**

visitNumber - The session number for this user. If this is the first session, then this is set to 1.

visitStartTime - The timestamp (expressed as POSIX time).

hits - This row and nested fields are populated for any and all types of hits. Provides a record of all page visits.

customDimensions - This section contains any user-level or session-level custom dimensions that are set for a session. This is a repeated field and has an entry for each dimension that is set.

totals - This set of columns mostly includes high-level aggregate data.

3 Data Collection

3.1 Load Libraries

library("readxl")
## Warning: package 'readxl' was built under R version 3.6.3
library(shiny)              
## Warning: package 'shiny' was built under R version 3.6.2
#library(googleAuthR)        # To prompt for authentication by the user
#library(googleAnalyticsR)   # For the pulling of the data
library(tidyverse)          
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library(dplyr)
library(lubridate)
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require(shinydashboard)
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library(gridExtra)
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library(psych)
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library(ggpubr)
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library(ggcorrplot)   

For this purpose of this project, we will only use the train data and will not touch the test data provided by Kaggle.

ga_train <- read.csv("train.csv")

4 Data Cleaning and Transformation

As we can see our dataset is a mess. It is the raw data that is pulled in from Big Query in csv format and needs to be cleaned in order for us to perform any analysis and visualization. The columns device, geoNetwork, totals and Traffic Source has multiple key value pairs.

#JSON columns are "device", "geoNetwork", "totals", "trafficSource"

tr_device <- paste("[", paste(ga_train$device, collapse = ","), "]") %>% fromJSON(flatten = T)
tr_geoNetwork <- paste("[", paste(ga_train$geoNetwork, collapse = ","), "]") %>% fromJSON(flatten = T)
tr_totals <- paste("[", paste(ga_train$totals, collapse = ","), "]") %>% fromJSON(flatten = T)
tr_trafficSource <- paste("[", paste(ga_train$trafficSource, collapse = ","), "]") %>% fromJSON(flatten = T)


#Combine to make the full training and test sets
ga_train <- ga_train %>%
    cbind(tr_device, tr_geoNetwork, tr_totals, tr_trafficSource) %>%
    select(-device, -geoNetwork, -totals, -trafficSource)
    

#Remove temporary tr_ and te_ sets
rm(tr_device)
rm(tr_geoNetwork)
rm(tr_totals)
rm(tr_trafficSource)


# convert some character variables to categorical variables
factorVars <- c("channelGrouping", "browser", "operatingSystem", "deviceCategory", "country")
ga_train[, factorVars] <- lapply(ga_train[, factorVars], as.factor)

#converting the date variable from integer to the date format
ga_train$date <- ymd(ga_train$date)

#converting character variables into numeric
numVars <- c("visits", "hits", "bounces", "pageviews", "newVisits")
ga_train[, numVars] <- lapply(ga_train[, numVars], as.integer)
ga_train$transactionRevenue <- as.numeric(ga_train$transactionRevenue)

#converting visit start times to POSIXct
ga_train$visitStartTime <- as.POSIXct(ga_train$visitStartTime, tz="UTC", origin='1970-01-01')

Let’s look at the structure and descriptive statistic of our data.

