Business Acquisition Case Study with Power BI

Public link to the Dashborad: https://app.powerbi.com/view?r=eyJrIjoiMTE1ZTEwY2ItZTBkNS00ZDZkLThjM2YtYWU2MGI2OGE4OGMwIiwidCI6ImQ0Y2FkNDliLWRkNTYtNGUzYi04MGE4LWVlYzk0N2Y0MzFlNCJ9

Project Overview and KPIs


Two companies based in Australia are joining forces. For a smooth transition, the following parameters need to be checked to audit the business structure of both the companies:

  • How much did companies sell and how many products did they sell?
  • How much money did they make and what was the profit margin?
  • We need last year's detailed performance for comparison.
  • Compare this year's performance with last year's.
  • Find out the sales from two years ago for any chosen year.
  • Look at the average profit and sales trends.
  • Break down the analysis by quarters.
  • Compare cumulative sales this year versus last year using an area chart.
  • Identify the top-performing products and the top 7 customers.
  • Analyze sales performance by region, sales channel, city, and product names.
  • Tracking the average profit and sales trends over time.

Key Insights


  • The total profit for 2016 increased by just 0.57% compared to last year, whereas the total profit increased 71.83% compared to 2014.
  • Total Sales for 2016 rose by only 1.1% compared to last year and it was increased by 69.5% compared to 2014.
  • Majority of sales (53.67%) are from Wholesale, 31.68% from distributors, and 14.64% from Export.
  • Product Penter holds the maximum profit margin of 40.15%.
  • 2016 made up 38.91% of the total sales.
  • Coffs Harbour, Sydney, Albury, and Grafton contributes the most to sales across all 14 cities.
  • Product Brimmer accounted for 6.58% of the total quantity sold in 2015.
  • The moving average remained flat throughout 2015 and 2016.

Data Model View


GitHub Logo

Dashboard Overview


1. Sales and Profit Dashboard:

GitHub Logo

2. Past Years Sales and Profit:

GitHub Logo

3. Moving Average:

A moving average is a statistical calculation used to analyze data points by creating a series of averages of different subsets of the full dataset. It smooths out fluctuations to identify trends over time.

GitHub Logo


To analyse further using the interactive Dashboard Report, please use the public link to the Dashboard.