__STYLES__
Conduct analysis on the data to understand the different product lines, the products lines performing best and the product lines that need to be improved.
This analysis aims to answer the question of the sales trends of products. The result of this can help us measure the effectiveness of each sales strategy the business applies and what modifications are needed to gain more sales.
This analysis aims to uncover the different customer segments, purchase trends, and the profitability of each customer segment.
Build a database create a table and insert the data. Select columns with null values in them. There are no null values in our database as in creating the tables, we set NOT NULL for each field, hence null values are filtered out.
Add a new column named
time_of_day
to give insight of sales in the Morning, Afternoon and Evening. This will help answer the question on which part of the day most sales are made.
Add a new column named
day_name
that contains the extracted days of the week on which the given transaction took place (Mon, Tue, Wed, Thur, Fri). This will help answer the question on which week of the day each branch is busiest.
Add a new column named
month_name
that contains the extracted months of the year on which the given transaction took place (Jan, Feb, Mar). Help determine which month of the year has the most sales and profit.
Cost of goods sold (COGS) includes all of the costs and expenses directly related to the production of goods. COGS excludes indirect costs such as overhead and sales and marketing.
January also had the highest COGS and February with the least.
What product line had the largest VAT?
Which branch sold more products than the average product sold?
What is the most common product line by gender?
What is the average rating of each product line?
What month had the highest Profit?
Which product line had the highest Profit?
Number of sales made at each time of the day per weekday
Which of the customer types brings the most revenue?
Which city has the largest tax percentage/ 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 the most ratings?
Which time of the day do customers give the most ratings per branch?
Which day of the week has the best avg ratings?
Which day of the week has the best average ratings per branch?