# look at descriptive statistics
metastats <- data.frame(describe(ga_train))
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metastats <- tibble::rownames_to_column(metastats, "STATS")
metastats["pct_missing"] <- round(metastats["n"]/903653, 3)
head(metastats)
##                  STATS vars      n         mean           sd          min
## 1      channelGrouping    1 903653          NaN           NA          Inf
## 2                 date    2 903653          NaN           NA          Inf
## 3        fullVisitorId    3 903653 4.505845e+18 3.071128e+18 4.823595e+12
## 4            sessionId    4 903653          NaN           NA          Inf
## 5 socialEngagementType    5 903653          NaN           NA          Inf
## 6              visitId    6 903653 1.485007e+09 9.022124e+06 1.470035e+09
##            max        range           se pct_missing
## 1         -Inf         -Inf           NA           1
## 2         -Inf         -Inf           NA           1
## 3 5.198308e+19 5.198307e+19 3.230703e+15           1
## 4         -Inf         -Inf           NA           1
## 5         -Inf         -Inf           NA           1
## 6 1.501657e+09 3.162238e+07 9.490911e+03           1
#summary of the dataset
summary(ga_train)
##        channelGrouping        date            fullVisitorId      
##  Organic Search:381561   Min.   :2016-08-01   Min.   :4.824e+12  
##  Social        :226117   1st Qu.:2016-10-27   1st Qu.:1.593e+18  
##  Direct        :143026   Median :2017-01-09   Median :4.388e+18  
##  Referral      :104838   Mean   :2017-01-20   Mean   :4.506e+18  
##  Paid Search   : 25326   3rd Qu.:2017-04-21   3rd Qu.:7.195e+18  
##  Affiliates    : 16403   Max.   :2017-08-01   Max.   :5.198e+19  
##  (Other)       :  6382                                           
##                           sessionId                socialEngagementType
##  0011338928267756760_1471848731:     2   Not Socially Engaged:903653   
##  0014884852016449602_1483257533:     2                                 
##  0019612470994343873_1496731675:     2                                 
##  0031542961723593073_1495261490:     2                                 
##  0039798900163330977_1485331011:     2                                 
##  0047064119019485310_1480924286:     2                                 
##  (Other)                       :903641                                 
##     visitId           visitNumber      visitStartTime               
##  Min.   :1.470e+09   Min.   :  1.000   Min.   :2016-08-01 07:00:12  
##  1st Qu.:1.478e+09   1st Qu.:  1.000   1st Qu.:2016-10-27 09:39:29  
##  Median :1.484e+09   Median :  1.000   Median :2017-01-09 08:02:14  
##  Mean   :1.485e+09   Mean   :  2.265   Mean   :2017-01-21 14:01:30  
##  3rd Qu.:1.493e+09   3rd Qu.:  1.000   3rd Qu.:2017-04-21 07:12:42  
##  Max.   :1.502e+09   Max.   :395.000   Max.   :2017-08-02 06:59:53  
##                                                                     
##               browser       browserVersion     browserSize       
##  Chrome           :620364   Length:903653      Length:903653     
##  Safari           :182245   Class :character   Class :character  
##  Firefox          : 37069   Mode  :character   Mode  :character  
##  Internet Explorer: 19375                                        
##  Edge             : 10205                                        
##  Android Webview  :  7865                                        
##  (Other)          : 26530                                        
##   operatingSystem   operatingSystemVersion  isMobile       mobileDeviceBranding
##  Windows  :350072   Length:903653          Mode :logical   Length:903653       
##  Macintosh:253938   Class :character       FALSE:664530    Class :character    
##  Android  :123892   Mode  :character       TRUE :239123    Mode  :character    
##  iOS      :107665                                                              
##  Linux    : 35034                                                              
##  Chrome OS: 26337                                                              
##  (Other)  :  6715                                                              
##  mobileDeviceModel  mobileInputSelector mobileDeviceInfo  
##  Length:903653      Length:903653       Length:903653     
##  Class :character   Class :character    Class :character  
##  Mode  :character   Mode  :character    Mode  :character  
##                                                           
##                                                           
##                                                           
##                                                           
##  mobileDeviceMarketingName flashVersion         language        
##  Length:903653             Length:903653      Length:903653     
##  Class :character          Class :character   Class :character  
##  Mode  :character          Mode  :character   Mode  :character  
##                                                                 
##                                                                 
##                                                                 
##                                                                 
##  screenColors       screenResolution   deviceCategory    continent        
##  Length:903653      Length:903653      desktop:664479   Length:903653     
##  Class :character   Class :character   mobile :208725   Class :character  
##  Mode  :character   Mode  :character   tablet : 30449   Mode  :character  
##                                                                           
##                                                                           
##                                                                           
##                                                                           
##  subContinent                 country          region         
##  Length:903653      United States :364744   Length:903653     
##  Class :character   India         : 51140   Class :character  
##  Mode  :character   United Kingdom: 37393   Mode  :character  
##                     Canada        : 25869                     
##                     Vietnam       : 24598                     
##                     Turkey        : 20522                     
##                     (Other)       :379387                     
##     metro               city              cityId          networkDomain     
##  Length:903653      Length:903653      Length:903653      Length:903653     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    latitude          longitude         networkLocation        visits 
##  Length:903653      Length:903653      Length:903653      Min.   :1  
##  Class :character   Class :character   Class :character   1st Qu.:1  
##  Mode  :character   Mode  :character   Mode  :character   Median :1  
##                                                           Mean   :1  
##                                                           3rd Qu.:1  
##                                                           Max.   :1  
##                                                                      
##       hits           pageviews         bounces         newVisits     
##  Min.   :  1.000   Min.   :  1.00   Min.   :1        Min.   :1       
##  1st Qu.:  1.000   1st Qu.:  1.00   1st Qu.:1        1st Qu.:1       
##  Median :  2.000   Median :  1.00   Median :1        Median :1       
##  Mean   :  4.596   Mean   :  3.85   Mean   :1        Mean   :1       
##  3rd Qu.:  4.000   3rd Qu.:  4.00   3rd Qu.:1        3rd Qu.:1       
##  Max.   :500.000   Max.   :469.00   Max.   :1        Max.   :1       
##                    NA's   :100      NA's   :453023   NA's   :200593  
##  transactionRevenue    campaign            source             medium         
##  Min.   :1.000e+04   Length:903653      Length:903653      Length:903653     
##  1st Qu.:2.493e+07   Class :character   Class :character   Class :character  
##  Median :4.945e+07   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :1.337e+08                                                           
##  3rd Qu.:1.077e+08                                                           
##  Max.   :2.313e+10                                                           
##  NA's   :892138                                                              
##    keyword          isTrueDirect   referralPath        adContent        
##  Length:903653      Mode:logical   Length:903653      Length:903653     
##  Class :character   TRUE:274005    Class :character   Class :character  
##  Mode  :character   NA's:629648    Mode  :character   Mode  :character  
##                                                                         
##                                                                         
##                                                                         
##                                                                         
##  campaignCode       adwordsClickInfo.criteriaParameters adwordsClickInfo.page
##  Length:903653      Length:903653                       Length:903653        
##  Class :character   Class :character                    Class :character     
##  Mode  :character   Mode  :character                    Mode  :character     
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##  adwordsClickInfo.slot adwordsClickInfo.gclId adwordsClickInfo.adNetworkType
##  Length:903653         Length:903653          Length:903653                 
##  Class :character      Class :character       Class :character              
##  Mode  :character      Mode  :character       Mode  :character              
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##  adwordsClickInfo.isVideoAd
##  Mode :logical             
##  FALSE:21460               
##  NA's :882193              
##                            
##                            
##                            
## 

We have 903653 obvervations, with 55 variables. SessionId is the unique identifier for each user. We can see that the number of distinct sessionid is almost same as the number of observations.

