PROJECT DATA ANALYSIS FOR RETAIL : SALES PERFORMANCE REPORT
Haiii my name is Apnesia Feronika. Now I am learning from DQLab course. This is my first project from DQLab. This project can support my current career, namely as Sales Analyst.
SQL(Structured Query Language) is a standardized programming language that is used to manage relational databases and perform various operations on the data in them.
Retail is the sale of goods and services to customers in contrast to wholesaling, which is sales to business or institutional customers.
Data is used: data transaction from dqlab_sales_store from 2009 until 2012. The amount of raw is 5500.
This is column of my dataset:
- Overall performance from 2009 until 2012 by number of order and total sales with status “Order finished”
From the result we know that overall performance and number of order are not growing significant.
2. Overall performance by sub category from 2009 until 2012.
From the result we know some of category product have a good performance like chair and chairmats, office machine, and tables.
3. Effectivitas and Efficient form our promotion every year.
We can use burn rate analysis. Burn rate analysis to know whether campaign that we have done so far is effective and efficient. The point of burn rate ≤4,5%. We can use derived table to know burn rate percentage.
Derived table is an expression that generates a table within the scope of query FROM clause.
From the overall performance our hypothesis is that the campaign that we have done so far is not really effective .We can see there is something wrong. our burn rate is growing each year. However it doesn’t reflect into our sales. It means that we spend so much money but if fall to boost our sales.
4. Effectivitas and Efficient form our promotion every year by sub-category
From these data we can see the relation between money that we spent and total sales that we can generate. Some with the highest burn rate are actually have a low sales.
Because the burn rate does not match with the sales then something wrong. Now, let’s analyze it from the customer segmentation.
5. Total of customer every month
As wee can see, number of customer for each year is more or less the same
6. Analysis New Customer
From the result we get that total of new customer that we get every year is decreasing significantly. It is declining over 70% every year. From this we know our campaign is not efficient.
Customer retention can be analyzed using cohort analysis. The purpose is to know whether we can get our customer come back and have transaction again after their first transaction
From this data we can get that some of customer who do first transaction but in other month they don’t do transaction again. So tha total of customer is decreasing.
So the insight that we can provide to management is:
- We want to increase our sales but also keeping our burn rate low. Differentiate customer based on their sales.
- We group the items that are promoted each month differently according to the needs of each month.
This is my project. Enjoy reading and studying. I’m sorry for my mistake. Thank You