__STYLES__
We'll start with the requirements, the goal of the dashboard is to enable users which are the managers to toggle between their names and what quarter it is.
Backend Data:
The dataset is originally composed of 4 tables,
Accounts - Dimension Table
Products - Dimension Table
sales_teams - Dimension Table
sales_pipeline - Fact table
Data Model:
The Underlying data model is a star schema:
Measures Used: A total of 22 Explicit measures are used, the most important of which are [Deals Won], [Closed Value],[Win Rate], and [Duration].
1) Deals Won - Number of Wins, the amount of opportunity IDs with deal state equal to Won (Higher is better)
2) Closed Value($) - The sum of closed value (Higher is better)
3) Win Rate (%)-The number of Deals won over (Higher is better)
4) Duration(Days) - Number of days it takes to close a deal , it is the difference between Closed Date and Engagement date
Report Buidling:
The Report is composed of two tabs, the Executive Summary Tab and the Locations Tab.
Composed of 4 Cards, 2 Bar Charts, 1 Column Chart, 1 Donut Chart, and 1 Matrix, containing all the performance metrics of all the sales agents. The figures can be changed by filtering by Manager and by Quarter.
2) The Locations Tab: Which contains all locations per parameter (ClosedValue/Revenue, Wins, WinRate, and Duration), this page contains a shape map of the world that contains countries involved in the Dataset, the second one is a column chart containing those countries , in which the y-axis is based on a field parameter that is set by a slicer, and finally, a heatmap showing the products vs the sales agent, so that the managers can easily identify the most sold product and the top performing agent.
With this dashboard, managers can easily identify which Sales Agent is performing well in terms of Closed Value(Highes values of sales), Deals Won/Wins (Highest number of sales), Win Rate (Most likely to close a deal), and Duration(Fastest to close a deal).