Based on the Channel Grouping, we are acquiring majority of the users via Organic Search, followed by Social. Majority of the visitors are using Chrome as their browser, using Desktop as a device and are from United Staes.

Let’s look at the missing values in more detail.

colSums(is.na(ga_train))
##                     channelGrouping                                date 
##                                   0                                   0 
##                       fullVisitorId                           sessionId 
##                                   0                                   0 
##                socialEngagementType                             visitId 
##                                   0                                   0 
##                         visitNumber                      visitStartTime 
##                                   0                                   0 
##                             browser                      browserVersion 
##                                   0                                   0 
##                         browserSize                     operatingSystem 
##                                   0                                   0 
##              operatingSystemVersion                            isMobile 
##                                   0                                   0 
##                mobileDeviceBranding                   mobileDeviceModel 
##                                   0                                   0 
##                 mobileInputSelector                    mobileDeviceInfo 
##                                   0                                   0 
##           mobileDeviceMarketingName                        flashVersion 
##                                   0                                   0 
##                            language                        screenColors 
##                                   0                                   0 
##                    screenResolution                      deviceCategory 
##                                   0                                   0 
##                           continent                        subContinent 
##                                   0                                   0 
##                             country                              region 
##                                   0                                   0 
##                               metro                                city 
##                                   0                                   0 
##                              cityId                       networkDomain 
##                                   0                                   0 
##                            latitude                           longitude 
##                                   0                                   0 
##                     networkLocation                              visits 
##                                   0                                   0 
##                                hits                           pageviews 
##                                   0                                 100 
##                             bounces                           newVisits 
##                              453023                              200593 
##                  transactionRevenue                            campaign 
##                              892138                                   0 
##                              source                              medium 
##                                   0                                   0 
##                             keyword                        isTrueDirect 
##                              502929                              629648 
##                        referralPath                           adContent 
##                              572712                              892707 
##                        campaignCode adwordsClickInfo.criteriaParameters 
##                              903652                                   0 
##               adwordsClickInfo.page               adwordsClickInfo.slot 
##                              882193                              882193 
##              adwordsClickInfo.gclId      adwordsClickInfo.adNetworkType 
##                              882092                              882193 
##          adwordsClickInfo.isVideoAd 
##                              882193

We have a lot of missing values in campaigncode, transactionrevenue and some other variables. For the purpose of this project, we wont be handling these missing values. As we can see Channel Grouping variable which gives us the variety of Marketing Channels that we acquire the users from dont have any missing values along with sessionid, visit number.

