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Development of a Near Real-Time Competitor Pricing Analysis Platform for Luxury Retail

n-ix.com

Retail

Consumer products & services

eCommerce

Business Challenges in Competitor Pricing Analysis

The client struggled with outdated third-party pricing data retrieval systems that offered only 70% product matching accuracy, daily reporting delays, and limited scalability to add new competitors. This hindered their ability to create timely, data-driven pricing strategies in a competitive luxury market.

About the Client

A leading luxury store chain operating in over 100 countries, specializing in exclusive clothing, accessories, and home products.

Project Goals for Enhanced Pricing Strategy Platform

Develop a microservices-based platform for near real-time competitor pricing data collection
Improve product-price matching accuracy to support strategic decision-making
Enable seamless scalability for competitor analysis
Create an intuitive UI for sales and marketing teams
Implement automated CI/CD pipelines for rapid deployment

Core System Functionalities and Features

Web scraping bots for competitor price extraction
Real-time pricing data requests by market, currency, brand, and product category
Automated Excel report generation with competitor-client price comparisons
Intuitive UI for managing pricing data and filters
Manual testing protocols for error-free data collection

Technology Stack Requirements

Scrapy

Angular

AWS

Redis

NoSQL

Flask

Docker

External System Integrations

Competitor website scraping interfaces
Client's existing CRM and sales analytics systems

Performance and Scalability Requirements

99.9% accuracy in product-price matching
Sub-5-minute data refresh intervals
Horizontal scalability for 500+ competitor websites
Role-based access control for pricing data
99.99% system uptime SLA

Expected Business Outcomes

The platform will enable dynamic pricing strategies through real-time competitor insights, projected to increase sales conversion rates by 15-20%. Automated reporting will reduce manual analysis time by 70%, while the CI/CD pipeline accelerates feature deployment cycles by 40%.