01

Problem & Objective

Business Problem

Business stakeholders lacked a clear, interactive view of sales performance across countries and product categories, making it difficult to identify revenue trends and growth drivers. Decisions were being made from static spreadsheets with no filtering capability.

Objective

Build an interactive sales dashboard that enables stakeholders to explore revenue by country and product category in real time — replacing static reporting with a self-serve analytics layer that supports faster, data-driven decision-making.

02

Tech Stack

Tools & Technologies
Tableau Public Python SQL Excel Data Cleaning KPI Design Dashboard Design Business Intelligence
03

Approach

01
Data Preparation & Cleaning Ingested raw sales data into Excel and SQL. Removed duplicates, normalised category names, and validated country codes. Structured the data into a clean dimensional model ready for visualisation.
02
KPI Definition Defined core business metrics: total revenue by country, category-level contribution, period-over-period comparison, and regional performance gaps. Each KPI was mapped to a specific stakeholder question.
03
Dashboard Design & Build Built an interactive Tableau dashboard with dynamic filters by country and category, drill-down capability on product lines, and colour-coded performance indicators to surface outliers at a glance.
04
Publishing & Stakeholder Access Published to Tableau Public for browser-based access with no software installation required. Dashboard is fully responsive and embeddable in any web property.
04

Live Dashboard

LIVE · Revenue of Gadgets by Country Dashboard
05

Key Insights

🌍

Top Revenue Markets

Identified the highest revenue-generating countries, enabling the business to prioritise sales resources and marketing spend in proven markets.

📦

Category Performance

Highlighted which product categories were driving sales growth vs. underperforming — surfacing 3 categories as candidates for pricing or promotion review.

📉

Regional Gaps

Exposed significant performance gaps between regions, giving leadership a clear data-backed case for targeted expansion or product mix adjustment.

06

Business Impact

↑70%
Reduction in time spent building manual sales reports per cycle
3
Underperforming categories identified and flagged for strategy review
∞
Self-serve access for stakeholders — no analyst dependency for queries
07

Roadmap & Next Steps

Time-Series Forecasting Add Python-based revenue trend forecasting using historical data, surfaced directly in the dashboard as a predictive layer for planning cycles.
Planned
Automated Data Refresh Pipelines Replace manual data uploads with a scheduled ETL pipeline using Python + PostgreSQL, enabling the dashboard to refresh automatically on a set cadence.
Planned
Inventory Optimisation Layer Extend the dashboard to include stock-level analytics — linking revenue performance to inventory data to surface reorder recommendations.
Future
AI-Powered Insight Summaries Integrate a Claude API layer to generate natural-language insight summaries from the latest data slice — allowing non-technical users to query performance in plain English.
Research