#Let's look at our columns
head(ga_train)
##   channelGrouping       date fullVisitorId                      sessionId
## 1  Organic Search 2016-09-02  1.131660e+18 1131660440785968503_1472830385
## 2  Organic Search 2016-09-02  3.773060e+17  377306020877927890_1472880147
## 3  Organic Search 2016-09-02  3.895546e+18 3895546263509774583_1472865386
## 4  Organic Search 2016-09-02  4.763447e+18 4763447161404445595_1472881213
## 5  Organic Search 2016-09-02  2.729444e+16   27294437909732085_1472822600
## 6  Organic Search 2016-09-02  2.938943e+18 2938943183656635653_1472807194
##   socialEngagementType    visitId visitNumber      visitStartTime    browser
## 1 Not Socially Engaged 1472830385           1 2016-09-02 15:33:05     Chrome
## 2 Not Socially Engaged 1472880147           1 2016-09-03 05:22:27    Firefox
## 3 Not Socially Engaged 1472865386           1 2016-09-03 01:16:26     Chrome
## 4 Not Socially Engaged 1472881213           1 2016-09-03 05:40:13 UC Browser
## 5 Not Socially Engaged 1472822600           2 2016-09-02 13:23:20     Chrome
## 6 Not Socially Engaged 1472807194           1 2016-09-02 09:06:34     Chrome
##                  browserVersion                   browserSize operatingSystem
## 1 not available in demo dataset not available in demo dataset         Windows
## 2 not available in demo dataset not available in demo dataset       Macintosh
## 3 not available in demo dataset not available in demo dataset         Windows
## 4 not available in demo dataset not available in demo dataset           Linux
## 5 not available in demo dataset not available in demo dataset         Android
## 6 not available in demo dataset not available in demo dataset         Windows
##          operatingSystemVersion isMobile          mobileDeviceBranding
## 1 not available in demo dataset    FALSE not available in demo dataset
## 2 not available in demo dataset    FALSE not available in demo dataset
## 3 not available in demo dataset    FALSE not available in demo dataset
## 4 not available in demo dataset    FALSE not available in demo dataset
## 5 not available in demo dataset     TRUE not available in demo dataset
## 6 not available in demo dataset    FALSE not available in demo dataset
##               mobileDeviceModel           mobileInputSelector
## 1 not available in demo dataset not available in demo dataset
## 2 not available in demo dataset not available in demo dataset
## 3 not available in demo dataset not available in demo dataset
## 4 not available in demo dataset not available in demo dataset
## 5 not available in demo dataset not available in demo dataset
## 6 not available in demo dataset not available in demo dataset
##                mobileDeviceInfo     mobileDeviceMarketingName
## 1 not available in demo dataset not available in demo dataset
## 2 not available in demo dataset not available in demo dataset
## 3 not available in demo dataset not available in demo dataset
## 4 not available in demo dataset not available in demo dataset
## 5 not available in demo dataset not available in demo dataset
## 6 not available in demo dataset not available in demo dataset
##                    flashVersion                      language
## 1 not available in demo dataset not available in demo dataset
## 2 not available in demo dataset not available in demo dataset
## 3 not available in demo dataset not available in demo dataset
## 4 not available in demo dataset not available in demo dataset
## 5 not available in demo dataset not available in demo dataset
## 6 not available in demo dataset not available in demo dataset
##                    screenColors              screenResolution deviceCategory
## 1 not available in demo dataset not available in demo dataset        desktop
## 2 not available in demo dataset not available in demo dataset        desktop
## 3 not available in demo dataset not available in demo dataset        desktop
## 4 not available in demo dataset not available in demo dataset        desktop
## 5 not available in demo dataset not available in demo dataset         mobile
## 6 not available in demo dataset not available in demo dataset        desktop
##   continent    subContinent        country                        region
## 1      Asia    Western Asia         Turkey                         Izmir
## 2   Oceania     Australasia      Australia not available in demo dataset
## 3    Europe Southern Europe          Spain           Community of Madrid
## 4      Asia  Southeast Asia      Indonesia not available in demo dataset
## 5    Europe Northern Europe United Kingdom not available in demo dataset
## 6    Europe Southern Europe          Italy not available in demo dataset
##                           metro                          city
## 1                     (not set)                         Izmir
## 2 not available in demo dataset not available in demo dataset
## 3                     (not set)                        Madrid
## 4 not available in demo dataset not available in demo dataset
## 5 not available in demo dataset not available in demo dataset
## 6 not available in demo dataset not available in demo dataset
##                          cityId   networkDomain                      latitude
## 1 not available in demo dataset    ttnet.com.tr not available in demo dataset
## 2 not available in demo dataset     dodo.net.au not available in demo dataset
## 3 not available in demo dataset unknown.unknown not available in demo dataset
## 4 not available in demo dataset unknown.unknown not available in demo dataset
## 5 not available in demo dataset unknown.unknown not available in demo dataset
## 6 not available in demo dataset   fastwebnet.it not available in demo dataset
##                       longitude               networkLocation visits hits
## 1 not available in demo dataset not available in demo dataset      1    1
## 2 not available in demo dataset not available in demo dataset      1    1
## 3 not available in demo dataset not available in demo dataset      1    1
## 4 not available in demo dataset not available in demo dataset      1    1
## 5 not available in demo dataset not available in demo dataset      1    1
## 6 not available in demo dataset not available in demo dataset      1    1
##   pageviews bounces newVisits transactionRevenue  campaign source  medium
## 1         1       1         1                 NA (not set) google organic
## 2         1       1         1                 NA (not set) google organic
## 3         1       1         1                 NA (not set) google organic
## 4         1       1         1                 NA (not set) google organic
## 5         1       1        NA                 NA (not set) google organic
## 6         1       1         1                 NA (not set) google organic
##           keyword isTrueDirect referralPath adContent campaignCode
## 1  (not provided)           NA         <NA>      <NA>         <NA>
## 2  (not provided)           NA         <NA>      <NA>         <NA>
## 3  (not provided)           NA         <NA>      <NA>         <NA>
## 4 google + online           NA         <NA>      <NA>         <NA>
## 5  (not provided)         TRUE         <NA>      <NA>         <NA>
## 6  (not provided)           NA         <NA>      <NA>         <NA>
##   adwordsClickInfo.criteriaParameters adwordsClickInfo.page
## 1       not available in demo dataset                  <NA>
## 2       not available in demo dataset                  <NA>
## 3       not available in demo dataset                  <NA>
## 4       not available in demo dataset                  <NA>
## 5       not available in demo dataset                  <NA>
## 6       not available in demo dataset                  <NA>
##   adwordsClickInfo.slot adwordsClickInfo.gclId adwordsClickInfo.adNetworkType
## 1                  <NA>                   <NA>                           <NA>
## 2                  <NA>                   <NA>                           <NA>
## 3                  <NA>                   <NA>                           <NA>
## 4                  <NA>                   <NA>                           <NA>
## 5                  <NA>                   <NA>                           <NA>
## 6                  <NA>                   <NA>                           <NA>
##   adwordsClickInfo.isVideoAd
## 1                         NA
## 2                         NA
## 3                         NA
## 4                         NA
## 5                         NA
## 6                         NA

