TATA Data Visualization : Empowering Business with Effective Insights
Introduction
Through my virtual experience internship at Forage, I completed a case study on TATA group data analysis and visualization. This valuable experience enabled me to utilize my data analytics skills and tools in a professional setting to address real-world problems.
Background
I have been hired by an online retail store as a consultant to review their data and provide insights that would be valuable to the CEO and CMO of the business. The business has been performing well and the management wants to analyse what the major contributing factors are to the revenue so they can strategically plan for next year. The leadership is interested in viewing the metrics from both an operations and marketing perspective. Management also intends to expand the business and is interested in seeking guidance into areas that are performing well so they can keep a clear focus on what’s working. They would also like to view different metrics based on the demographic information that is available in the data.
The Management would like to know what the drivers are for their business and then would like to use the insights from this as guidance to expand the business more. This was the primary request of the CEO and CMO, so anything outside this is just a deviation from the primary objective.
Data Preparation
About the Dataset
The dataset 'Online Retail' was provided by TATA.
The dataset has 9 columns - (InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country, Revenue).
The InvoiceNo column has the invoice number of each purchase made by customers.
StockCode has the product's stock code.
The description has the product description and information.
InvoiceDate has dates from when the purchase happened.
UnitPrice has the price of the product.
CustomerID tells us who purchased the product in numerical form.
The country is where the customer resides.
I multiplied UnitPrice and Quantity to make the column Revenue show insights for the client.
Data Preparation
Before the analysis, I made sure the data is clean and without errors using Excel to clean the dataset with the following steps:
Removed Rows in the Quantity column showing negative numbers or below 1.
Removed Rows in the UnitPrice column that are below $0.
Removed Rows with blanks.
Added Revenue Column by multiplying UnitPrice with Quantity.
Data Visualization and Insight
The Visualisation of the data was used to answer the following questions and help the CEO and CMO improve the business
Time series of revenue generated in 2011
Top 10 countries generating the highest revenue excluding the United Kingdom
Customers that generate the highest revenue for the company
Countries with the highest demand for their products
Revenue Trend
The line chart was used to visualize the revenue by month for the year 2011. There is an increase in revenue from August through November. The highest revenue generated was in November and the business might need to check why this is the case. There is incomplete data in December and this might explain the decline in revenue experienced in December.
Countries by Revenue and Quantity
The clustered column chart was used to compare the quantity sold and the revenue per country. The chart depicts that the quantity sold is correlated with the revenue and the countries that sold more generated more revenue. The UK was excluded from this chart and the business need to do more to increase unit sales in low-earning countries.
Customers Generating the Highest Revenue
The aim of this visual was to get the top 10 customers that generated the highest revenue for the retail store. The CMO should target customers who generate higher revenue and ensure that they remain satisfied with the products and services.
Product Demand Across Countries
Excluding the UK, the map highlights substantial revenue from countries such as the Netherlands and Australia. To generate more revenue, the company should invest in these promising locations and aim to achieve revenue growth similar to that of the UK. Additionally, expanding efforts in other continents beyond Europe will further enhance progress.
Tableau Dashboard Capturing all Insights
Final Recommendation
The analysis conducted offers valuable insights for the CEO and CMO to make informed decisions about the business and plan future strategic expansions for the upcoming year.
Although the December chart is incomplete, the trends from previous months indicate promising potential.
Opportunities for further expansion can be explored in countries like the Netherlands, Ireland, and other top countries with high unit sales and revenue.
To enhance sales in countries such as Belgium, Norway, and Portugal, which have lower unit sales and revenue, the CMO should consider investing in marketing initiatives like paid ads to increase awareness and drive sales.
