The Business of Diwali

The Business of Diwali: A Sales Analysis

Vedanta Bhattacharya

PGDM-International Business

Roll No:183052

Data Analysis using Excel & Python (DAEP)

Submitted for evaluation to

Prof. Prashant Kumar

Assistant Professor (Visiting)

FORE School of Management, New Delhi

September 2024
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Summary of project

Abstract:

This report presents an analytical study of Diwali sales data using Python to uncover key

consumer trends and patterns. Visualizations, made possible by matplot.lib, are used to highlight

significant findings, offering a clear, data-driven view of the Diwali sales landscape. This

analysis aids in understanding consumer behavior during the festival, providing valuable insights

for future marketing strategies and inventory planning.

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1. Introduction

“Diwali is the festival of lights, and it reminds us that even in the darkest times, hope and

kindness will shine through.” This report aims to analyse the Diwali sales data using Python,

focusing on consumer demographics and purchasing behaviour during the festive season. The

analysis covers various aspects, including Gender Count, Age Distribution, State-wise sales,

Occupation-based trends, and product category preferences. Through comprehensive data

visualization, we seek to understand the key trends that influence Diwali shopping.

Purpose/Objective:

• To examine gender and age distribution among customers.

• To analyse the geographic spread of Diwali sales.

• To study occupation-wise purchasing patterns.

• To investigate the popularity of product categories during Diwali.

Scope:

Using Python’s powerful libraries such as Pandas, Matplotlib, and Seaborn, we will process and

visualize the Diwali sales data. The focus will be on understanding customer behaviour by

categorizing sales based on gender, age, state, occupation, and product types. This analysis will

provide actionable insights for businesses looking to capitalize on Diwali season sales.
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2. About Dataset:

User_ID: Unique identifier for each customer.

Cust_name: Customer’s name.

Product_ID: Unique identifier for each product.

Gender: Gender of the customer (Male or Female).

Age Group: Age group of the customer (e.g., 26-35).

Age: Actual age of the customer.

Marital Status: Indicates whether the customer is married (1) or not (0).

State: The state where the customer resides.

Zone: The region of India where the state belongs (e.g., Western, Southern).

Occupation: Customer’s occupation (e.g., Healthcare, Automobile).

Product_Category: Category of the purchased product (e.g., Auto).

Orders: Number of orders placed by the customer.

Amount: Total amount spent by the customer.

Status: Possibly indicates the status of the transaction, but it has missing values.

Unnamed1: An additional column, likely unnecessary, possibly a placeholder or artifact from

data entry.
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Impact of the Labels:

User_ID, Cust_name: Help in identifying individual customers for tracking and personalization.

Product_ID, Product_Category: Essential for analyzing sales performance by product type.

Gender, Age Group, Age, Marital_Status: Useful for demographic segmentation and targeting

of marketing efforts.

State, Zone: Help analyze geographic trends in sales, allowing for region-specific marketing

strategies.

Occupation: Provides insights into customer spending patterns based on profession.

Orders, Amount: Critical for understanding sales volume and revenue generation.

Status: Could indicate transaction completion or order fulfillment, though it has missing data.

Each of these labels enables a detailed understanding of customer behavior and sales

performance, crucial for optimizing marketing and sales strategies during the Diwali season.
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3. Results with screenshots of python code:

Figure 1: Plotting a bar chart for Gender and its count

Figure 2: Plotting a bar chart for Age Group and its count
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Figure 3: Distribution of Number of orders (Top 10 States)

Figure 4: Marital Status of consumers

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Figure 5: Distribution of consumers based on occupation

Figure 6: Distribution of Production Categories and its count

Figure 7: Product-ID v/s Orders

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4. Discussion on the key findings

The analysis of Diwali sales data provided significant insights into consumer behaviour during

the festive season. Below are the main findings:

1. Gender-Based Trends: A bar chart displaying the count of customers based on gender

revealed that purchasing behaviour slightly skewed towards males. This suggests that marketing

strategies during Diwali might be more effective if tailored towards male customers, while not

neglecting female shoppers.