5 Data Exploration

Let’s further perform explanatory data analysis to this data set.

c <- ga_train %>% 
  ggplot(aes(channelGrouping)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())

  
se <- ga_train %>% 
  ggplot(aes(socialEngagementType)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())


d <- ga_train %>% 
  ggplot(aes(deviceCategory)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())


co <- ga_train %>% 
  ggplot(aes(continent)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())



me <- ga_train %>% 
  ggplot(aes(medium)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())


ggarrange(c, se, d, co, me, nrow = 5)

We can see that overall Organic Search is providing the biggest consumer acquisition for the Google Store, next channel is Social followed by Referral. I think this makes sense as Organic Search is ruled by Google’s Search Algorithm and they dont neccessarily need any paid or display media for user acquisition.

Let’s look at Sessions and Transaction Revenue, one of the usual business goals of an ecommerce business.

# Assign $0 value to the TransactionRevenu that has missing values.
ga_train$transactionRevenue[is.na(ga_train$transactionRevenue)] <- 0

y <- ga_train$transactionRevenue #saving original values in a vector
ga_train$transactionRevenue <- ga_train$transactionRevenue/1000000


ga_train %>% filter(transactionRevenue >0) %>% summarize('number of transactions'=n(), 'total revenues train set'=sum(transactionRevenue))
##   number of transactions total revenues train set
## 1                  11515                  1540071

We dont neccessarily need to see the transaction values that are 0 or less than $1,000

range(ga_train %>% select(transactionRevenue) %>% filter(transactionRevenue !=0))
## [1]     0.01 23129.50
ga_train %>% filter(transactionRevenue>=1000) %>% summarize('number of transactions with at least 1000 USD revenues'=n(), 'sum revenues of transactions with at least 1000 USD revenues'=sum(transactionRevenue))
##   number of transactions with at least 1000 USD revenues
## 1                                                    195
##   sum revenues of transactions with at least 1000 USD revenues
## 1                                                     418588.3
z1 <- ga_train %>% 
  group_by(date) %>% 
  summarise(dailySessions = n()) %>%
  ggplot(aes(x=date, y=dailySessions)) + geom_line(col='plum') +
  scale_y_continuous(labels=comma) + geom_smooth(col='tomato') +
  labs(x="", y="Sessions per Day") + scale_x_date(date_breaks = "1 month", date_labels = "%b %d")

z2 <- ga_train %>% 
  group_by(date) %>% 
  summarise(dailyRevenue = sum(transactionRevenue)) %>%
  ggplot(aes(x=date, y=dailyRevenue)) + geom_line(col='plum') +
  scale_y_continuous(labels=comma) + geom_smooth(col='tomato') +
  labs(x="", y="Daily Revenues (USD)") + scale_x_date(date_breaks = "1 month", date_labels = "%b %d")

grid.arrange(z1,z2)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

We can see that sessions have increased starting October and peaked in November, however went back to normal in January. When we compare the Daily Revenus within the same time frame we see that it stayed flat. This means, users are engaging with the Google eStore, however not contributing to the purchase.

Let’s analyze further to see when consumers are engaging with the website the most.

# creating this function to plot the next two visualizations easily

plotSessions <- function(dataframe, factorVariable, topN=10) {
    var_col <- enquo(factorVariable)
    dataframe %>% count(!!var_col) %>% top_n(topN, wt=n) %>%
    ggplot(aes_(x=var_col, y=~n, fill=var_col)) +
    geom_bar(stat='identity')+
    scale_y_continuous(labels=comma)+
    labs(x="", y="number of sessions")+
    theme(legend.position="none")
    }

#also creating a function to plot transactionRevenue for a factorvariable
plotRevenue <- function(dataframe, factorVariable, topN=10) {
    var_col <- enquo(factorVariable)
    dataframe %>% group_by(!!var_col) %>% summarize(rev=sum(transactionRevenue)) %>% filter(rev>0) %>% top_n(topN, wt=rev) %>% ungroup() %>%
    ggplot(aes_(x=var_col, y=~rev, fill=var_col)) +
    geom_bar(stat='identity')+
    scale_y_continuous(labels=comma)+
    labs(x="", y="Revenues (USD)")+
    theme(legend.position="none")
    }
# create weekday
ga_train$weekday <- wday(ga_train$date, label=TRUE)
options(repr.plot.height=4)
week1 <- plotSessions(ga_train, weekday)
week2 <- plotRevenue(ga_train, weekday)
grid.arrange(week1, week2)

ga_train$month <- month(ga_train$date, label=TRUE)


m1 <- plotSessions(ga_train, month, 12)
m2 <- plotRevenue(ga_train, month, 12)
grid.arrange(m1, m2)

Based on this analysis, we can see that users engage with the website the most on Tuesday and Wednesday and contribute to the Revenue the most on Tuesday. We had a spike in number of sessions on November, however didnt contribute to the Revenue. However, when we look at the December, even though we didnt have as much user engagement compare to November and October, we have the biggest contribution to the Revenue.

