Price Optimizing
Price Optimizing
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Project: AI-Powered Price Optimization
Project: AI-Powered Price Optimization
The Challenge
The Challenge
A local retailer struggled with stagnant sales—their pricing strategy was stuck in the past!
A local retailer struggled with stagnant sales—their pricing strategy was stuck in the past!
The Solution
The Solution
Using Python and price elasticity analysis, I built a dynamic pricing model that:
Using Python and price elasticity analysis, I built a dynamic pricing model that:
- 📈 Increased total sales by $2,190 without changing inventory.
- 📈 Increased total sales by $2,190 without changing inventory.
- 🎯 Personalized prices for 5 product segments (e.g., Beauty, Electronics).
- 🎯 Personalized prices for 5 product segments (e.g., Beauty, Electronics).
How It Works
How It Works
1. Data Analysis: Uncovered trends in historical sales data.
1. Data Analysis: Uncovered trends in historical sales data.
2. Elasticity Calculation: Identified which products could handle price changes.
2. Elasticity Calculation: Identified which products could handle price changes.
3. Dynamic Rules: Automated price adjustments (+5% for low-elasticity, -5% for high-elasticity).
3. Dynamic Rules: Automated price adjustments (+5% for low-elasticity, -5% for high-elasticity).
See the Magic
See the Magic
The Price Whisperer: How AI Boosted Sales by 2K in 30 Days!
The Price Whisperer: How AI Boosted Sales by 2K in 30 Days!
Chapter 1: The Data Detective
Chapter 1: The Data Detective
It all began with a CSV file named retail_sales_dataset.csv—a treasure trove of 1,000 transactions. My mission? To crack the code of pricing chaos!
Toolkit: Python, Pandas, Matplotlib.
First Clue: Histograms revealed most products sold were in the $50-200 range.
Plot Twist: Men bought expensive electronics; women preferred mid-range clothing!
Chapter 2: The Time Traveler
Chapter 2: The Time Traveler
I transformed the Date column into a time machine! Weekly trends showed:
Holiday Peaks: Sales spiked in November (Black Friday madness!).
Spring Slump: Flat sales in March-April.
Time to disrupt the status quo with dynamic pricing!
Chapter 3: The Elasticity Enigma
Chapter 3: The Elasticity Enigma
Price elasticity (elasticity = %ΔQty / %ΔPrice) was the key!
Low-Price Products : Elasticity = -0.49 → "Raise prices, no one cares!"
Luxury Products : Elasticity = +0.12 → "Cheaper = More prestige?!
Chapter 4: The Dynamic Pricing Revolution
Chapter 4: The Dynamic Pricing Revolution
I unleashed pricing rules like a wizard:
Low-elasticity items: +5% price hike.
High-elasticity luxuries: -5% discount.
Result? Sales jumped from $373K to $375K—without losing customers!
🚀 GitHub CTA Section:
🚀 GitHub CTA Section:
Curious about the code?
Visit my Github to see the magic behind the numbers!
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