🎯 AI-Driven Media Investment Plan Across Channels for E-commerce 🚀 BUDGET MASTER APPLICATION

Project Name

Description

Hackathon Project Summary: AI-Driven Media Investment Plan 🚀💼

Problem: E-commerce businesses face challenges in optimizing marketing budgets across multiple paid media channels, often resulting in inefficient allocation and reduced ROI. Traditional methods lack data-driven insights into customer journeys and ad performance, limiting their effectiveness. 📉

Solution: The BUDGET MASTER APPLICATION leverages AI-driven, Retrieval-Augmented Generation (RAG) LLM chatbot to intelligently optimize a $200,000 marketing budget. By analyzing customer journey data and ad spend insights, combined with machine learning techniques, the application reallocates budgets across channels to maximize conversions and boost overall marketing efficiency. 💡

Key Tools:

AzureChatOpenAI 🤖: Provides real-time AI insights and generates intelligent responses.
LangChain & Chainlit 🛠️: Enables document retrieval and a conversational interface for interactive user queries.
Machine Learning (Random Forest & Gradient Boost) 📊: Powers predictive analysis and budget reallocation strategies.
Python REPL & Vector Store 🧰: Handles data processing, document search, and deep analysis for decision-making.

This solution empowers marketers with data-driven decision-making tools, enabling better budget management, improved conversion rates, and a higher return on investment. 📈

Technology & Languages

[ ] JavaScript
[ ] Java
[ ] .NET
[X] Python
[X] AI Studio
[X] AI Search
[ ] PostgreSQL
[ ] Cosmos DB
[ ] Azure SQL

Project Repository URL

https://github.com/karthik-keerthi/BUDGET-MASTER---ECOMMERCE

Deployed Endpoint URL

No response

Project Video

https://www.popai.pro/share.html?shareKey=51436dd50214b0bb877c20f5b09c519a025e6cd19e568d00fda8ce24ce954155&utm_source=presentationsharepage

Team Members

https://github.com/karthik-keerthi

microsoft / RAG_Hack