Let’s look at our Marketing Channels and their contribution to the user engagement and Revenue since that is what we want to explore in terms of solving business problem.

#adding reordering of x manually
sessionOrder <- ga_train %>% 
  count(channelGrouping) %>% 
  top_n(10, wt=n) %>% 
  arrange(desc(n))
sessionOrder <- sessionOrder$channelGrouping

channel1 <- plotSessions(ga_train, channelGrouping) + scale_x_discrete(limits=sessionOrder)
channel2 <- plotRevenue(ga_train, channelGrouping) + scale_x_discrete(limits=sessionOrder)
grid.arrange(channel1, channel2)

We can see that Organic Search has the highest number of Sessions however does not bring the biggest Revenue. Even thogh Referral has the 4th place in terms of User Engagement with the site, we see that it brings the highest contribution to the Revenue by far.

6 Conclusion

Even though as businesses we would like to have higher user acqusition and engagement, that doesnt always means that this meets their business goals. Depending on the website, business goals can mean content consumption, lead generation or higher purchase conversion which is the case in our analysis. We may not neccessarily want to have higher sessions but rather we would like to have higher unique visitors or higher revenue and allcote our Channel Distribution budgets accordingly.

Finally, we have complied these analysis into the below shiny apps to further present the visualization of Channel Performance.

References: https://www.youtube.com/watch?v=4Ht_vEXJ4wo : Create a Talking GA Shiny App, https://medium.com/compassred-data-blog/google-analytics-dashboards-in-r-shiny-fc8e0ebcef2c : Google Analytics Dashboard in R.

---
title: "Final Project Data 608"
author: Anil Akyildirim
date: "5/6/2020"
output:
  html_document:
    code_download: yes
    code_folding: hide
    highlight: pygments
    number_sections: yes
    theme: flatly
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
---


# Introduction

Google Analytics is a well known Web Analytics Platform that allows businesses to track the consumer acquisition, engagement, beahvior and conversion through their website. Even though it might be considered as a basic platform, it follows the standard Data Science process where it collects data from website user's browser, transforms and cleans based on business input and provides analysis of the data via the Google Analytics User Interface. In my final project, I decided to leverage Google Analytics Data to solve one of the most commont problems Marketing Departments within businesses solve everyday. The high level problem statement is "How should our business allocate their Marketing Budget on weekly, daily, yearly or even daily basis?" . In order to explore this problem further, we would need to look at the Google Analytics Data in detail for Unique visitors, Sessions, Channel Grouping, Transaction Revenue and New vs Returning Consumer. Our purpose of this analysis is to provide actionable insights to the marketing team so they can use their budget based on the business conversion goals. Instead of using a Power Point Presentation, we will build an engaging analysis and shiny app that works with our Google Analytics Data. 


#  About the Data

In this case, since I wasnt able to access any real business data, I decided to leverage a Google Analytics Customer Revenue Prediction Competion data available by Kaggle. The full Data set can be found here: ( https://www.kaggle.com/c/ga-customer-revenue-prediction/data ) . The raw Toogle Analytics data between the dates of August 1st 2016 and April 30th 2018, for the Google Merchandise store. YOu can access to the Google Merchandise Store data through the Google Analytics Demo Account (https://analytics.google.com/analytics/web/?utm_source=demoaccount&utm_medium=demoaccount&utm_campaign=demoaccount#/report-home/a54516992w87479473p92320289). 

##Data Fields

**fullVisitorId- A unique identifier for each user of the Google Merchandise Store.**

**channelGrouping - The channel via which the user came to the Store.**

**date - The date on which the user visited the Store.**

**device - The specifications for the device used to access the Store.**

**geoNetwork - This section contains information about the geography of the user.**

**socialEngagementType - Engagement type, either "Socially Engaged" or "Not Socially Engaged".**

**totals - This section contains aggregate values across the session.**

**trafficSource - This section contains information about the Traffic Source from which the session originated.**

**visitId - An identifier for this session. This is part of the value usually stored as the _utmb cookie. This is only unique to the user. For a completely unique ID, you should use a combination of fullVisitorId and visitId.**

**visitNumber - The session number for this user. If this is the first session, then this is set to 1.**

**visitStartTime - The timestamp (expressed as POSIX time).**

**hits - This row and nested fields are populated for any and all types of hits. Provides a record of all page visits.**

**customDimensions - This section contains any user-level or session-level custom dimensions that are set for a session. This is a repeated field and has an entry for each dimension that is set.**

**totals - This set of columns mostly includes high-level aggregate data.**

# Data Collection  

## Load Libraries

```{r}

library("readxl")
library(shiny)              
#library(googleAuthR)        # To prompt for authentication by the user
#library(googleAnalyticsR)   # For the pulling of the data
library(tidyverse)          
library(scales)
library(googleCharts)
library(dplyr)
library(lubridate)
require(shinydashboard)
library(ggplot2)
library(plotly)
library(stringr)
library(knitr)
library(kableExtra)
library(knitr)
library(corrplot)
library(ggplot2)
library(jsonlite)
library(scales)
library(lubridate)
library(repr)
library(ggrepel)
library(gridExtra)
library(tidyr)
library(psych)
library(ggpubr)
library(ggcorrplot)   



