Presented and Analyze by- Saddam Ansari @Aspiring Data Analyst Linkeldin
LiveProject at Novypro NovyproLink
- Project Objective
- Project Overview
- Project Requirements
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- Recommendation
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During my virtual internship at Quantium, I undertook a key task that involved conducting a comprehensive analysis of the chip category. The primary objective was to provide strategic recommendations for the impending category review, led by Julia, the Category Manager. This task was instrumental in honing my analytical skills and applying them to real-world business scenarios.
During my virtual internship at Forage with Quantium, I undertook a pivotal project involving retail strategy and analytics. This marked my inaugural project, dealing with a sizable dataset of approximately 2.5 lakhs rows. The primary tools utilized for data analysis were Power BI and Excel, with a focus on meticulous data cleaning and insightful visualizations.
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Data Cleaning: Meticulously examined transaction data for inconsistencies, missing values, and outliers using Power BI. Implemented data cleaning processes to ensure the dataset's integrity.
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Data Integration: Employed Power BI to scrutinize customer data, addressing issues and ensuring seamless data integration. Merged transaction and customer data effectively for a consolidated dataset.
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Feature Engineering: Utilized Power BI to derive additional features, such as pack size and brand name, to enhance the dataset's richness.
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Metric Definition: Defined relevant metrics using Power BI, including criteria for customer segmentation and drivers of sales. Explored metrics such as purchase frequency, average spend per transaction, and pack size preferences.
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Commercial Application: Ensured that insights derived from the analysis had practical applications for strategic decision-making. Formulated a clear and actionable strategy, leveraging Power BI's capabilities, to support Julia in the category review.
The task assigned by Quantium focused on retail strategy and analytics. To address this, I crafted a comprehensive report using Power BI, consisting of distinct pages to delve into various aspects of the data.
The inaugural page served as the overview dashboard, providing a snapshot of the key metrics shaping the retail landscape.
- Total Sales: $1.93 million
- Total Products Sold: 505,000
- CY Sales (Current Year): $956,670
- PY Sales (Previous Year): $976,440
- YoY Growth: -$19,770 or -2.02%
A visual representation in the form of a bar chart illustrated the sales trend by month, spotlighting December and March as the peak months with sales reaching $168,000 and $166,000, respectively. Conversely, February recorded the lowest sales at $151,000.
A comprehensive analysis revealed that the mainstream customer category significantly dominated, contributing to sales peaking at $1.68 million.
The exploration indicated that the older singles/couples lifestage exhibited the highest sales, amounting to $0.40 million.
A closer examination of categories unveiled that Chips and Kettle categories held the spotlight with the highest sales.
This initial overview page not only set the stage for subsequent analyses but also provided a macroscopic view of the retail dynamics, acting as a crucial foundation for the deeper dives into the data that followed.
This section of the project focuses on providing an in-depth analysis of product sales, incorporating various key performance indicators (KPIs) and charts for a comprehensive overview.
- Total Weighted Sales: 48.31 tons
- Total Products Sold: 505,000
- CY Sales (Current Year): 23.94 tons
- PY Sales (Previous Year): 24.38 tons
- YoY Sales in Weights Growth: -4.39 tons or -1.80% decrease
December showcased the highest sales in weights, amounting to 4.17 tons, while February recorded the lowest at 3.73 tons.
The analysis revealed that mainstream customer categories contributed the most, with sales totaling 41.69 tons.
Older singles/couples emerged as the predominant customer lifestage, accounting for the highest sales of 9.9 tons.
Chips products dominated the sales, amounting to a significant 12 tons.
Sunday asserted its prominence as the peak sales day, contributing to the overall highest sales.
Both Friday and Sunday exhibited the highest sales in weighted products, underscoring specific patterns in consumer behavior.
Chips again took the lead, recording the highest sales quantity of 128k units.
This meticulous analysis provides valuable insights into product sales trends, contributing to a comprehensive understanding of the retail landscape during the internship at Quantium.
This segment constitutes the heart of the entire report, providing an in-depth analysis focused exclusively on chips products.
- Total Sales: $465,000 for chips.
- Total Products Sold: 128,000 units of chips.
- Total Weights: 12.26 tons of chips sold.
- CY Chips Sales: $229,100, equivalent to 6.05 tons.
- PY Chips Sales: $236,000, equating to 6.21 tons.
- YoY Growth: Chips sales experienced a YoY decrease of -$6,930 or -3%.
December witnessed the highest chip sales at $41,000, accounting for 1.05 tons.
Maximum chip sales occurred on Sunday and Friday.
Top Performer: Older singles/couples contributed the most significant sales, reaching $97,000.
Insights: Older singles/couples dominated sales, while new families exhibited the highest growth, showcasing a steady year-by-year increase in chip purchases.
Observation: Mainstream lifestage customers displayed a higher inclination towards purchasing chips.
Detailed Breakdown: A comprehensive table presenting chip sales on a day-by-day basis, including sales by weights, growth percentages, etc.
Future Outlook: Utilizing a line chart, I forecasted sales trends for the upcoming 15 days, providing a clear visualization of expected sales patterns.
This thorough and meticulously crafted analysis not only showcases my adept use of Power BI and Excel but also highlights my ability to derive actionable insights from complex datasets. This project significantly contributed to my analytical skills and strategic thinking, setting the stage for future endeavors in the dynamic field of retail analytics.
This section of the project focused on providing a comprehensive overview of all products, incorporating essential key performance indicators (KPIs) and insightful analyses.
This page provides a comprehensive snapshot of the performance of all products, offering valuable insights into sales trends, customer categories, lifestages, and future projections.
- Diversification Drive: Explore opportunities to diversify chip offerings, tapping into emerging trends.
- Marketing Marvel: Craft targeted marketing campaigns, especially during peak sales months, to maximize impact.
- Demographic Delight: Tailor promotions to attract new families, considering their consistent year-over-year growth in chip purchases.
- Category Collaboration: Foster collaborations within the category to enhance the sales potential of complementary products.
- Tech Transformation: Consider leveraging emerging technologies for predictive analytics to enhance future sales forecasting accuracy.
Don't forget to give a start to this project because its motivate me and also please follow me on LinkedIn. and Please consider me for any internship or entry level data analyst role. I need a job or internship even thought its a free or paid. Thanks in Advance.
Created & Presented by -Saddam Ansari @ Aspiring Data Analyst
Date- 16/12/2023
Place- Bihar, India