E-commerce giants design and run frequent campaigns on their touchpoints which also includes websites to attract more and more customers. The purpose of this paper is to investigate the effectiveness of a newly launched web page for consumers and find out if the new page is resulting in different consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’ helps us find out if the different user groups of old and new web page are significantly different from each other based on conversion rate or not!

1. A/B Testing - Customer Experience Platformexperimentation using Pearson’s Chi-
Squared Test
Aurangzeb Khan
Senior Data Analyst
rana.aurangzeb@hotmail.com
MBA, University of Wollongong, Australia
Abstract: E-commerce giants design and run frequent campaigns on their touch points which includes
website to attract more and more customers. The purpose of this paper is to investigate the effectiveness
of a newly launched web page for consumers and find out if the new page is resulting into different
consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’
helps us find out if the different user groups of old and new web page are significantly different from each
other based on conversion rate or not!
The Business Problem
As described in Kaggle (Kaggle link is here), an e-commerce company has designed a new web
page for a website to attract more customers. The e-commerce company wants to investigate if
it should implement the new page or keep the old web page.
Many of the times the consumer/user groups are exposed to and/ or studied based on different
situation (before and after a change) to find out if there is a significant difference in terms of their
performance/consumer behavior using some set of metrics like web site visits, click-through-rate
and the conversion rate. The ‘control’ consumer group is exposed to the ‘old page’ and the
‘treatment’ consumer group is exposed to the ‘new page’ of the website. Now the e-commerce
company wants to know that if the two consumergroups are significantly different from each other
in terms of conversion rate & hence consumer behavior.
Analytical Problem
To determine if the two user/consumer groups exposed to ‘old page’ vs ‘new page’ (consumer
group being the categorical variable) are different in term of click-through-rate and conversion
rates we recommend using Pearson’s Chi-Squared Test for Independence. The Chi-Square
Test is suitable for quantifying the independence of pairs of categorical variables i.e the click and
non-click behavior of the consumers against the website page design.
Chi Square also tell us if the input variable has significant impact on the output variable and hence
will let us choose or drop certain variables when we decide to continue feature selection for the
Analysis.

2. Formula : Chi Square
Image source: Author
Fo: Observed Frequencies
Fe: Expected Frequencies
Steps to conduct Chi-Square of Independence
1. Data wrangling & data consolidation in the shape of contingency table
2. Hypothesis Formulation & Decision Rule
3. Data Visualization
4. Test Statistics calculation
5. Conclusion
1. Data wrangling & Data consolidation in the shape of contingency table
The pairs of categorical variables i.e user group and the click/non-click variables will be displayed
in a contingency table to help us visualize the frequency distribution of the variables.
Below is an example of how the overall data should look like:
Click No-Click Click + No-
Click
Old Page 17489 127785 145,274
New Page 17264 128047 145,311
Old Page + New
Page
34,753 255,832 290,585
Table 1.0: Sample Format of the Data

3. For Chi Square Test we need the Data in below format
Click No-Click
Old Page 17489 127785
New Page 17264 128047
Table 2.0: Sample format for Contingency Table
First of all, we need to import the Python (Data Analysis programming language) libraries and
the data from Kaggle and visualize it.
Python Code:
# Import necessary Libraries
import numpy as np
import pandas as pd
import seaborn as sns
import scipy
import matplotlib.pyplot asplt
# The data has been taken fromKaggle per below link
# https://www.kaggle.com/zhangluyuan/ab-testing
df = pd.read_csv('ab_data.csv')
df.head()
Table 3.0: Simple Data Visualization

