ANALYZING DATA OF GODREJ MARKET
SUBMITTED BY :- AYUSHMAN VATS
236/PMB/014
MBA 3RD
SEMESTER
2023-2025
INTRODUCTION
Brief Introduction: “Hello, everyone. I’m Ayushman Vats,
MBA 3RD semester student from Gautam Buddha University . I
interned as a Data Analyst at Godrej from June 2024 to August
2024. In this presentation, I’ll share my experiences, the
projects I contributed to, and key learnings from this role.”
ABOUT
GODREJ
Company Overview: “Godrej is a diversified conglomerate with
business operations in sectors like consumer goods, real estate, and
industrial engineering. With a focus on sustainability and innovation,
Godrej is committed to improving lives through cutting-edge
solutions.”
LITERATURE REVIEW ON DATA
ANALYSIS
• 1. Data Analysis Concepts and Importance
Data analysis is essential for transforming raw data into actionable insights, and its
importance has grown with the rise of data-driven decision-making in organizations.
In "Data Science for Business," Provost and Fawcett (2013) emphasize the role of
data analysis in extracting value from large datasets, noting that the ability to
analyze data is key for businesses to remain competitive in the modern economy.
• 2. Statistical and Exploratory Methods
John Tukey's seminal work, Exploratory Data Analysis (1977), revolutionized the way
analysts approach data. Tukey proposed the importance of understanding data
through visualization and descriptive statistics before applying more complex
models. He argued that EDA helps uncover underlying patterns, spot anomalies, and
guide further analysis, thus minimizing bias before conducting inferential statistics.
• 3. Machine Learning and Predictive Analytics
In recent literature, Hastie, Tibshirani, and Friedman (2009), in The Elements of
Statistical Learning, discuss how modern data analysis increasingly integrates
machine learning techniques. They emphasize predictive analytics—using
historical data to predict future outcomes—through models like regression,
decision trees, and support vector machines. These methods rely heavily on
training data and model validation, ensuring that insights are reliable and
generalizable.
• 4. Data Analysis in Big Data
As the volume of data has increased, techniques for handling and analyzing
large-scale datasets have evolved. In Big Data: A Revolution That Will Transform
How We Live, Work, and Think, Mayer-Schönberger and Cukier (2013) highlight
the shift from traditional sampling methods to the use of entire datasets for
analysis. They argue that this shift allows for a more comprehensive
understanding of phenomena, leading to more accurate predictions and insights.
ROLE & RESPONSIBILITIES
Internship Role: “As a Data Analyst intern, I supported
multiple teams by providing actionable insights through data
analysis. My main responsibilities included:
• Extracting and cleaning raw data from internal databases
• Analyzing large datasets to identify trends
• Creating visual dashboards for various departments to track KPIs
• Conducting statistical analyses to support decision-making."
KEY PROJECTS
Project 1: Sales Data Analysis
Objective: To analyze monthly sales data from 5 regions (North, South, East,
West, Central) and provide actionable insights to optimize distribution.
Tools Used: SQL, Power BI, Excel
Key Analysis and Findings:
• Sales Data: 500,000 records across 12 months
• After cleaning the data, I performed a trend analysis to compare monthly sales
performance.
• Using Power BI, I created an interactive dashboard, which helped the sales team quickly
identify that the Southern region experienced a 15% drop in Q2 2024 due to stockouts.
Outcome: Recommendations led to a more aggressive stock replenishment
strategy, resulting in a 7% increase in sales in the following quarter.
PROJECT 2: CUSTOMER SENTIMENT
ANALYSIS ON PRODUCT REVIEWS
• Objective: To analyze customer feedback from online platforms (Amazon, Flipkart) and
categorize sentiment (positive, neutral, negative) to improve customer experience.
• Tools Used: Python (pandas, NLTK), Power BI
• Data Overview:
• 50,000 customer reviews spanning over 10 product categories.
• Key Analysis and Findings:
• Sentiment analysis showed that 30% of the negative reviews mentioned delayed delivery as the
primary issue.
• Analyzed review trends to identify common keywords associated with product defects and quality
issues.
• Outcome: Insights were used to improve logistics and product design, leading to a 20%
reduction in negative reviews over the next two months
PROJECT 3: INVENTORY
FORECASTING
• Objective: To develop a forecasting model to predict monthly inventory
needs for high-demand products in the household appliances segment.
• Tools Used: Excel (forecasting), Python (ARIMA model), Tableau
• Data Overview:
• Historical sales data from 2020–2024 for 20 popular SKUs.
• Key Findings:
• Developed a time series forecasting model using the ARIMA algorithm, which
predicted a 12% increase in demand for refrigerators during summer months.
• Outcome: The forecast helped the procurement team optimize stock
levels, reducing excess inventory costs by 8%.
TECHNICAL SKILLS & TOOLS
USED
Tools & Technologies:
• Python: Data analysis, sentiment analysis, forecasting (pandas, NLTK, ARIMA)
• SQL: Database querying for large-scale data extraction
• Power BI & Tableau: Dashboard creation and data visualization
• Excel: Statistical analysis and forecasting
Visual Examples:
• Include a screenshot of your Power BI sales dashboard
• Include a visual of sentiment analysis results (e.g., a pie chart of
sentiment distribution)
CHALLENGES & LEARNING
OUTCOMES
Challenges Faced:
• Dealing with inconsistent data formats across different databases.
• Implementing cleaning strategies for missing or erroneous data.
Learning Outcomes:
• Developed a keen attention to detail through data cleaning techniques
(handling missing data, outliers).
• Gained experience in communicating complex insights to non-technical
stakeholders.
Example: “For the sales data project, 20% of the records had
missing product IDs, which required me to use SQL joins to cross-
reference missing information from different tables.”
IMPACT & CONTRIBUTIONS
Quantified Contributions:
• Improved regional sales strategy resulting in a 7% increase in sales.
• Enhanced inventory planning, reducing excess inventory costs by 8%.
• Helped improve customer sentiment, with a 20% reduction in negative
reviews over two months.
Business Impact:
• The insights derived from the dashboards and analyses were shared with
senior management to inform strategic decisions, contributing to an overall
improvement in business performance.
KEY TAKEAWAYS
Personal Growth:
• Strengthened my data analysis skills, particularly in using Python for advanced
analytics and SQL for querying large datasets.
• Improved my communication skills by presenting findings to both technical and
non-technical stakeholders.
Gained hands-on experience in creating actionable business
insights from data, reinforcing my passion for data-driven decision-
making.
CONCLUSION & THANK YOU
Closing Remarks: “Thank you for giving me the
opportunity to share my experience as a Data Analyst intern
at Godrej. I’ve gained valuable skills that will undoubtedly
serve me well in my future career. I’m happy to take any
questions.”
