Introduction
Sample SuperStore is a fictional dataset made for practicing Data Analysis
Problem Statement
Our goal is to Perform Exploratory Data Analysis on the Sample SuperStore dataset and we have to find out the weak areas where we can work to make more profit.
And also any other business problems we can derive by exploring the data?.
Solution
I have loaded the dataset and cleaned (preprocess) it for consistency then moved to the Exploratory Data Analysis (EDA) Part.
In EDA, I have compare the sales with the highest reason to the sales of the lowest region to find out the reason of low sales in the weaker areas.
Challenges
The data doesn’t contain any dates data so we cant check for how much sales is increase for year or month or weeks.
Conclusion
After fully exploring the data, I have suggested below recommendations:
Insights
- The Central Region is showing lowest sales & profit ( lower than the avg sales of the country)
- The profit is less in furniture and office supplies
- The sales & profit of Tables is low in the Central region. Also Bookcases & Furnishings going in loss in Central region
- The avg. profit of Home Office & Consumer is low in Central Region
- Central Region is giving less discounts in comparison with South Region (which is the highest sales region)
Other Insights
- The ship mode in same day of South Region is making loss.
Recommendations
- Superstore needs to increase their discounts to boost up the market in the Central Region
- Marketing teams should consider Furniture and Office Supplies Category while thinking about a new Sales campaign
- Also Superstore can start advertising about their Bookcases, Furnishing, Tables products to the customer from Home Office & Consumer lets say the quality and reliability and give special offer for bulk purchase or Home office kit.
Next Steps
SuperStore should try to give loyalty points/cards to regular users and can target the segments of specific age group to see how much sales are improving by which age group.