What needed to be understood.
The assignment required ten separate business questions and a consolidated dashboard, creating a need for consistent calculations, annotations, filtering, and visual hierarchy.
How the analysis was built.
- Built worksheets for revenue, products, customers, trends, seasonality, day-of-month performance, and product contribution.
- Applied country, year, customer, and product filters.
- Combined five required worksheets into a Tableau dashboard.
- Reviewed denominators and chart suitability before defining the public case-study conclusions.
What the evidence showed.
- Germany generated approximately €56.1M in revenue.
- Men’s Off Road Bike was the highest-revenue product.
- June recorded the highest sales quantity and led each individual year in the dataset.
- Day 30 produced the highest aggregated revenue, although the pattern requires date-level investigation.
What should happen next.
- Prioritize precise ranked charts over decorative tag clouds for product comparisons.
- Investigate month-end transactions before treating Day 30 as a recurring behavioral effect.
- Use KPI cards and a clearer visual hierarchy in the executive version.
What this project does—and does not—prove.
This was a guided academic dashboard. The 28.24% product contribution refers specifically to bike-only German revenue after accessories were excluded.