26 December, 2025 (Last Updated)

Best Hackathon Project Ideas for Retail

What kind of project can stand out in a retail hackathon where customer experience, data, and business impact matter the most? Choosing the right idea is key when solutions must be practical and demo-ready.

Retail hackathons reward projects that improve shopping journeys, optimise inventory, or personalise customer interactions using technology. Exploring the right retail hackathon project ideas helps you build solutions that are relevant, scalable, and achievable within the limited hackathon time.

This guide brings focused retail project ideas that you can quickly build, test, and present with confidence during a hackathon.

Top Retail Hackathon Projects – Overview

Here’s an overview of the 10 best Retail hackathon project ideas:

S.No.	Project Title	Complexity	Estimated Time	Source Code
1	Smart Billing and Checkout System	Easy	6–8 hours	Link
2	Customer Feedback Analysis Tool	Easy	7–10 hours	Link
3	Inventory Tracking and Alert System	Medium	10–12 hours	Link
4	Retail Sales Dashboard and Analytics	Medium	12–14 hours	Link
5	Smart Product Recommendation System	Medium	12–16 hours	Link
6	Demand Forecasting for Retail Products	Medium	15–18 hours	Link
7	AI-Powered Customer Segmentation Tool	Medium	18–22 hours	Link
8	Computer Vision-Based Shelf Monitoring System	Hard	20–26 hours	Link
9	Dynamic Pricing System for Retail Stores	Hard	22–28 hours	Link
10	End-to-End Smart Retail Management Platform	Hard	28–36 hours	Link

Key Focus Areas in Retail Hackathons

Retail hackathons focus on building solutions that improve customer experience, optimise operations, and drive smarter business decisions.
Teams are evaluated on how well their ideas solve real retail challenges.

Customer Experience: Enhancing in-store and online shopping journeys through smoother interactions and personalization.
Inventory Management: Tracking stock levels accurately and preventing overstock or stockouts.
Sales and Revenue Analytics: Analysing sales data to identify trends, peak demand, and performance gaps.
Personalisation and Recommendations: Offering relevant product suggestions based on customer behaviour.
Supply Chain Efficiency: Improving coordination between suppliers, warehouses, and stores.
Pricing and Promotions: Optimising pricing strategies and promotional offers using data insights.

10 Best Retail Hackathon Project Ideas

Choosing the right idea is crucial in a retail-focused event where business value and user experience matter the most.

Below are the best Retail hackathon project ideas that are practical, impactful, and suitable for quick hackathon development.

1. Smart Billing and Checkout System

A fast and efficient billing system that reduces checkout time by automating item scanning and bill generation. It focuses on improving in-store customer experience and reducing manual effort.

Duration: 6–8 hours

Difficulty Level: Easy

Tech Stack Required: Python or JavaScript, QR Code Scanner, SQLite

Implementation Steps:

Scan product QR or barcode
Fetch product price details
Generate bill automatically
Display total and invoice

Key Features:

Quick checkout process
Automated bill calculation
Reduced manual errors

Learnings:

Retail billing workflows
Barcode and QR handling
Transaction logic

Real-World Application:

Supermarkets
Retail outlets

Get Started

2. Customer Feedback Analysis Tool

A tool that collects and analyses customer feedback to understand satisfaction levels and common issues. It helps retailers make data-driven improvements.

Duration: 7–10 hours

Difficulty Level: Easy

Tech Stack Required: Python, NLP Libraries, Flask

Implementation Steps:

Collect customer feedback
Clean and preprocess text
Perform sentiment analysis
Visualise feedback trends

Key Features:

Sentiment classification
Feedback dashboards
Insight summaries

Learnings:

Text analytics
Customer sentiment analysis
Data visualisation

Real-World Application:

Customer support analysis
Service improvement

Get Started

3. Inventory Tracking and Alert System

A system that monitors inventory levels in real time and alerts staff when stock is low. It helps prevent stockouts and overstocking.

Duration: 10–12 hours

Difficulty Level: Medium

Tech Stack Required: Node.js, MongoDB, REST APIs

Implementation Steps:

Track inventory quantities
Define reorder thresholds
Generate alerts
Display inventory dashboard

Key Features:

Real-time stock tracking
Low-stock alerts
Centralised inventory view

Learnings:

Inventory management logic
Backend APIs
Alert systems

Real-World Application:

Warehouses
Retail stores

Get Started

4. Retail Sales Dashboard and Analytics

A dashboard that visualises sales performance across products, categories, and time periods. It helps retailers identify trends and opportunities.

Duration: 12–14 hours

Difficulty Level: Medium

Tech Stack Required: Python, Power BI or Chart.js, SQL

Implementation Steps:

Load sales data
Aggregate metrics
Build visual charts
Enable date filtering

Key Features:

Sales trend analysis
Category performance
Interactive charts

Learnings:

Business analytics
Data aggregation
Dashboard design

Real-World Application:

Retail reporting
Business decision making

Get Started

5. Smart Product Recommendation System

A recommendation engine that suggests products based on customer browsing and purchase history. It focuses on increasing average order value.

Duration: 12–16 hours

Difficulty Level: Medium

Tech Stack Required: Python, ML Libraries, Flask

Implementation Steps:

Collect customer behaviour data
Train recommendation model
Generate product suggestions
Display recommendations

Key Features:

Personalised recommendations
Behaviour-based suggestions
Improved conversions

Learnings:

Recommendation algorithms
Customer behaviour analysis
ML integration

Real-World Application:

E-commerce platforms
Online retail stores

Get Started

6. Demand Forecasting for Retail Products

A forecasting tool that predicts future demand based on historical sales data. It helps retailers plan inventory and promotions.

