SALES PERFORMANCE ANALYSIS USING POWER BI

As a freelance data analyst, I translate data into valuable and comprehensive insights. my goals are to improve results, make the right decisions. I worked on Veeralakrishna predict-demand data and tracked sales trend over the years and provide insight that would be valuable to the company.

THE BUSINESS QUESTIONS

1. What was the trend in the company's sales over the
   years (2012 - 2018)?

2. Which of the brand has the highest and lowest sales?

3. Which city did the company make more sales?

4. What were the company's top brand?

5. Which of the shops sold more?

6. What is the total revenue, total revenue previous
   year, total revenue year difference, % total revenue
   year difference?

7. What is the total amount and average of brand
   quantity sold?

TASKS
During cause of analysis, I am going to perform the
following procedures using DAX and data modelling.

This outline includes the following requirements:

1. Cleaning the dataset using power query, this involves
   replacing null values, sorting data into the right
   format, removal of duplicates, merging necessary
   queries.

2. Using the DAX format method to separate Date table
   into month number, month and year.

3. Create a relationship between the sales table, date
   table, brand table and city table.

4. Using Dax, to create measures to answer questions,
   which include Total revenue, Total orders, Total
   quantity sold, Total revenue previous year, Total
   revenue previous year difference, % of Total revenue
   year difference.

5. Visual representation of the above business
   questions.

ANALYSIS

1. Date Table with Month and Year was separated and created into new columns as seen below.

2.The Created Model relationship below.

3.DAX functions were written to create measures for the analysis. An example is shown below.

1. New measures were also created to find total revenue previous year, total revenue previous year difference, %total revenue previous year difference.

Find below Dax calculations used in creating new
measures.

DASHBOARD
Below is the visual representation of Analysis.

INSIGHT AND RECOMENDATION

• MORE SHOPS TO BE OPEN IN ATHENS:

From the above Analysis, Athens generated more revenue over the years, with a Total Revenue of over 54 million dollars , followed by patra with over 50 million dollars generated as Total Revenue. Therefore it recomended that more shops be opened in Athens, which leads to increase in sales, it is also advisable that sales teams strategies on how to improve sales in other cities.

• SLIGHT INCREASE IN YEARLY SALES :

From the Analysis there is increase in sales across the years from (2012-2018), in 2012 the Total Revenue generated was 28 million dollars, while 2018 Total Revenue generated is 32 million dollars, the company has experience more profit than loss.

• TOTAL REVENUE YEAR DIFFERENCE:

In 2013 the Total revenue percentage year difference is 2.44%, in 2014 Total revenue difference is 2.08%, in 2015 1.98% difference, in 2016 1.64% difference, in 2017 2.39%, in 2018 2.00%, this clearly shows increase in revenue over the years.

• MORE PRODUCTION OF BEST-SELLING BRAND:

Adult-cola is seen as the best-selling brand with over 49 million dollars as Total Revenue, followed by orange-power with 48 million dollars in Total Revenue, These Brands contribute immensely to the company's Total Revenue, therefore they should never be out of stock, because of high demand. A survey should be carried out on why people prefer the high demand brand to the low demand brand.

• DISCOUNTS AND PROMO IN LOW SALES SHOPS:

From the above analysis shop 6 generated the highest revenue with over 50 million as Total Revenue, followed by shop 4 with a Total Revenue of over 40 million, the location of the shop was not stated , but it is safe to guess they are located in the same city, Therefore it is recommended that the sales team should come up with strategies towards low performing shops, enough resources should be channeled to low performing shops to ensure the smooth running of the shop. Discount and promo could be introduced to attract more sales.

Dataset: https://www.kaggle.com/datasets/veeralakrishna/predict-demand

Thanks for viewing, feedbacks are highly welcomed ([e-mail])