```

For this purpose of this project, we will only use the train data and will not touch the test data provided by Kaggle. 

```{r}

ga_train <- read.csv("train.csv")


```


# Data Cleaning and Transformation


As we can see our dataset is a mess. It is the raw data that is pulled in from Big Query in csv format and needs to be cleaned in order for us to perform any analysis and visualization. The columns device, geoNetwork, totals and Traffic Source has multiple key value pairs. 


```{r}

#JSON columns are "device", "geoNetwork", "totals", "trafficSource"

tr_device <- paste("[", paste(ga_train$device, collapse = ","), "]") %>% fromJSON(flatten = T)
tr_geoNetwork <- paste("[", paste(ga_train$geoNetwork, collapse = ","), "]") %>% fromJSON(flatten = T)
tr_totals <- paste("[", paste(ga_train$totals, collapse = ","), "]") %>% fromJSON(flatten = T)
tr_trafficSource <- paste("[", paste(ga_train$trafficSource, collapse = ","), "]") %>% fromJSON(flatten = T)


#Combine to make the full training and test sets
ga_train <- ga_train %>%
    cbind(tr_device, tr_geoNetwork, tr_totals, tr_trafficSource) %>%
    select(-device, -geoNetwork, -totals, -trafficSource)
    

#Remove temporary tr_ and te_ sets
rm(tr_device)
rm(tr_geoNetwork)
rm(tr_totals)
rm(tr_trafficSource)


# convert some character variables to categorical variables
factorVars <- c("channelGrouping", "browser", "operatingSystem", "deviceCategory", "country")
ga_train[, factorVars] <- lapply(ga_train[, factorVars], as.factor)

#converting the date variable from integer to the date format
ga_train$date <- ymd(ga_train$date)

#converting character variables into numeric
numVars <- c("visits", "hits", "bounces", "pageviews", "newVisits")
ga_train[, numVars] <- lapply(ga_train[, numVars], as.integer)
ga_train$transactionRevenue <- as.numeric(ga_train$transactionRevenue)

#converting visit start times to POSIXct
ga_train$visitStartTime <- as.POSIXct(ga_train$visitStartTime, tz="UTC", origin='1970-01-01')


```



Let's look at the structure and descriptive statistic of our data.



```{r}
# look at descriptive statistics
metastats <- data.frame(describe(ga_train))
metastats <- tibble::rownames_to_column(metastats, "STATS")
metastats["pct_missing"] <- round(metastats["n"]/903653, 3)
head(metastats)
```


```{r}
#summary of the dataset
summary(ga_train)

```


We have 903653 obvervations, with 55 variables. SessionId is the unique identifier for each user. We can see that the number of distinct sessionid is almost same as the number of observations. 

Based on the Channel Grouping, we are acquiring majority of the users via Organic Search, followed by Social. Majority of the visitors are using Chrome as their browser, using Desktop as a device and are from United Staes.  

Let's look at the missing values in more detail.

```{r}

colSums(is.na(ga_train))

```

We have a lot of missing values in campaigncode, transactionrevenue and some other variables. For the purpose of this project, we wont be handling these missing values. As we can see Channel Grouping variable which gives us the variety of Marketing Channels that we acquire the users from dont have any missing values along with sessionid, visit number.

```{r}
#Let's look at our columns
head(ga_train)


```

# Data Exploration

Let's further perform explanatory data analysis to this data set.

```{r fig1, fig.height=8, fig.width= 15, fig.align='center'}

c <- ga_train %>% 
  ggplot(aes(channelGrouping)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())

  
se <- ga_train %>% 
  ggplot(aes(socialEngagementType)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())


d <- ga_train %>% 
  ggplot(aes(deviceCategory)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())


co <- ga_train %>% 
  ggplot(aes(continent)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())



me <- ga_train %>% 
  ggplot(aes(medium)) +
  geom_bar(aes(fill=channelGrouping), position = position_dodge())


ggarrange(c, se, d, co, me, nrow = 5)

```

We can see that overall Organic Search is providing the biggest consumer acquisition for the Google Store, next channel is Social followed by Referral. I think this makes sense as Organic Search is ruled by Google's Search Algorithm and they dont neccessarily need any paid or display media for user acquisition.


Let's look at Sessions and Transaction Revenue, one of the usual business goals of an ecommerce business.

```{r}
# Assign $0 value to the TransactionRevenu that has missing values.
ga_train$transactionRevenue[is.na(ga_train$transactionRevenue)] <- 0

y <- ga_train$transactionRevenue #saving original values in a vector
ga_train$transactionRevenue <- ga_train$transactionRevenue/1000000


ga_train %>% filter(transactionRevenue >0) %>% summarize('number of transactions'=n(), 'total revenues train set'=sum(transactionRevenue))


```


We dont neccessarily need to see the transaction values that are 0 or less than $1,000


```{r}

range(ga_train %>% select(transactionRevenue) %>% filter(transactionRevenue !=0))
ga_train %>% filter(transactionRevenue>=1000) %>% summarize('number of transactions with at least 1000 USD revenues'=n(), 'sum revenues of transactions with at least 1000 USD revenues'=sum(transactionRevenue))


