A Simple Data Analysis Case Study : Superstore Dataset
Learning data analysis as my new skill, I still have a long way to go, but right now I am trying to do a simple data analysis case study as an exercise.
I have taken a dataset from Kaggle (which is originally from the Tableau free dataset) and built the data visualization using Tableau. Here’s the result.
Goals
Superstore Giant is seeking knowledge in understanding what works best for them. They would like to understand which products, regions, categories, and customer segments they should target or avoid. In this case, I will focus on products and categories only.
Analysis
The dataset contains multiple columns, such as Sales, Profit, Discount, and Quantity. It also has several dimensions like Categories, Sub-Category, Product Name, Order Date, etc.
To get some insights about the performance of the store for 2014–2017, let’s see the amount of revenue and profit through the years. The top 20 products with the highest sales are also shown.
It also needed to look into the distribution of revenue and profit for three product categories which will help the store manager to decide what product category should get more attention to increase the store’s profit.
The furniture’s profit seems too small compared to other categories, even though the sales distribution among product categories is balanced.
When we look into the detailed profit dan sales for all categories, the furniture’s profit has fluctuated while others are increasing significantly. Furthermore, the amount of profit is way too low. We have to find out the sub-category which is responsible for this phenomenon.
Sub-category “Tables” showed a significant fall-off of profit in 2017. Superstore has to take action to prevent more loss in the future.
Result
Here are some solutions I suggest :
1. Managing price of the product, especially which gave the lowest profit, e.q Tables, Bookcases, and Supplies sub-categories.
2. If it is impossible to increase the price, the store should select only products with high profit on average. Removing several (for example, 10) lowest profit products can also be an option.
3. Decreasing the discount
