Data-Driven Insights for Strategic Retail Decision Making: An Analytical Report on Multichannel Sales Performance
Instructions
You will work on a real-world dataset using powerful advanced data analysis techniques. The dataset comprises sales transactions of a retail store collected by the data science team of a multinational chain. The sales data pertains to their products from various stores situated in diverse cities and has been gathered systematically. In addition, you will use a web analytics platform to analyse sales data on a business website of your choice.
You will analyse the data from the provided dataset, and the website, and produce a report on your findings. The aim of this report is to support business decision making. The report should encompass an executive summary that prominently showcases the major findings resulting from the analysis. It should also feature a dataset description with fundamental descriptive statistics, relevant graphical representations to elucidate the data, and an insightful interpretation of both the data and the graphs. Furthermore, the report should acknowledge any unresolved questions or issues stemming from the analysis. In the conclusion, a concise summary of the major business findings and their implications on decision making should be provided.
Report Word Count - your report should aim to be around 3,000 - 4,000 words in length.
Dataset: You can download the dataset here:
Dataset.zip set.zip
This assessment has three parts. They are described below.
Parts
Part 1: Exploratory Data Analysis and Preprocessing
Part 2: Apply Analytical Techniques
Part 3: Utilize Web Analytics
Utilize web analytics platforms to analyse sales on a business website. You may choose any business website of your choice that has sales data.
Interpret data metrics to extract insights on crucial features impacting sales. Present the findings in your comprehensive report, showcasing your expertise in web analytics tools and providing actionable recommendations to enhance sales strategies.
Discuss your findings and recommendations in relation to your findings and analysis of the dataset from Parts 1 and 2. Consider relationships and trends across the sales data, drawing critical insights to support and inform business decision making.