```





```{r fig13, fig.height=10, fig.width= 15, fig.align='center'}

z1 <- ga_train %>% 
  group_by(date) %>% 
  summarise(dailySessions = n()) %>%
  ggplot(aes(x=date, y=dailySessions)) + geom_line(col='plum') +
  scale_y_continuous(labels=comma) + geom_smooth(col='tomato') +
  labs(x="", y="Sessions per Day") + scale_x_date(date_breaks = "1 month", date_labels = "%b %d")

z2 <- ga_train %>% 
  group_by(date) %>% 
  summarise(dailyRevenue = sum(transactionRevenue)) %>%
  ggplot(aes(x=date, y=dailyRevenue)) + geom_line(col='plum') +
  scale_y_continuous(labels=comma) + geom_smooth(col='tomato') +
  labs(x="", y="Daily Revenues (USD)") + scale_x_date(date_breaks = "1 month", date_labels = "%b %d")

grid.arrange(z1,z2)

```

We can see that sessions have increased starting October and peaked in November, however went back to normal in January. When we compare the Daily Revenus within the same time frame we see that it stayed flat. This means, users are engaging with the Google eStore, however not contributing to the purchase.

Let's analyze further to see when consumers are engaging with the website the most.


```{r}
# creating this function to plot the next two visualizations easily

plotSessions <- function(dataframe, factorVariable, topN=10) {
    var_col <- enquo(factorVariable)
    dataframe %>% count(!!var_col) %>% top_n(topN, wt=n) %>%
    ggplot(aes_(x=var_col, y=~n, fill=var_col)) +
    geom_bar(stat='identity')+
    scale_y_continuous(labels=comma)+
    labs(x="", y="number of sessions")+
    theme(legend.position="none")
    }

#also creating a function to plot transactionRevenue for a factorvariable
plotRevenue <- function(dataframe, factorVariable, topN=10) {
    var_col <- enquo(factorVariable)
    dataframe %>% group_by(!!var_col) %>% summarize(rev=sum(transactionRevenue)) %>% filter(rev>0) %>% top_n(topN, wt=rev) %>% ungroup() %>%
    ggplot(aes_(x=var_col, y=~rev, fill=var_col)) +
    geom_bar(stat='identity')+
    scale_y_continuous(labels=comma)+
    labs(x="", y="Revenues (USD)")+
    theme(legend.position="none")
    }


```



```{r}
# create weekday
ga_train$weekday <- wday(ga_train$date, label=TRUE)


```



```{r fig14, fig.height=10, fig.width= 15, fig.align='center'}

options(repr.plot.height=4)
week1 <- plotSessions(ga_train, weekday)
week2 <- plotRevenue(ga_train, weekday)
grid.arrange(week1, week2)



```

```{r fig15, fig.height=10, fig.width= 15, fig.align='center'}


ga_train$month <- month(ga_train$date, label=TRUE)


m1 <- plotSessions(ga_train, month, 12)
m2 <- plotRevenue(ga_train, month, 12)
grid.arrange(m1, m2)




```

Based on this analysis, we can see that users engage with the website the most on Tuesday and Wednesday and contribute to the Revenue the most on Tuesday. We had a spike in number of sessions on November, however didnt contribute to the Revenue. However, when we look at the December, even though we didnt have as much user engagement compare to November and October, we have the biggest contribution to the Revenue. 

Let's look at our Marketing Channels and their contribution to the user engagement and Revenue since that is what we want to explore in terms of solving business problem.


```{r fig16, fig.height=10, fig.width= 15, fig.align='center'}

#adding reordering of x manually
sessionOrder <- ga_train %>% 
  count(channelGrouping) %>% 
  top_n(10, wt=n) %>% 
  arrange(desc(n))
sessionOrder <- sessionOrder$channelGrouping

channel1 <- plotSessions(ga_train, channelGrouping) + scale_x_discrete(limits=sessionOrder)
channel2 <- plotRevenue(ga_train, channelGrouping) + scale_x_discrete(limits=sessionOrder)
grid.arrange(channel1, channel2)



```

We can see that Organic Search has the highest number of Sessions however does not bring the biggest Revenue. Even thogh Referral has the 4th place in terms of User Engagement with the site, we see that it brings the highest contribution to the Revenue by far.

# Conclusion


Even though as businesses we would like to have higher user acqusition and engagement, that doesnt always means that this meets their business goals. Depending on the website, business goals can mean content consumption, lead generation or higher purchase conversion which is the case in our analysis. We may not neccessarily want to have higher sessions but rather we would like to have higher unique visitors or higher revenue and allcote our Channel Distribution budgets accordingly.

Finally, we have complied these analysis into the below shiny apps to further present the visualization of Channel Performance. 

References: https://www.youtube.com/watch?v=4Ht_vEXJ4wo : Create a Talking GA Shiny App, https://medium.com/compassred-data-blog/google-analytics-dashboards-in-r-shiny-fc8e0ebcef2c : Google Analytics Dashboard in R. 

