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E-commerce Price Monitoring with AI-Driven Web Scraping and Cloud Deployment

Client Overview

Our client, an emerging e-commerce platform specializing in consumer electronics, faced challenges in staying competitive in a dynamic market. Competitors frequently adjusted prices, launched promotions, and restocked popular products faster than the client could respond. Manual tracking methods were ineffective and limited their ability to attract and retain customers.

Challenge

The client required a solution to:Monitor competitor pricing and promotions in real-time.

Track stock availability of key products to avoid missed sales opportunities.

Use predictive analytics to identify emerging product trends and optimize inventory.

Access actionable insights seamlessly, overcoming delays caused by local infrastructure limitations.

The lack of automated, real-time insights placed the client at a disadvantage during flash sales and promotional events.

Solution Implemented

We implemented an AI-powered price monitoring and trend analysis system using a custom Python-based web scraper integrated with advanced AI algorithms and deployed on the cloud for scalability and performance

  • Key Features of the Solution

    1. Dynamic Price Tracking:

    An automated scraping system powered by Python (Scrapy, Selenium) to monitor competitor websites for product prices, discounts, and promotions.

    AI algorithms for detecting outliers and pricing patterns, ensuring accurate data even from dynamic JavaScript-heavy sites.

  • 2. Stock Availability Monitoring:

    AI-driven stock detection that analyzed product pages for visual and textual cues (e.g., “Out of Stock” banners).

    Enabled the client to respond quickly to changes in competitor inventory.

  • 3. Trend Analysis with AI:

    Integrated machine learning models to identify product trends, seasonal demand, and pricing behavior using historical data.

    Sent predictive recommendations to optimize inventory and pricing strategies.

  • 4. User-Friendly Dashboard:

    Built an interactive dashboard with Streamlit to present real-time insights, trend predictions, and actionable metrics.

  • 5. Cloud Deployment:

    Deployed the solution on AWS, ensuring high availability and scalability.

  • AI and Cloud Deployment Details

    1. AI Integration:

    Deployed TensorFlow for trend analysis and predictive modeling.

    Incorporated NLP algorithms to analyze competitor reviews for additional insights into customer sentiment and demand.

  • 2. Cloud Infrastructure:

    AWS EC2 Instances: Hosted the scraper and AI models for continuous operation.

    AWS Lambda: Used serverless computing to schedule and execute periodic scraping tasks, reducing costs.

    AWS RDS (PostgreSQL): Ensured secure and scalable data storage for large volumes of scraped data.

    AWS S3: Stored scraped HTML files for archival and auditing purposes.

    AWS Elastic Beanstalk: Hosted the dashboard with auto-scaling and load balancing.

  • 3. Data Security:

    Leveraged AWS IAM roles and VPC for restricted access to the infrastructure, ensuring data privacy.

Outcome

Increased Sales by 30% : Real-time pricing and stock insights enabled the client to competitively adjust their pricing strategy, resulting in higher sales.

Improved Decision-Making : AI-driven trend analysis provided actionable insights, helping the client proactively stock high-demand products.

Operational Efficiency : Automation reduced manual efforts by 80%, freeing up the client’s team to focus on strategic activities.

Scalability : Cloud-based deployment ensured the system handled growing data volumes and user demand without performance degradation.

Reduced Downtime : The robust AWS infrastructure ensured 99.9% uptime for uninterrupted access to insights.

Client Testimonial

“The AI-powered solution developed by  Exotica IT Solutions has transformed our approach to pricing and inventory management. The integration of automation, predictive analytics, and cloud scalability has given us a significant edge in a competitive market.”

Tools and Technologies Used

AI Tools : TensorFlow, Scikit-learn, NLP algorithms

Python Libraries : Scrapy, Selenium, Pandas, BeautifulSoup

Dashboard : Streamlit

Database : PostgreSQL on AWS RDS

Cloud Platform : AWS (EC2, RDS, Lambda, S3, Elastic Beanstalk, IAM)