4. Let's perform few steps to validate if the data is clean and is ready for Chi Square Test
Python Code:
# The control group represents the users of Old Page
# The treatment group represents the users of new Page
# Let’s see how the data looks like
df.groupby(['group','landing_page']).count()
Table 4.0: Data Aggregation for Visualization
We have noticed above that some users in ‘control group’ have visited ‘new page’ and the data
is wrongly classifying against our objectives. We have also noticed that some users in
‘treatment group’ have visited ‘old page’ and the data is wrongly classified against our
objectives. Now instead of cleaning the data we can only pick the relevant correct data
(control/new_page and treatment/old_page) with the help of below Python code.
Python Code:
# from 'Control Group' we only need old page
# From 'Treatment Group' we only neednew page
df_cleaned= df.loc[(df['group'] == 'control') & (df['landing_page'] == 'old_page') |(df['group'] == 'treatment')
& (df['landing_page'] == 'new_page')]
df_cleaned.groupby(['group','landing_page']).count()
Table 5.0: Cleansed and consolidated Data for both user groups

5. Finding Duplicates
Python Code:
# Checking for duplicate values
print(df_cleaned['user_id'].duplicated().sum())
# Finding user_idfor duplicate value
df_cleaned[df_cleaned.duplicated(['user_id'],keep=False)]['user_id']
# Now we need to drop the Duplicates
df_cleaned= df.drop_duplicates(subset='user_id',keep="first")
Preparing the Contingency Tabe for Chi-Square Test
### To prepare and arrange the Data for Chi-Square Contigency Table
# 1) Take out the Control group
control = df_cleaned[df_cleaned['group'] == 'control']
# 2) Take out the Treatment group
treatment = df_cleaned[df_cleaned['group'] == 'treatment']
# 2A) A-click -i.e The ones who convertedfrom Control group
control_click = control.converted.sum()
# 2B) No-click,i.e The one who did not click fromControl group
control_noclick = control.converted.size -control.converted.sum()
#3 B-click, B-noclick
# 3A) A-click -i.e The ones who convertedfrom Treatment group
treatment_click = treatment.converted.sum()
# 2B) No-click,i.e The one who did not click fromTreatment group
treatment_noclick = treatment.converted.size - treatment.converted.sum()
# 3) Create np array
Table = np.array([[control_click, control_noclick], [treatment_click, treatment_noclick]])
print(Table)
2. Hypothesis Formulation & Decision Rule
Null Hypothesis
H0:
The ‘control’ user group and ‘treatment’ user group are independent in terms of their conversion rate.
Alterative hypothesis
H1:
The ‘control’ user group and ‘treatment’ user group are dependent and different in terms of their
conversion rate
Level of significance
For this test, we assume that α = 0.05 or Confidence Interval = 95%
Decision Rule
If p-value is less than Level of significance (5%) then we will Reject Null Hypothesis (H0).

6. 3. Data Visualization
Let’s printthe multidimensional array thatwe created in Python :
Click No-Click
Old Page 17471 127761
New Page 17274 128078
Table 6.0: Chi-Square Test Contingency Table
4. Test Statistics Calculations
To perform the Test let’s import the necessary Python libraries and get the following parameters
1. Test Statistics
2. P- Value
3. Degree of Freedom
4. Expected Frequencies
Python Code:
import scipy
from scipy importstats
# The correction will Adjustthe observerdvalue by .5 towards the corressponding ExpectedValues
stat,p,dof,expected = scipy.stats.chi2_contingency(Table,correction=True)
print('nStat : ',stat)
print('nP-Value : ',p)
print('nDegree of Freedom : ',dof)
print('nObservedFrequencies: ',Table)
print('nExpectedFrequencies: ',expected)
Snapshot 1.0: Chi-Square Test Results

7. Python Code:
# interpret p-value
alpha = 1.0 - .95
if p <= alpha:
print('Dependent(reject H0)')
else:
print('Independent(fail to reject H0)')
5. Conclusion:
The p-value is 22.9% at 5% level of significance. As the p-value is greater than alpha so we do
not Reject the Null Hypothesis
The old and new page's users did not behave significantly different and the conversion ratio is
not significantly different. Hence, the new web page is not different from the old one.
The conversion rate is considered independent as the observed and expected frequencies are
similar, the variables do not interact and are not dependent.
THE END