Customer Behavior Analysis is a process that involves examining and understanding how customers interact with a business, product, or service. This analysis helps organizations make informed decisions, tailor their strategies, and enhance customer experiences. If you want to learn how to analyze customer behaviour on a platform, this article is for you. In this article, I’ll take you through the task of Customer Behaviour Analysis using Python.
Customer Behaviour Analysis: Process We Can Follow
Customer Behavior Analysis is a valuable process that empowers businesses to make data-driven decisions, enhance customer experiences, and remain competitive in a dynamic market. Below is the process we can follow for the task of Customer Behaviour Analysis:
- Collect data related to customer interactions. It can include purchase history, website visits, social media engagement, customer feedback, and more.
- Identify and address data inconsistencies, missing values, and outliers to ensure the data’s quality and accuracy.
- Calculate basic statistics like mean, median, and standard deviation to summarize data.
- Create visualizations such as histograms, scatter plots, and bar charts to explore trends, patterns, and anomalies in the data.
- Use techniques like clustering to group customers based on common behaviours or characteristics.
So, the process starts with collecting data based on customer behaviour on a platform. I found an ideal dataset for this task. You can download the data from here.
Customer Behaviour Analysis using Python
Now, let’s get started with the task of Customer Behaviour Analysis by importing the necessary Python libraries and the dataset:
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv("ecommerce_customer_data.csv")
print(data.head())User_ID Gender Age Location Device_Type Product_Browsing_Time \ 0 1 Female 23 Ahmedabad Mobile 60 1 2 Male 25 Kolkata Tablet 30 2 3 Male 32 Bangalore Desktop 37 3 4 Male 35 Delhi Mobile 7 4 5 Male 27 Bangalore Tablet 35 Total_Pages_Viewed Items_Added_to_Cart Total_Purchases 0 30 1 0 1 38 9 4 2 13 5 0 3 20 10 3 4 20 8 2
Before moving forward, let’s have a look at the summary statistics for both numerical and categorical columns in the dataset:
# Summary statistics for numeric columns numeric_summary = data.describe() print(numeric_summary)
User_ID Age Product_Browsing_Time Total_Pages_Viewed \
count 500.000000 500.000000 500.000000 500.000000
mean 250.500000 26.276000 30.740000 27.182000
std 144.481833 5.114699 15.934246 13.071596
min 1.000000 18.000000 5.000000 5.000000
25% 125.750000 22.000000 16.000000 16.000000
50% 250.500000 26.000000 31.000000 27.000000
75% 375.250000 31.000000 44.000000 38.000000
max 500.000000 35.000000 60.000000 50.000000
Items_Added_to_Cart Total_Purchases
count 500.000000 500.000000
mean 5.150000 2.464000
std 3.203127 1.740909
min 0.000000 0.000000
25% 2.000000 1.000000
50% 5.000000 2.000000
75% 8.000000 4.000000
max 10.000000 5.000000
# Summary for non-numeric columns categorical_summary = data.describe(include='object') print(categorical_summary)
Gender Location Device_Type count 500 500 500 unique 2 8 3 top Male Kolkata Mobile freq 261 71 178
Now, let’s have a look at the distribution of age in the dataset:
# Histogram for 'Age' fig = px.histogram(data, x='Age', title='Distribution of Age') fig.show()
Now, let’s have a look at the gender distribution:
# Bar chart for 'Gender'
gender_counts = data['Gender'].value_counts().reset_index()
gender_counts.columns = ['Gender', 'Count']
fig = px.bar(gender_counts, x='Gender',
y='Count',
title='Gender Distribution')
fig.show()
Analyzing Customer Behaviour
Now, let’s have a look at the relationship between the product browsing time and the total pages viewed:
# 'Product_Browsing_Time' vs 'Total_Pages_Viewed'
fig = px.scatter(data, x='Product_Browsing_Time', y='Total_Pages_Viewed',
title='Product Browsing Time vs. Total Pages Viewed',
trendline='ols')
fig.show()
The above scatter plot shows no consistent pattern or strong association between the time spent browsing products and the total number of pages viewed. It indicates that customers are not necessarily exploring more pages if they spend more time on the website, which might be due to various factors such as the website design, content relevance, or individual user preferences.
Now, let’s have a look at the average total pages viewed by gender:
# Grouped Analysis
gender_grouped = data.groupby('Gender')['Total_Pages_Viewed'].mean().reset_index()
gender_grouped.columns = ['Gender', 'Average_Total_Pages_Viewed']
fig = px.bar(gender_grouped, x='Gender', y='Average_Total_Pages_Viewed',
title='Average Total Pages Viewed by Gender')
fig.show()
Now, let’s have a look at the average total pages viewed by devices:
devices_grouped = data.groupby('Device_Type')['Total_Pages_Viewed'].mean().reset_index()
devices_grouped.columns = ['Device_Type', 'Average_Total_Pages_Viewed']
fig = px.bar(devices_grouped, x='Device_Type', y='Average_Total_Pages_Viewed',
title='Average Total Pages Viewed by Devices')
fig.show()
Now, let’s calculate the customer lifetime value and visualize segments based on the customer lifetime value:
data['CLV'] = (data['Total_Purchases'] * data['Total_Pages_Viewed']) / data['Age']
data['Segment'] = pd.cut(data['CLV'], bins=[1, 2.5, 5, float('inf')],
labels=['Low Value', 'Medium Value', 'High Value'])
segment_counts = data['Segment'].value_counts().reset_index()
segment_counts.columns = ['Segment', 'Count']
# Create a bar chart to visualize the customer segments
fig = px.bar(segment_counts, x='Segment', y='Count',
title='Customer Segmentation by CLV')
fig.update_xaxes(title='Segment')
fig.update_yaxes(title='Number of Customers')
fig.show()
Now, let’s have a look at the conversion funnel of the customers:
# Funnel analysis funnel_data = data[['Product_Browsing_Time', 'Items_Added_to_Cart', 'Total_Purchases']] funnel_data = funnel_data.groupby(['Product_Browsing_Time', 'Items_Added_to_Cart']).sum().reset_index() fig = px.funnel(funnel_data, x='Product_Browsing_Time', y='Items_Added_to_Cart', title='Conversion Funnel') fig.show()
In the above graph, the x-axis represents the time customers spend browsing products on the e-commerce platform. The y-axis represents the number of items added to the shopping cart by customers during their browsing sessions.
Now, let’s have a look at the churn rate of the customers:
# Calculate churn rate data['Churned'] = data['Total_Purchases'] == 0 churn_rate = data['Churned'].mean() print(churn_rate)
0.198
A churn rate of 0.198 indicates that a significant portion of customers has churned, and addressing this churn is important for maintaining business growth and profitability.
So, this is how you can analyze customer behaviour on a platform using Python. You can find many more Data Analysis projects solved and explained using Python here.
Summary
Customer Behavior Analysis is a process that involves examining and understanding how customers interact with a business, product, or service. This analysis helps organizations make informed decisions, tailor their strategies, and enhance customer experiences. I hope you liked this article on Customer Behaviour Analysis using Python. Feel free to ask valuable questions in the comments section below.
