Business Statistics: E-News Express
Daniel Njuguna Kuria
2025-12-12
E-News Express — Business Statistics Project
Problem Statement
E-News Express wants to test whether a new landing page improves user engagement and conversions compared to the old page.
Objectives
- To perform univariate analysis
- To perform bivariate analysis
- To provide insight and business recommendations
# Reading the file
data <- read.csv("C:\\Users\\njugu\\Downloads\\New folder\\abtest.csv")
View(data)
head(data)#first few row## user_id group landing_page time_spent_on_the_page converted
## 1 546592 control old 3.48 no
## 2 546468 treatment new 7.13 yes
## 3 546462 treatment new 4.40 no
## 4 546567 control old 3.02 no
## 5 546459 treatment new 4.75 yes
## 6 546558 control old 5.28 yes
## language_preferred
## 1 Spanish
## 2 English
## 3 Spanish
## 4 French
## 5 Spanish
## 6 Englishtail(data)# the last rows## user_id group landing_page time_spent_on_the_page converted
## 95 546550 control old 3.05 no
## 96 546446 treatment new 5.15 no
## 97 546544 control old 6.52 yes
## 98 546472 treatment new 7.07 yes
## 99 546481 treatment new 6.20 yes
## 100 546483 treatment new 5.86 yes
## language_preferred
## 95 English
## 96 Spanish
## 97 English
## 98 Spanish
## 99 Spanish
## 100 Englishdim(data)#shape of data## [1] 100 6str(data)#structure of the data## 'data.frame': 100 obs. of 6 variables:
## $ user_id : int 546592 546468 546462 546567 546459 546558 546448 546581 546461 546548 ...
## $ group : chr "control" "treatment" "treatment" "control" ...
## $ landing_page : chr "old" "new" "new" "old" ...
## $ time_spent_on_the_page: num 3.48 7.13 4.4 3.02 4.75 ...
## $ converted : chr "no" "yes" "no" "no" ...
## $ language_preferred : chr "Spanish" "English" "Spanish" "French" ...summary(data)# summary statistics## user_id group landing_page time_spent_on_the_page
## Min. :546443 Length:100 Length:100 Min. : 0.190
## 1st Qu.:546468 Class :character Class :character 1st Qu.: 3.880
## Median :546493 Mode :character Mode :character Median : 5.415
## Mean :546517 Mean : 5.378
## 3rd Qu.:546567 3rd Qu.: 7.022
## Max. :546592 Max. :10.710
## converted language_preferred
## Length:100 Length:100
## Class :character Class :character
## Mode :character Mode :character
##
##
## colSums(is.na(data))# check for missing error## user_id group landing_page
## 0 0 0
## time_spent_on_the_page converted language_preferred
## 0 0 0sum(duplicated(data))#check for duplicates## [1] 0# UNIVARIATE ANALYSIS
# Histogram
hist(data$time_spent_on_the_page,
main="Histogram of Time Spent",
xlab="Minutes",
col="skyblue",
border="black")
# Boxplot
boxplot(data$time_spent_on_the_page,
main="Boxplot of Time Spent",
col="lightgreen")
# Frequency table
table(data$landing_page)##
## new old
## 50 50# Proportions
prop.table(table(data$landing_page))##
## new old
## 0.5 0.5# Landing Page
table(data$landing_page)##
## new old
## 50 50prop.table(table(data$landing_page))##
## new old
## 0.5 0.5barplot(table(data$landing_page),
main='DISTRIBUTION OF LANDING PAGE',
col='lightgreen')
# Converted
table(data$converted)##
## no yes
## 46 54prop.table(table(data$converted))##
## no yes
## 0.46 0.54barplot(table(data$converted),
main='DISTRIBUTION OF CONVERSION RATE',
col='skyblue')
# Language Preferred
table(data$language_preferred)##
## English French Spanish
## 32 34 34prop.table(table(data$language_preferred))##
## English French Spanish
## 0.32 0.34 0.34barplot(
table(data$language_preferred),
main = "DISTRIBUTION OF LANGUAGE PREFERRED",
col = c("skyblue", "green", "lightgreen")
)
# BIVARIATE ANALYSIS
# Do users spend more time on the new landing page?
# hypothesis
#Null hypothesis : Users spend the same amount of time on old and new pages.
#Alternative hypothesis : Users spend more time on the new page
#If p-value < 0.05, users spend significantly more time on the new page.
# Separate groups
time_old <- data %>% filter(landing_page=="old") %>% pull(time_spent_on_the_page)
time_new <- data %>% filter(landing_page=="new") %>% pull(time_spent_on_the_page)
# Boxplot for comparison
boxplot(time_spent_on_the_page ~ landing_page, data=data,
main="Time Spent by Landing Page",
col=c("lightblue","lightgreen"))
# Step 5: t-test -one-tailed
t.test(time_new, time_old, alternative="greater")##
## Welch Two Sample t-test
##
## data: time_new and time_old
## t = 3.7868, df = 87.975, p-value = 0.0001392
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 0.9485536 Inf
## sample estimates:
## mean of x mean of y
## 6.2232 4.5324#Is conversion rate higher for new page?
#H_0: Conversion rate is the same for old and new pages.
#H_1: Conversion rate is higher for the new page.
# Contingency table
conv_tab <- table(data$landing_page, data$converted)
conv_tab##
## no yes
## new 17 33
## old 29 21# Proportion test
prop.test(x = c(conv_tab["new","yes"], conv_tab["old","yes"]),
n = c(sum(conv_tab["new",]), sum(conv_tab["old",])),
alternative = "greater")##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(conv_tab["new", "yes"], conv_tab["old", "yes"]) out of c(sum(conv_tab["new", ]), sum(conv_tab["old", ]))
## X-squared = 4.8712, df = 1, p-value = 0.01365
## alternative hypothesis: greater
## 95 percent confidence interval:
## 0.06086519 1.00000000
## sample estimates:
## prop 1 prop 2
## 0.66 0.42#Is conversion independent of preferred language?
#H_0:Conversion is independent of language.
#H_1:Conversion depends on language.
lang_tab <- table(data$language_preferred, data$converted)
lang_tab##
## no yes
## English 11 21
## French 19 15
## Spanish 16 18chisq.test(lang_tab)##
## Pearson's Chi-squared test
##
## data: lang_tab
## X-squared = 3.093, df = 2, p-value = 0.213#Is time spent same for different language users (new page only)?
#H_0:Mean time spent is the same across languages.
#H_1:some of the language group spend different time
new_page <- data %>% filter(landing_page=="new")
anova_result <- aov(time_spent_on_the_page ~ language_preferred, data=new_page)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## language_preferred 2 5.68 2.838 0.854 0.432
## Residuals 47 156.10 3.321we conclude that there is no significance in language and time spent
CONCLUSION
The new landing page is more effective at engaging users and encouraging conversions. It performs consistently well across different languages. This suggest that the redesign of the new page is successful in the conversion of users
RECOMMENDATION
The new page should replace the old page
#Since users stay longer on the new page the business should add more personalized recomendation to the users on the new page ## Based on the analysis the new landing page performs better than the old landing page and should be adopted as the primary page which will help the E-NEWS Express to get more subscribers and be more competitive