Turning Raw Transactions into a

20% Efficiency Strategy

How we used data to identify peak sales hours, eliminate inventory waste, and drive profitability for a coffee retail business.

screenshot 2025 11 02 212009
Case Study: Optimizing Retail Revenue | Usamah Maphumulo

The Project Stack

Technical Foundation & Methodology

This project was built on a modern, robust data stack to ensure reliability, scalability, and deep analytical capability, transforming raw transaction logs into actionable business intelligence.

Python (Pandas)
SQL (Data Warehousing)
Tableau (Visualization)
Data Cleaning & ETL

The Challenge: Operating on "Gut Feeling"

Many businesses operate by guessing. They guess how much stock to order, when to schedule staff, and which products drive profit. This leads to wasted inventory and lost revenue during unpredicted peak hours.

The Solution: A Decision Engine

We didn't just build a dashboard; we built a proactive management tool. By analyzing thousands of transaction records, we moved the business from reactive guessing to data-driven precision.

80/20
Rule Identified

Identified the 20% of products driving 80% of revenue.

15%
Waste Reduction

Cut underperforming stock from inventory orders.

Peak
Optimization

Captured missed revenue during hidden "5 PM Rushes".

ROI Focused

Solving Real Business Problems

Moving beyond charts to actionable business intelligence.

The Problem

"Are we busy, or are we profitable?"

The client knew mornings were busy but missed revenue opportunities in the afternoon due to understaffing.

The Fix: Discovered a hidden "3 PM Slump" (cut costs) and a "5 PM Rush" (add staff).

The Problem

Dead Stock & Cash Flow

Certain products were sitting on shelves, expiring, and tying up cash flow.

The Fix: A "Sales Velocity vs. Margin" matrix helped cut the bottom 15% of underperformers.

The Transformation

OLD WAY

Gut Feeling

  • Ordering stock based on "what looks low."
  • Staffing the same number of people all day.
  • "We sell a lot of Lattes."
NEW WAY

Data Intelligence

  • Ordering based on predictive demand.
  • Dynamic staffing aligned with hourly revenue.
  • "Lattes drive volume, but Espressos drive profit."

Stop Guessing. Start Growing.

Your business generates data every single day. Are you using it to make money, or letting it sit in a spreadsheet?