Christmas Sales & Trends

Definition

Actual Revenue This is the revenue generated after the discounts given out and refunded by customers.

Revenue after Discount This is the revenue that is generated after the discounts(black Friday and Christmas sales) It is calculated by subtracting the discounts from the total Price.

 Total Price – Discount

Revenue This is not in any of the dashboards but I would like to talk about it. It is calculated by adding the column of total price.

SUM(Total Price)

This could not be the revenue because there were discounts given out and some returns were done by some.

Refund This is the number of returns that were made by people.

$ Refund

This is the amount of refunds that were done by people in terms of money.

Transactions This is the number of transactions that were carried out during the period from 2018 to 2023.

Customers This is the number of customers that ordered from the store during the 6 Years of Business.

Buttons were used to easily navigate the pages and Bookmarks were used to create more space for the visuals that could not fit in the page.

Objective When I was getting started with the data, the Questions I asked myself were:

1. What is the revenue?
2. What is the total quantity?
3. The average customer satisfaction?
4. The number of customers and the total transactions?

As I continued to analyze the data, I noticed that something was not right with the revenue generated as it kept on reducing. Now the Objective was to find where the company was losing its money.

This dashboard I created was to highlight the area where the company lost most of its money. I used simple charts that could be easily understood by anyone but also showed the necessary results. Apart from the filters, the charts can also be used as filters.

I went step by step showing at every level that the company was losing money.

Observation As we can see the store had a total of 500 customers and 10K transactions meaning there were many returning customers. From the analysis, it is clear that the store is actually losing money since there are many discounts given out but despite the discounts, there is also a high number of returns. Before any discount or returns, the store generates $1,654,260 and after the discounts, the revenue generated is $ 1,584,714. The amount of Discounts given out in the 6 years was $ 69,545 The amount of Returns given out in the 6 years was $ $ 803,340 which is close to $ 1M. The Discounts and Returns add up to $ 872,885. The actual Revenue that is generated is $ 781,375. This is less than half the money that was generated before the discounts. In the data, there is no expenses column such as labor, lights, and others meaning that when those are subtracted from the Actual Revenue, the number will reduce. There is also no column for the initial cost of the goods which is to be subtracted from the revenue to get the actual Profit. Even if the store manufactured its goods there is also the cost of the machines and raw materials used. All these factored in means that the store has been generating a small Profit or even worse no Profit at all.

Also to Note In 2023, it was not the year that generated the most revenue (even though the year is not complete), It was the second-highest year in discounts given out and also the year with the most returns.

2020 was their best year, they generated the most revenue, had low total discounts, and had the least number of returns. This is very positive for the business since it was during the coronavirus pandemic where many businesses failed but for them, they thrived.

Recommendations

1. The company should look for ways to attract more customers. From the data, it doesn’t look like they have a problem with maintaining the customers.

2. The company should look back to what it did in 2020 that is different from the current year and implement only the positive things.

3. The company should have a suggestion part after the customer satisfaction to know on areas they can improve on.

Not Negative

The Age bracket 58-70 generated most of the revenue for the store, had the highest number of transactions, were the highest in returning the goods they purchased, and were also the most people who were given discounts.

On the other side, the age bracket 18-27 purchased the least from us and had the lowest returns, least discount, and the least number of transactions.