Duration: 15–18 hours

Difficulty Level: Medium

Tech Stack Required: Python, Time Series Models, Pandas

Implementation Steps:

Prepare historical sales data
Train forecasting model
Predict future demand
Visualise forecasts

Key Features:

Demand prediction
Trend analysis
Forecast charts

Learnings:

Time series analysis
Forecasting techniques
Retail planning logic

Real-World Application:

Inventory planning
Supply chain management

Get Started

7. AI-Powered Customer Segmentation Tool

A system that groups customers based on purchase behaviour and demographics. It supports targeted marketing strategies.

Duration: 18–22 hours

Difficulty Level: Medium

Tech Stack Required: Python, Clustering Algorithms, Pandas

Implementation Steps:

Load customer data
Apply clustering techniques
Label customer segments
Display segment insights

Key Features:

Customer grouping
Behavioural insights
Marketing support

Learnings:

Unsupervised learning
Customer analytics
Data segmentation

Real-World Application:

Loyalty programs
Targeted promotions

Get Started

8. Computer Vision-Based Shelf Monitoring System

A vision-based system that detects empty or misplaced items on shelves using images. It improves shelf availability and store operations.

Duration: 20–26 hours

Difficulty Level: Hard

Tech Stack Required: Python, OpenCV, Deep Learning

Implementation Steps:

Capture shelf images
Detect empty slots
Analyse product placement
Generate alerts

Key Features:

Shelf availability detection
Image-based monitoring
Automated alerts

Learnings:

Computer vision basics
Image processing
Retail automation

Real-World Application:

Large retail stores
Smart supermarkets

Get Started

9. Dynamic Pricing System for Retail Stores

A pricing engine that adjusts product prices based on demand, stock levels, and competition. It focuses on revenue optimisation.

Duration: 22–28 hours

Difficulty Level: Hard

Tech Stack Required: Python, ML Models, Pricing APIs

Implementation Steps:

Analyse pricing factors
Build pricing logic
Update prices dynamically
Track pricing impact

Key Features:

Demand-based pricing
Revenue optimisation
Real-time updates

Learnings:

Pricing strategies
Data-driven decisions
Business optimisation

Real-World Application:

Online retail
Competitive markets

Get Started

10. End-to-End Smart Retail Management Platform

A comprehensive platform that integrates billing, inventory, analytics, and customer insights into one system. It demonstrates full retail digital transformation.

Duration: 28–36 hours

Difficulty Level: Hard

Tech Stack Required: Full Stack, Cloud Services, Databases

Implementation Steps:

Integrate retail modules
Manage central database
Build unified dashboard
Deploy on cloud

Key Features:

Unified retail operations
Centralised analytics
Scalable architecture

Learnings:

System integration
Full-stack development
Retail domain knowledge

Real-World Application:

Large retail chains
Omni-channel retail systems

Get Started

Examples of Top Retail Hackathon Winners

Amazon Smbhav Hackathon (2024) – AI-Powered Regulatory Navigator: The team Coders@IIITB won at the Amazon Smbhav Hackathon with an AI-based regulatory compliance solution designed to improve seller experience on Amazon’s platform.

Flipkart GRiD 7.0 – Intelligent Retail Search Solution: Team Vision Voyagers were national winners at Flipkart GRiD 7.0 for building an advanced autosuggest and real-time personalised search results page system to improve e-commerce search experience.

Final Words

Retail hackathons are all about solving real business problems using technology that improves customer experience and operational efficiency.

By choosing a focused idea, using clean data, and building a demo-ready prototype, you can create a solution that is both practical and impactful within hackathon timelines.

FAQs

1. What are the best Retail project ideas for hackathons?

The best Retail project ideas for hackathons include smart billing systems, inventory tracking tools, recommendation engines, sales analytics dashboards, and demand forecasting solutions.

2. How do I choose the right Retail project for a hackathon?

Choosing the right Retail project depends on the problem statement, data availability, team skills, and selecting an idea that can be built and demonstrated clearly.

3. Which Retail domains are most popular in hackathons?

Retail domains most popular in hackathons include customer experience, inventory management, pricing optimisation, sales analytics, supply chain efficiency, and personalisation.

4. Where can I find open datasets for Retail hackathon projects?

Open datasets for Retail hackathon projects are available on Kaggle, Google Dataset Search, government open data portals, and sample retail datasets on GitHub.

5. Can beginners participate in Retail hackathons?

Yes, beginners can participate in Retail hackathons by choosing simple analytics or automation projects and focusing on clear logic rather than complex models.

6. What tools and frameworks are commonly used in Retail projects?

Tools and frameworks commonly used in Retail projects include Python, SQL, Pandas, Power BI, Tableau, basic ML libraries, and web frameworks for dashboards.

7. How can I complete Retail project quickly during a hackathon?

Completing a Retail project quickly during a hackathon requires limiting features, using sample datasets, dividing tasks clearly, and focusing on one strong use case.

Aarthy R

Aarthy is a passionate technical writer with diverse experience in web development, Web 3.0, AI, ML, and technical documentation. She has won over six national-level hackathons and blogathons. Additionally, she mentors students across communities, simplifying complex tech concepts for learners.