ACF5320 Business Analytics Individual A3 Brief 2025 - Monash University

ACF5320 OVERVIEW:

In this assignment, part of an accounting analytics unit, you are required to use Power BI Desktop to analyse and visualise key performance indicators (KPIs) and customer segmentation for a US retailer specialising in office supplies. This task is structured to assess your abilities in data visualisation, cluster analysis, and decision support within a business analytics context.

• Exploring Key Performance Indicators (KPIs): Initially, you will explore the provided "Retailer_data.xlsx" dataset, which includes data from three different sheets: Orders, Returns, and Employees. This preparatory step involves understanding the available data fields to identify and refine measures for analysing KPIs in four critical areas: financial performance, process performance, customer satisfaction, and employee performance.
• Customer Profiling using Cluster Analysis: In a separate dashboard tab titled 'Cluster Analysis', you will perform cluster analyses to profile customers based on their purchasing behaviour. This includes using scatter plots to display customers by average sales versus order count and average sales versus percent returned, segmenting them into distinct clusters. This analysis helps in understanding customer segments better and tailoring business strategies accordingly.
• Reporting: You will summarise the actual performance of the retailer with respect to the target performance levels for the four KPIs in a concise report. This will demonstrate your ability to communicate key insights and findings effectively.

Your submission should clearly demonstrate your proficiency in using business analytics tools to analyse data and present findings in a manner that supports business decision-making. This includes not only the technical execution of creating interactive visualisations but also the ability to interpret and articulate the significance of your analyses in a clear, concise manner.

ACF5320 OBJECTIVES:

• Understand and apply data visualisation techniques.
• Analyse relationships between various business performance metrics.
• Develop visual dashboards based on the data analysis.
• Interpret and evaluate the outputs from data visualisations and cluster analyses.
• Communicate analytical findings effectively.