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anaplatform Data Consultancy
Sales Analysis

We develop analytical best practices for the retail industry.

Sales Analysis

Case Study - Sales Analysis

In this case study, we will analyze sales data and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc.

The dataset can be seen as below. Typicaly these datas are stored per csv files per month on a cloud drive. First we need to make this data logical, our first task will be to merge all 12 months’ worth of sales data into a single file


Data Merging

Now as the first task is to merge all 12 months’ worth of sales data into a single file. It will take a little time because of heavy computation. But once it’s done, we will see the data contains all 12 months data in our directory.

ID

Product

Quantity

Price

Order Date

Purchase Address

176558

USB Charging Cable

2

11.95

04/19/19 04:46

917 1st, Dallas, RX 75001

NaN

NaN

NaN

NaN

NaN

NaN

176559

SoundSport Headpohones

1

99.99

04/19/19 04:46

682 Chestnut St, Boston, MA 02215

176560

Google Phone

1

600

04/12/19 14:38

669 Spruce St, Loas Angeles, CA 90001

176560

Wired Headpohones

1

11.99

04/12/19 14:38

669 Spruce St, Loas Angeles, CA 90001

Questions to be answered

Throughout the task we will answer various questions, each will help us proper marketing.

Q1: What was the best month for Sales? How much was earned that month?

We can answer these questions it by we using some analytics method.

Month

Ordered

Price

Sales

January

22703

22703

29287

February

30701

30701

22703

March

41563

41563

22703

April

17005

17005

22703

May

20558

20558

22703

June

18667

18667

24361

July

41563

41563

22703

August

17005

17005

22703

September

20558

20558

22703

October

18667

18667

24361

We use panda to read the csv file and create a dataframe from it. Now the first task is to merge all 12 months’ worth of sales data (12 csv files) into a single csv file. It will take a little longer time because of heavy computation. But once it’s done. You will see a new csv file contains all 12 months data in our directory.


Q1: Which city has highest number of sales?

Now we see bar graph as below.


Q1: What time should we display advertisements to increase customer likelihood of buying product?

Now we can answer the third question, what time should we display advertisements to maximize likelihood of customer’s buying product? To answer this, we’re gonna group it by the hours and counting all of the orders.

Now we see bar graph as below.



Recommendations

Here are the Recommendations

  • Create a Home Office package with both high-selling categories like Phones and Chairs and less profitable products like Tables and Bookcases to offset losses.
  • Consider increasing the price of Tables as it’s a high-selling product. However, Bookcases and Supplies could be either dropped from the product list or negotiated with suppliers for a cheaper price.
  • Customer Success teams should target inactive customers to drive sales and marketing goals. Special discounts on their favorite products can be offered to retain the relationship and prevent churn..
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