top of page
logo

Global Gross Profit Dashboard (Locker Studio)

Google Data Studio.png

1. Business Problem

The business needed a unified, interactive dashboard to understand global revenue, gross profit, product performance, and country-level trends.

This project was important because leadership lacked a consolidated view of profitability across product lines, sales channels, and regions.

Requested by: Executive Leadership & Finance

Supported decisions:

  • Product portfolio optimization

  • Regional sales strategy

  • Pricing and cost management

  • Forecasting and revenue planning

Document evidence: “Gross Profit by Product Line — Golf Equipment 40.2%, Outdoor Protection 33.9%”.

2. Objectives

Goals:

  • Build a centralized profitability dashboard

  • Improve visibility into product and regional performance

  • Enable data-driven revenue forecasting

  • Support executive decision-making with real-time insights

KPIs Measured:

  • Revenue

  • Gross Profit

  • Gross Profit %

  • Product-level profitability

  • Country-level revenue

  • Sales channel performance

Success Definition:

  • Executives can quickly identify high-performing products, regions, and channels

  • Forecast accuracy improves

  • Manual reporting time decreases

3. Stakeholders

  • Executive Leadership

  • CFO & Finance Team

  • Sales & Marketing

  • Regional Managers

  • Product Managers

  • Operations

4. Data Sources

Based on the dashboard structure, the project likely used:

  • Sales transaction data (SQL or CSV)

  • Product master data

  • Country/region mapping tables

  • Order method data (Web, Telephone, Sales Visit, Email)

Document evidence: “Gross Profit by Order Method Type — Web $149M, Telephone $142M”.

5. Data Volume

From the dashboard:

  • 89.2M units sold

  • $4.7B revenue

  • $1.9B gross profit

  • Multi-year country-level data (2013–2016 table)

6. Data Cleaning

Issues likely addressed:

  • Missing country values

  • Standardizing product categories

  • Correcting revenue and cost fields

  • Removing invalid or negative values

  • Ensuring consistent date formats

7. Data Transformation

Transformations performed:

  • Aggregated revenue and gross profit by product line

  • Built calculated fields for gross profit %

  • Created date hierarchies (Year, Month)

  • Merged product, sales, and country datasets

  • Built forecasting metrics (10% revenue forecast)

Document evidence: “Forecast: 10% — Revenue Forecast 5.2B”.

8. Data Model

Likely a star schema:

  • Fact Table: Sales Transactions (Revenue, Cost, Units, Date, Country, Product)

  • Dimensions: Product, Country, Order Method, Date

  • Relationships based on product ID, country code, and date key.

9. Analysis

Key insights discovered:

  • Golf Equipment generated the highest gross profit (40.2%).

  • Outdoor Protection also performed strongly (33.9%).

  • Eyewear had the highest revenue ($867M) and strong gross profit ($352M).

  • United States and Japan were top revenue contributors (“Revenue $826,912,571.54”).

  • Web and Telephone channels drove the highest gross profit.

  • Monthly revenue showed seasonal fluctuations.

10. Visualization Choices

  • KPI Cards for revenue, gross profit, product cost → quick executive overview

  • Bar Charts for product line profitability → easy comparison

  • Line Charts for monthly revenue → trend analysis

  • Maps for country-level revenue → geographic insights

  • Tables for product-level revenue and gross profit → detailed breakdown

  • Stacked bars for order method performance → channel comparison

Each visual was chosen to make insights intuitive and actionable.

11. Automation

  • Automated data refresh through Google Data Studio connectors

  • Dynamic filters (Year, Product, Country)

  • Real-time updates from source data

  • Forecasting logic applied automatically

12. Challenges & Solutions

Challenges:

  • Multiple product categories

  • Large data volume (billions in revenue)

  • Country-level inconsistencies

  • Need for executive-friendly visuals

Solutions:

  • Standardized product and country fields

  • Used aggregated views to improve performance

  • Built clear KPI cards and segmented visuals

  • Implemented filters for flexible exploration

13. Business Impact

  • Improved executive decision-making

  • Enabled faster profitability analysis

  • Highlighted high-margin product lines

  • Supported global expansion strategy

  • Reduced manual reporting time significantly

  • Provided clear visibility into regional performance

14. KPIs

  • Revenue

  • Gross Profit

  • Gross Profit %

  • Units Sold

  • Product Revenue

  • Country Revenue

  • Order Method Profitability

  • Forecasted Revenue

15. Tools Used

  • Google Data Studio

  • SQL (likely for source data)

  • Excel / CSV

  • Data connectors

  • Geographic visualization tools

  • Forecasting formulas

16. Technical Skills Demonstrated

  • Data Cleaning

  • Data Modeling

  • ETL

  • Dashboard Design

  • KPI Development

  • Trend Analysis

  • Geographic Visualization

  • Forecasting

  • Executive Reporting

17. Lessons Learned

  • Importance of clear KPI definitions

  • Need for standardized product and country data

  • Value of geographic visuals for global businesses

  • Importance of performance optimization

  • Designing dashboards for executive consumption

18. What Would You Improve?

  • Add predictive analytics for future revenue

  • Implement real-time data streaming

  • Add drill-through pages for product managers

  • Enhance mobile layout

  • Add row-level security for regional managers

  • Expand forecasting beyond 10%

bottom of page