Second Version on the Product Color Variation Management using Artificial Intelligence

This research explores using artificial intelligence (AI) for managing color variations in products to boost market performance by optimizing product aesthetics and aligning with consumer preferences. The study investigates a system that leverages AI, deep learning, and neural networks to analyze real-time consumer data, including product preferences, buying history, and sales history. An AI model was created to predict and modify product colors dynamically, aiming to maximize consumer appeal and engagement. The system workflow includes stages for data gathering, processing, feature extraction, model training, software integration, and testing. AI-driven interventions were evaluated through consumer satisfaction, sales metrics, and digital engagement analytics, illustrating the potential for AI to influence product design effectively. This research suggests a transformative best practice for consumer-centered marketing, where AI facilitates color customization aligned with evolving consumer trends. (Sasibhushan Rao Chanthati, 2022)

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-12
DOI
https://doi.org/10.5281/zenodo.14095303
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Second Version on the Product Color Variation Management using Artificial Intelligence

Zenodo (CERN European Organization for Nuclear Research)
Functional Brain Connectivity Studies
article

Second Version on the Product Color Variation Management using Artificial Intelligence

article en

Abstract

This research explores using artificial intelligence (AI) for managing color variations in products to boost market performance by optimizing product aesthetics and aligning with consumer preferences. The study investigates a system that leverages AI, deep learning, and neural networks to analyze real-time consumer data, including product preferences, buying history, and sales history. An AI model was created to predict and modify product colors dynamically, aiming to maximize consumer appeal and engagement. The system workflow includes stages for data gathering, processing, feature extraction, model training, software integration, and testing. AI-driven interventions were evaluated through consumer satisfaction, sales metrics, and digital engagement analytics, illustrating the potential for AI to influence product design effectively. This research suggests a transformative best practice for consumer-centered marketing, where AI facilitates color customization aligned with evolving consumer trends. (Sasibhushan Rao Chanthati, 2022)

Zenodo (CERN European Organization for Nuclear Research)
Institute for Advanced Journalism Studies (US)
Openalex Percentile: Top 60%
Functional Brain Connectivity Studies
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Second Version on the Product Color Variation Management using Artificial Intelligence · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS