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Development of an AI-Powered Product Recognition and Information App for Sustainable Retail Experiences
  1. case
  2. Development of an AI-Powered Product Recognition and Information App for Sustainable Retail Experiences

Development of an AI-Powered Product Recognition and Information App for Sustainable Retail Experiences

3sidedcube.com
Retail
eCommerce

Addressing Consumer Information Gaps and Reducing Packaging Waste in Retail Environments

The client faces challenges in conveying product information to customers without relying on physical packaging, amidst rising environmental concerns and consumer demand for transparency. They seek to create an innovative in-store experience that educates customers about products while minimizing packaging waste, aligning with sustainable retail practices.

About the Client

A pioneering retail chain aiming to enhance customer engagement through technology while promoting environmental sustainability and reducing packaging waste.

Goals for Implementing an Intelligent Product Recognition and Information Solution

  • Develop a mobile application capable of accurately recognizing a wide range of products through image recognition technology.
  • Enable instant retrieval and display of detailed product information, including descriptions, ingredients, origin, and health benefits.
  • Enhance the in-store customer experience by integrating the app into devices that are readily accessible without prior installation.
  • Implement machine learning to continuously improve recognition accuracy and adapt to new product launches.
  • Support scalable deployment across multiple store locations with consistent performance and security.

Core Functionalities for a Smart Product Recognition and Customer Engagement Application

  • Product Recognition Module: Utilizes image recognition to identify products, differentiating based on shapes, colors, textures, and patterns from a curated database.
  • Instant Information Retrieval: Displays product names, descriptions, ingredients, origin, and health benefits immediately upon recognition.
  • Pre-installed Device Integration: Supports deployment on preconfigured devices (e.g., in-store tablets) to facilitate effortless user access without downloads or setup.
  • Ongoing Machine Learning Training: Continuously updates and improves recognition accuracy, capable of learning new products as they are launched.
  • User Interaction and Engagement: Enables customers to interact with product data and enhance educational experiences in-store.

Preferred Technical Frameworks and Platforms for Implementation

Open-source machine learning frameworks (e.g., TensorFlow, similar to Google’s ML tools)
Mobile application development with compatibility for pre-installed store devices
Image recognition algorithms optimized for real-time processing
Database systems for storing product images and metadata

External System Integrations Required for Seamless Functionality

  • Product image databases for recognition matching
  • Product information management systems for retrieving product data
  • Device management and deployment platforms for pre-installed devices

Key Non-Functional System Requirements for Scalability and Security

  • High recognition accuracy with minimal false positives
  • Real-time processing with response times under a second
  • Scalable infrastructure to support multiple store locations
  • Robust security measures to protect user data and device integrity
  • Cross-platform compatibility for various device hardware

Projected Business Benefits and Environmental Impact of the Recognition System

The deployment of the AI-powered recognition app aims to significantly reduce packaging waste by enabling a fully unpackaged product display, with projected customer engagement via scans exceeding 2 million interactions. This initiative supports sustainability goals, enhances customer transparency and education, and positions the retail chain as a leader in environmentally conscious retail practices.

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