Sales Analysis With Microsoft Power BI

This report is a summary of the sales data analysis I performed with Microsoft Power BI with focus on identified business problems and insights gotten from the data.

The dataset used in this analysis was downloaded from Enterprise DNA when I took their ultimate beginner’s guide to DAX (highly recommended). The dataset was pre-cleaned so did not perform much data cleaning. I did the basic checking of the columns and its data type to ensure they are correct and checked for null values as well. I then proceeded to view the data model and inspected the automatic relationships created by Power BI and I was okay with it.

I then created a date table using CALENDERAUTO() (DAX formular). This creates a date column from the start date in the dataset to the end date in the dataset. I then created Year, Month, Quarter, Month number columns accordingly using DAX and ensured I sorted the “month column” using “month number”. Then I marked it as my preferred date table. Went back to data model and linked it up with the fact table. At this point, I was ready for visualization.

Business Questions

For this analysis, the following questions were asked:

  1. Who are the top 5 customers?
  2. What are the top 5 products ?
  3. What are the top 5 counties?
  4. What is the trend in cumulative sales compared to previous year?
  5. What kind of relationship exist between total cost and total sales?
  6. What is the trend of total sales compared to sales last year?
  7. What is the trend of total profits compared to total profits last year?

To answer these business questions,I created some measures using DAX to aid my analysis and also included some card visuals for a general overview of the sales, profits, transactions and customers. The dashboard was further made interractive by adding year and quarter slicers for further insights. The full dashboard is shown below.

Results

The following insights were deduced from the static dashboard shown above:

  1. The top five customers are Martin Berry, William Andrews, Craig Wright, Wayne Johnson and Christopher Write.
  2. The best selling products are product 63, 28, 47, 59 and 29
  3. The top counties by sales are Broward, Suffork, New Haven, Middlesex, and Fairfield
  4. There is a steady increase in cumulative sales as against the previous year
  5. There is a positive linear relationship between total costs and total sales. That is, an increase in total cost leads to an increase in total sales.
  6. The six months sales data available so far for current year shows that they are above average whereas for previous year, the monthly sales were below average except in September.
  7. The monthly profits made so far for the current year were all above average except for April whereas for last year, the profits were below average except in July, September and December.

Limitation

The current year data stopped at June so it was not enough to make full comparison with previous year.

PS: There are more insights to this dashboard especially when the year and quarter slicers are used. The interractive dashboard can be accessed here and the dataset used can be accessed here as well.

Let’s connect on LinkedIn.

Cheers!!!

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Chiamaka Uwaezuoke

Chiamaka Uwaezuoke

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Data Analyst. Here to write about my personal journey in all areas with the hope that it will inspire someone.