1. SALES PERFORMANCE - SUPERSTORE
Interactive analysis of sales, product mix, shipping modes, and geography. Details of the context, data model and report behind.
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Context & Objectives
Dataset & scope: Link to Kaggle; scope is EDA + business analytics (no forecasting here).
Business questions:
Geography: where do we sell more/less and how does lead time affect it?
Product: which (sub)categories drive revenue and volume?
Shipping: how do ship modes differ in lead time and contribution to sales?
Time: how do sales and orders evolve by year/quarter/segment?
Process & Data Model
Ingestion & cleaning (Power Query): types, standardization, splits, U.S. focus.
Date table (DAX): marked as Date table; proper relationships; sort by month.
Key measures (DAX): Total Sales, Sales LY, Sales YTD, Order Count, Product Count, Avg Order Value, Avg Items per Order, Avg Delivery Lead Time, % of Total Sales, % Sales in Category, % Orders in State, Total Sales per Order (with ALLEXCEPT).
Classifications & helper tables:
Order Size Category from Total Sales per Order.
State Summary via SUMMARIZE + Sales Category to drive the map legend.
Report Structure (5 pages)
Overview: KPIs, category split, ship mode bar, yearly trend, U.S. map by Sales Category.
Product Category Analysis: monthly trends by category, treemap by sub-category, matrix with % of total & within-category, Avg Price per Item vs Product Count.
Ship Mode Analysis: Avg Delivery Lead Time by mode, % orders by state (heatmap), sales proportion by mode, detailed matrix by category × mode.
Sales Over Time: trends by category and segment, sales by ship mode and year, breakdown table with LY/YTD.
Geographical Analysis: filled/bubble map with Sales Category, sales by region & year, detailed table by state/city incl. lead time.