📊 Ecommerce Sales Analysis

🔍 Analyzing Sales and Profit for Strategic Insights

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📖 Introduction

This project focuses on analyzing ecommerce sales and profit data to derive actionable insights. Using Python, key trends, patterns, and relationships were identified, enabling businesses to enhance decision-making and strategic planning.

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❓ Questions Answered

1. 📅 Monthly Sales Analysis: Which month had the highest and lowest sales?
2. 📦 Sales by Category: Which product category had the highest and lowest sales?
3. 🔎 Sales by Sub-Category: Performance analysis at the sub-category level.
4. 📈 Monthly Profit Trends: Which month had the highest profit?
5. 💰 Profit by Category and Sub-Category: Identifying profitable categories and sub-categories.
6. 👥 Sales and Profit by Customer Segment: Understanding segment-level performance.
7. 🧮 Sales-to-Profit Ratio: Measuring profitability efficiency.

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🛠️ Tools and Libraries Used

• Python Libraries:
  • pandas for data manipulation.
  • plotly.express and plotly.graph_objects for interactive visualizations.
• Dataset: Sample - Superstore.csv, loaded and analyzed with pandas.
• Key Features:
  • Conversion of date columns to datetime.
  • Additional columns created for better temporal analysis (e.g., Order month, Order year).

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❓ Questions Answered with Code

📅 1. Monthly Sales Analysis

Which month had the highest and lowest sales?

import pandas as pd 
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio  
import plotly.colors as colors

# Set the default template
pio.templates.default = 'plotly_white'

# Load the dataset
data = pd.read_csv('Sample - Superstore.csv', encoding='latin-1')

# Analysis
sales_by_month = data.groupby('Order month')['Sales'].sum().reset_index()
fig = px.line(sales_by_month,
              x='Order month',
              y='Sales',
              title='Monthly sales analysis')
fig.show()

📦 2. Sales by Category

Which product category had the highest and lowest sales?

sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
fig = px.pie(
    sales_by_category,
    values='Sales',
    names='Category',
    hole=0.5,
    color_discrete_sequence=px.colors.qualitative.Pastel1
)

fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Sales analysis by category', title_font=dict(size=24))
fig.show()

🔎 3. Sales by Sub-Category

Performance analysis at the sub-category level.

sales_by_Subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
fig = px.bar(sales_by_Subcategory, x='Sub-Category', y='Sales', title='Sales analysis by sub category')
fig.show()

📈 4. Monthly Profit Trends

Which month had the highest profit?

profit_by_month = data.groupby('Order month')['Profit'].sum().reset_index()
fig = px.bar(profit_by_month, x='Order month', y='Profit', title='Analysis profit by month')
fig.show()

💰 5. Profit by Category and Sub-Category

Identifying profitable categories and sub-categories.

# By Category
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
fig = px.pie(
    profit_by_category,
    values='Profit',
    names='Category',
    hole=0.5,
    color_discrete_sequence=px.colors.qualitative.Pastel1
)

fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Profit analysis by category', title_font=dict(size=24))
fig.show()

# By Sub-Category
profit_by_Subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
fig = px.bar(profit_by_Subcategory, x='Sub-Category', y='Profit', title='Profit analysis by Sub-Category')
fig.show()

👥 6. Sales and Profit by Customer Segment

Understanding segment-level performance.

sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
color_palette = colors.qualitative.Pastel

fig = go.Figure()

fig.add_trace(go.Bar(
    x=sales_profit_by_segment['Segment'],
    y=sales_profit_by_segment['Sales'],
    name='Sales',
    marker_color=color_palette[0]
))

fig.add_trace(go.Bar(
    x=sales_profit_by_segment['Segment'],
    y=sales_profit_by_segment['Profit'],
    name='Profit',
    marker_color=color_palette[1]
))

fig.update_layout(
    title='Sales and Profit Analysis by Customer Segment',
    xaxis_title='Customer Segment',
    yaxis_title='Amount',
    barmode='group'
)
fig.show()

🧮 7. Sales-to-Profit Ratio

Measuring profitability efficiency.

sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
sales_profit_by_segment['Sales_to_profit_ratio'] = sales_profit_by_segment['Sales'] / sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment', 'Sales_to_profit_ratio']])

🔑 Key Insights

Monthly Trends:

• Highest sales occurred in November; lowest in February.
• Monthly profits peaked in December.

Category and Sub-Category Analysis:

• Technology led in both sales and profit, while Office Supplies followed.
• Sub-categories like "Tables" had negative profit, highlighting areas for cost optimization.

Segment Analysis:

• The Consumer segment showed the highest sales and profit.

Profitability:

• Sales-to-Profit ratio revealed efficient and inefficient segments.

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📊 Visualizations

• Line chart: Monthly sales trends.
• Pie charts: Sales and profit by category.
• Bar charts: Sub-category performance, monthly profits, and segment analysis.

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📝 Conclusion

This project demonstrates the power of Python for data analysis and visualization, enabling businesses to uncover insights and make data-driven decisions. Each question was systematically addressed, showcasing proficiency in Python and storytelling through data.