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RetailAWS SageMakerPyTorch

Retail Sales Forecasting & Dynamic Pricing with AI/ML Platform

70% Improvement
Key Performance Metric

Project Overview

Client:Leading Retail Chain
Industry:Retail
Duration:7 months
Team Size:AI/ML Development Team

Challenge

Leading retail teams needed to improve sales forecasting across thousands of SKUs and automate pricing decisions that could adapt to market trends, inventory fluctuations, and promotional strategies. Key pain points included inaccurate sales forecasting leading to stockouts and overstock, manual pricing strategies lacking agility, and limited use of real-time inventory or demand signals.

Our Solution

We architected and deployed an end-to-end AI/ML platform tailored to dynamic retail environments.

Demand Forecasting

Built deep learning models using LSTM, ARIMA, and XGBoost. Integrated calendar features, promotions, seasonality and achieved higher accuracy by combining classical + neural methods.

Dynamic Pricing Optimization

Developed Reinforcement Learning and Bayesian Inference models. Simulated pricing scenarios and elasticity curves, integrated with POS data and external market factors.

Customer Segmentation & Cross-Sell

Implemented Market Basket Analysis using Apriori and FP-Growth. Boosted accessory and add-on sales by targeting high-affinity items.

Real-Time Architecture

Used PySpark on AWS Glue for ingestion. Live pipeline from sales/inventory systems to model triggers with automated restocking insights and pricing updates.

MLOps & Automation

Trained and deployed using AWS SageMaker Pipelines. Enabled model retraining, A/B testing, and monitoring with versioned pipeline artifacts and rollback support.

Results

+15% Sales Forecast Accuracy

Achieved significant improvement in sales forecasting accuracy across thousands of SKUs through advanced machine learning models.

12% Reduction in Stockouts

Dramatically reduced inventory management issues through better demand prediction and real-time insights.

+9% Revenue Uplift from Pricing Optimization

Dynamic pricing strategies based on reinforcement learning models delivered measurable revenue improvements.

+27% Boost in Cross-Sell

Market basket analysis and customer segmentation significantly increased accessory and add-on sales through targeted recommendations.

SAR 800k+ Monthly Business Impact

Delivered substantial monthly business impact across pilot stores through comprehensive AI/ML platform implementation.

What Our Client Says

"
The predictive pricing engine completely changed how we run promotions — real-time pricing with measurable lift.
Head of Retail Analytics
Leading Retail Chain

Technologies Used

Frontend & Backend

AWS SageMakerPyTorchXGBoostReinforcement LearningLSTM & ARIMA

Infrastructure & Tools

PySparkAWS GlueBayesian ModelsMarket Basket Analysis

Ready to Build Your Own AI-Driven Commerce Engine?

Let’s turn your data into real business advantage — with accurate forecasting, agile pricing, and smarter automation.