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Optimizing an E-commerce App Performance with Advanced Deep Learning-Based Demand Forecasting For A TV Home Shopping and Retail Platform

Optimizing an E-commerce App Performance with Advanced Deep Learning-Based Demand Forecasting For A TV Home Shopping and Retail Platform

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    Synopsis

    Technology Stack

    Azure Blob

    Azure ML Studio

    SQL Server

    Tensor Flow

    Python

    Open AI

    Lama 2

    Client Overview

    The client is a global retailer of fashion jewelry and lifestyle accessories, streaming live telecasts across the US, UK, Canada, and Ireland.

    Challenge

    Utilizing AI-powered demand forecasting with deep learning, the client aimed to optimize their e-commerce platform, to boost sales, improve customer satisfaction, streamline operations, and solidify their industry leadership.

    Solution

    • Advanced Demand Forecasting: The solution employs deep learning algorithms for accurate demand predictions, presented through intuitive visualizations.
    • Interactive Frontend Interfaces: Generative AI interfaces enable users to explore forecasted demand scenarios interactively, facilitating proactive inventory replenishment and strategic planning.
    • Data-driven Insights: Leveraging historical sales and seasonality data, the solution offers precise demand forecasts for informed decision-making.
    • Optimized Inventory Management: By anticipating demand trends and uncertainties, our solution aids in optimizing inventory levels to prevent revenue loss from stockouts or overstock situations.

    Outcome

    10-15% New Customer Acquisition
    Business Process Optimization
    20% Increase in Sales Conversion
    New Market Opportunities

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