AI-Powered Dynamic Pricing for a French Perfume Retailer
Project category
AmbreCouture
Ecommerce
France
4.5 Months
Boost your margins with AI-driven dynamic pricing and optimize your strategy.
Project Overview
A well-known French perfume retailer wanted to increase profits during the busiest shopping months. But they had one big issue:
Prices needed to change constantly based on demand, stock, and competitor updates — and doing it manually wasn’t possible anymore.
So, we built an AI-powered dynamic pricing system supported by an easy-to-use power bi ecommerce dashboard.
This automated system adjusted prices instantly based on real-time data and insights.
Result : They increased margins by 12% during peak season – automatically.
Main KPIs Tracked
The solution used ecommerce data analytics to monitor all the important pricing indicators.
Here are the KPIs we tracked — explained in simple language:
- Price Elasticity of Demand - How customers react to price changes — whether they buy more or less when price goes up or down.
- Revenue per Unit - How much money the company makes for each item sold.
- Competitor Price Comparison - Shows what competitors are charging so the retailer can adjust accordingly.
- Inventory Levels - How many units are left in stock.
- Sales Velocity - How fast products are selling.
- Gross Profit Margin - How much profit is made after subtracting product cost.
- Customer Conversion Rate - The percentage of people who visit the website and end up buying.
- Promotional Pricing Impact - Shows whether discounts and offers are actually increasing sales.
- Discount Levels - How much the retailer is reducing prices during sales or promotions.
All these KPIs were displayed in simple visuals so anyone could understand performance at a glance.
Solution Offered — AI-Driven Dynamic Pricing Dashboard Development
We followed a straightforward 5-step process to build a smart, automated pricing system.
Collecting All Data in One Place
- We gathered all important data such as sales numbers, previous prices, competitor pricing, inventory levels, website visitor behavior (for conversion rate), and historical discount records.
- Then we cleaned and organized everything so the AI system could understand demand patterns, sales velocity trends, and the impact of previous promotions.
Teaching the System How to Set Prices
- We created AI models that learned when to increase or decrease prices, how demand changes with price (price elasticity), how stock levels affect price, how fast products are selling (sales velocity), and how much revenue each unit generates.
- The AI also learned from competitor pricing, historical discount levels, and how past promotions changed sales, helping it understand the true impact of promotional pricing.
Creating a Simple, Visual Dashboard
- We built a dashboard that showed current pricing, competitor pricing, stock health, profit margins, revenue per unit, sales velocity, customer conversion rate, and the effect of promotions — all in one place.
- Each KPI was displayed in clear visuals so teams could quickly spot issues, compare trends, and understand performance without technical knowledge.
Automatic Price Updates
- The system updated prices automatically based on demand, stock left, competitor pricing, seasonality, discount levels, and how well previous promotions performed.
- This ensured prices stayed optimal at all times — maximizing profit while keeping conversion rates and sales momentum strong.
Alerts & Insights for the Team
- The team received instant alerts when competitors dropped prices, stock was running low, a product was selling too fast or too slow, or a promotion wasn’t delivering results as expected.
- These insights helped the retailer respond immediately, adjust pricing, update discounts, or run new promotional strategies to stay ahead of the competition.
Main Features of the Dashboard
This dynamic pricing dashboard includes smart, automated features that help the retailer adjust prices instantly, stay competitive, and monitor performance in real time. It brings together all the important pricing signals—demand, stock, and competitor moves all in one place.
Everything was automated using real-time ecommerce data.
Business Benefits of Dynamic Pricing
By using AI-driven pricing and real-time data, the retailer experienced major improvements across profit, sales, and customer experience. These benefits helped them respond faster to market changes, avoid stock issues, and maximize revenue during peak seasons.
01
Higher Profits
They achieved a 12% increase in margins during peak seasons.
02
Stay Ahead of Competitors
They reacted faster to competitor price changes.
More Sales
Better pricing led to 10% more sales.
04
Fewer Stock Problems
Smart pricing ensured products didn’t run out or pile up.
05
Better Customer Experience
Shoppers saw fair, competitive prices — boosting trust and conversions.
Who Gains Actionable Insights from This Dashboard?
Tech Stack Used
The solution is powered by a simple yet strong tech stack designed to automate pricing decisions and visualize insights clearly.
Together, these tools ensure fast data processing, smart price predictions, and easy-to-understand dashboards for daily decision-making.
Power BI
Azure Machine Learning
SQL Database
Results Achieved
The dynamic pricing system delivered strong, measurable improvements across profit, sales, conversions, and inventory efficiency.
Key Results:
The retailer not only earned more but also became faster, more competitive, and more customer-friendly during busy seasons.