In Maven Market project, I played a role a Business Analyst for Maven Market, a company that sells a wide varieties of grocery products in different continents.
The dataset contains transaction record in 1997 and 1998, returns record, customer, products, regions, and stores details. Each dataset holds information and associated details.
Due to the lack of real interactions with clients, I made below key assumptions to guide my dashboard design.
The target user group of the dashboard are executives who are interested in high level summary of the performance against business goal.
The business requires procurement to source products from different brands, thus the dashboard highlights the top brands to understand what are the most popular among customer.
The major KPI is the revenue made, alongside with other metrics such as profit margin, return rate, number of transactions, and monthly target.
It's is assumed the latest month is December 1998, however, in reality the dashboard would be updated regularly that reflects the current months.
Customer insights only reflects the timeline of the dataset. Given the demographic and time changes, customer composition and preference may have changed overtime.
The dashboard is designed to be interactive using tooltips, drillthrough functions that allows insights to details level. Although high level summary is the main focus for target audience, they may have interest to view more granular level of products to help them make informative decision when it comes to contracts with suppliers. Geographic chart is also included for regional sales information.
It is the main page of the dashboard where Current Month Transaction, Profit, and Return Rate is clearly displayed at the top. Revenue made against monthly target is displayed in a gauge chart at the bottom right.
The matrix lists Top 10 brands with most number of transactions. The data bar indicates visually the brands with top transactions, and background colour scales display their profit margin and return rate from light to darkest, allowing clear identification what brand(s) generate higher profit margin, and what brand(s) needs to be reviewed due to high return rate.
The map give spatial information of where transactions had taken place and their scale, represented by the bubble sizes. The treemap gives complementary details of sales in each region by implementing tooltips that includes KPI metrics and a line chart.
At the bottom of the main page, a column chart and a gauge chart are to show each region or brand's revenue trending, and whether it's revenue has reached its monthly target.
This is a drillthrough page that helps users dive deeper of the brand, and allows users to view KPI of each brand, as well as adjust price parameters to see its impact on revenue/profit.
This page breaks down customer composition in terms of gender, education, and country against revenue earned. I added membership column chart and a scatter chart that shows relationship between income and purchase product price tier, to bring more attention to opportunity where the business can improve in order to increase either customer loyalty or revenue more effectively.