Case study · 04
Australian apparel — sales intelligence
Overview
Analytics case study on regional apparel performance: exploratory analysis, KPI framing, and dashboards that highlight revenue drivers and underperforming segments.
Project preview
PreviewReserved for a hero capture, product walkthrough, or marketing frame.
Technologies used
- Python
- Pandas
- Tableau
- Matplotlib
Key features
- Exploratory analysis with Pandas to profile regions, categories, and seasonality
- Tableau dashboards for executives and operators with consistent metric definitions
- Matplotlib and Seaborn visuals for publication-ready narrative charts
- Clear documentation of assumptions, joins, and data quality checks
Challenges solved
- Reconciling sparse regional data with headline revenue trends without misleading cuts
- Keeping visual language consistent across exploratory and executive-facing views
- Surfacing actionable next steps instead of static historical reporting alone
Architecture & engineering highlights
Diagram
Notebook-first EDA feeding curated datasets into Tableau extracts; chart semantics stay aligned to a single KPI dictionary shared with stakeholders.
Engineering highlights
Metric discipline
One source of truth for revenue and margin definitions before any regional comparison ships to leadership.
Narrative visuals
Matplotlib/Seaborn for analyst review, Tableau for recurring operational monitoring with the same cuts.
Screenshots
- Frame 01Wire `screenshots` in caseStudies.ts when captures are ready.
- Frame 02Wire `screenshots` in caseStudies.ts when captures are ready.
- Frame 03Wire `screenshots` in caseStudies.ts when captures are ready.
Future improvements
- Scheduled data quality checks before extract refresh
- Scenario models for discount and seasonality stress tests
- Lightweight API layer if live dashboards need embedding outside Tableau