2. Age Distribution: The age distribution analysis showed a strong preference for Diwali

shopping among younger demographics, particularly in the 18–35 age group. This indicates that

businesses could benefit from targeting this age range through digital marketing, social media,

and product offerings that appeal to younger consumers.

3. State-Wise Sales: The geographic distribution of sales highlighted the top 10 states

contributing the most to Diwali purchases. This allows businesses to focus their logistical and

marketing efforts on these key regions, optimizing inventory placement and promotions.

4. Occupation-Based Purchases: An analysis of the occupations of consumers revealed

that salaried individuals formed a significant portion of the customer base. This insight suggests

the importance of targeting professionals, particularly around salary dates or offering relevant

schemes such as EMI options or festive bonuses.

5. Product Categories: The product category distribution indicated high sales of certain

categories, with electronics and home decor being particularly popular. These categories could

be prioritized in promotions and inventory stocking for future Diwali sales.

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6. Purchase Behaviour by Marital Status: Data on the marital status of consumers showed

that married individuals tended to spend more, likely driven by family-oriented shopping.

Businesses may capitalize on this by offering family bundles, discounts on home essentials, or

promotions tied to household needs.

These findings offer valuable insights for businesses looking to optimize their marketing,

inventory, and sales strategies for Diwali, aligning them with consumer preferences to drive

higher engagement and profitability.

Use of Seaborn:-

Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides

beautiful default styles and colour palettes to make statistical plots more attractive. It is built on

top matplotlib library and is also closely integrated with the data structures from pandas.

Seaborn aims to make visualization the central part of exploring and understanding data. It

provides dataset-oriented APIs so that we can switch between different visual representations for

the same variables for a better understanding of the dataset.

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Different categories of plot in Seaborn

Plots are basically used for visualizing the relationship between variables. Those variables can be

either completely numerical or a category like a group, class, or division. Seaborn divides the

plot into the below categories –

• Relational plots: This plot is used to understand the relation between two variables.

• Categorical plots: This plot deals with categorical variables and how they can be

visualized.

• Distribution plots: This plot is used for examining univariate and bivariate distributions

• Regression plots: The regression plots in Seaborn are primarily intended to add a visual

guide that helps to emphasize patterns in a dataset during exploratory data analyses.

• Matrix plots: A matrix plot is an array of scatterplots.

• Multi-plot grids: It is a useful approach to draw multiple instances of the same plot on

different subsets of the dataset.

Use of Matplotlib:-

Matplotlib is a powerful plotting library in Python used for creating static, animated, and

interactive visualizations. Matplotlib’s primary purpose is to provide users with the tools and

functionality to represent data graphically, making it easier to analyze and understand. It was

originally developed by John D. Hunter in 2003 and is now maintained by a large community of

developers.
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Key Features of Matplotlib:

Versatility: Supports various types of plots like line, scatter, bar, histograms, and pie charts.

Customization: Offers extensive control over plot details like line styles, colors, markers, labels,

and annotations.

NumPy Integration: Works seamlessly with NumPy, allowing easy plotting of data arrays.

Extensibility: Can be extended with toolkits like Seaborn, Pandas, and Basemap for specialized

plotting.

Cross-Platform: Runs on multiple operating systems like Windows, macOS, and Linux.

Interactive Plots: Supports interactive features like widgets and event handling for dynamic data

exploration.

5. Conclusion:

In conclusion, this report has highlighted the effectiveness of Python's data analysis and

visualization capabilities in uncovering key trends in Diwali sales. By leveraging libraries such

as Pandas, Matplotlib, and Seaborn, we were able to analyze consumer behavior across various

demographic factors including gender, age, occupation, and geographic location. The insights

drawn from this analysis provide actionable recommendations for businesses to tailor their

marketing strategies, optimize inventory, and enhance consumer engagement during the festive

season. This Python-driven approach serves as a valuable tool for data-driven decision-making,

reinforcing its importance in modern business analytics.

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References

Dataset(csv): G-Drive

Code: G-Drive