Comprehensive Report on Global E-Commerce Trends and Their Impact on Traditional
Retail
1. Introduction
This report presents a detailed analysis of global e-commerce trends and their impact on
traditional retail. Using the Kaggle Amazon Sales dataset, we examined various facets of ecommerce performance, including sales volumes, customer behavior, and market dynamics.
The primary aim was to understand how e-commerce trends influence traditional retail and
provide actionable insights based on data analysis.
2. Methodology
2.1 Data Collection and Preprocessing
Data Source: The dataset used for this analysis is the Kaggle Amazon Sales dataset,
comprising 1,465 entries with 16 columns, including product and pricing details, user reviews,
and more.
Preprocessing Steps:
1. Handling Missing Values:
o
Filled missing values in rating_count with the median value.
o
Converted necessary columns (discounted_price, actual_price,
discount_percentage, rating) from object to numeric types.
2. Outlier Detection and Treatment:
o
Applied box plots to identify outliers in numerical columns.
o
Capped outliers using the 5th and 95th percentiles.
3. Feature Engineering:
o
Created new features: discount_amount and price_per_rating to analyze
discount impact and price efficiency.
4. Normalization and Encoding:
o
Normalized numerical variables using standardization.
o
Encoded categorical variables (category) while avoiding the product_name
column to prevent excessive dimensionality.
3. Dimensionality Reduction with PCA
3.1 PCA Application
Principal Component Analysis (PCA) was applied to reduce the dimensionality of the dataset
while retaining significant variance.
Steps Taken:
1. Standardize Data: Ensured all numerical features were on a similar scale.
2. Apply PCA: Determined the number of components required to explain 90% of the
variance.
Results:
•
Cumulative Explained Variance: The first component explains 95.6% of the variance,
indicating that a single component is sufficient for capturing most of the data’s
variance.
3.2 Visualization
•
PCA Plot: The PCA plot showed data points concentrated along a single line, indicating
that most variance is explained by one principal component.
3.3 Impact of Dimensionality Reduction
Dimensionality reduction using PCA simplified the data structure, reducing complexity and
focusing on the principal variance factors, although it led to some loss of detailed information.
4. Customer Lifetime Value (CLV)
4.1 CLV Calculation
Using basic assumptions:
•
Average Purchase Value: Calculated using the discounted_price.
•
Purchase Frequency: Count of transactions per user.
•
Retention Rate: Assumed as 50%.
The CLV was calculated for each user segment, revealing significant variability.
4.2 Visualization
•
Distribution of CLV: A histogram and boxplot were used to visualize CLV distribution
across different categories. The analysis showed varying CLV values, with higher values
typically associated with e-commerce platforms.
5. What-If Analysis
5.1 Scenario Analysis
•
Increased Average Transaction Value: Analyzed how a 20% increase in average
transaction value affects CLV.
•
Increased Purchase Frequency: Examined the impact of a 20% increase in purchase
frequency on CLV.
Visualization: A histogram comparing baseline CLV and new CLV with increased values
illustrated the significant impact of these changes on CLV.
6. Insights and Narrative
6.1 Key Insights
1. E-Commerce Dominance: E-commerce trends show high correlation with key factors,
particularly average transaction values and customer ratings.
2. CLV Distribution: CLV varies significantly across customer segments, with higher
values observed in e-commerce.
3. Discount Impact: Discounts affect both purchase behavior and CLV, with complex
long-term effects.
4. E-Commerce vs. Traditional Retail: E-commerce platforms often capture higher CLV
compared to traditional retail.
5. Retention and Frequency: Increasing average transaction values and purchase
frequencies substantially impacts CLV.
6.2 Narrative
The retail industry is transforming as e-commerce gains prominence. Analysis reveals that ecommerce platforms not only achieve higher customer lifetime values but also are influenced
by key factors such as average transaction values and discount strategies. The what-if analysis
underscores the potential for growth by optimizing transaction values and purchase
frequencies, highlighting the need for targeted strategies to enhance CLV.
7. Recommendations
7.1 For Traditional Retailers
1. Embrace Omnichannel Strategies: Integrate online and offline experiences to
enhance customer engagement.
2. Leverage Data Analytics: Utilize data-driven insights to personalize offers and
promotions.
3. Optimize Discounting Strategies: Balance discounting to boost sales while preserving
long-term customer value.
7.2 For E-Commerce Platforms
1. Invest in Personalization: Enhance user experience with personalized
recommendations and targeted marketing.
2. Focus on Customer Retention: Implement loyalty programs and regular engagement
initiatives.
3. Expand Product Range: Diversify product offerings to attract a broader audience.
7.3 For Policymakers
1. Support E-Commerce Development: Create policies to support digital infrastructure
and fair competition.
2. Address Economic Displacement: Provide support for traditional retailers adapting to
e-commerce shifts.
3. Monitor Consumer Data Privacy: Ensure regulations protect consumer data and build
online trust.