The focus of this initiative is a thorough examination of Walmart's sales data, with an emphasis on identifying the strongest branches and product lines, discerning product sales patterns, and examining customer behaviors. The goal is to extract meaningful insights that could enhance and refine sales strategies. The data for this analysis comes from the Kaggle competition for Walmart Sales Forecasting.(https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/description)
The primary objective of this analysis is to delve into Walmart's sales figures to decipher the elements that impact sales performance across its various outlets.
This segment of the analysis will dissect the data to gauge the performance of various product lines, highlighting both the top-performing and underperforming categories.
The intention here is to decipher the sales patterns of products, which will assist in assessing and refining the effectiveness of deployed sales strategies and necessary adjustments for enhanced turnover.
The customer analysis component seeks to segment customers, track purchasing patterns, and evaluate the profitability of each segment.
Initially, the project undertakes data wrangling to ensure the integrity of the dataset, focusing on the identification and rectification of any missing or NULL values.
Database Construction: A database is assembled with stringent criteria to prevent NULL entries, thereby maintaining data quality. Feature Expansion: New data columns are introduced to shed light on sales dynamics across different times of day (morning, afternoon, evening), days of the week (Monday to Friday), and months of the year, facilitating a granular time-based analysis.
The exploratory phase utilizes the data to respond to the proposed questions and achieve the project's analytical objectives.
the questions were taken from another repository and were used to fulfill the analysis of this dataset(https://github.com/Princekrampah/WalmartSalesAnalysis?tab=readme-ov-file):
- How many unique cities does the data have?
- In which city is each branch?
- How many unique product lines does the data have?
- What is the most common payment method?
- What is the most selling product line?
- What is the total revenue by month?
- What month had the largest COGS?
- What product line had the largest revenue?
- What is the city with the largest revenue?
- What product line had the largest VAT?
- Fetch each product line and add a column to those product line showing "Good", "Bad". Good if its greater than average sales
- Which branch sold more products than average product sold?
- What is the most common product line by gender?
- What is the average rating of each product line?
- Number of sales made in each time of the day per weekday
- Which of the customer types brings the most revenue?
- Which city has the largest tax percent/ VAT (Value Added Tax)?
- Which customer type pays the most in VAT?
- How many unique customer types does the data have?
- How many unique payment methods does the data have?#
- What is the most common customer type?
- Which customer type buys the most?
- What is the gender of most of the customers?
- What is the gender distribution per branch?
- Which time of the day do customers give most ratings?
- Which time of the day do customers give most ratings per branch?
- Which day fo the week has the best avg ratings?
- Which day of the week has the best average ratings per branch?
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