Region:Asia
Author(s):Geetanshi
Product Code:KRAA4780
Pages:98
Published On:September 2025
By Type:The market is segmented into four main types: Predictive Analytics, Prescriptive Analytics, Descriptive Analytics, and Others.Predictive Analyticsis currently the leading sub-segment, driven by its ability to forecast future demand based on historical data, which is crucial for inventory management and sales strategies.Prescriptive Analyticsfollows closely, providing actionable insights to optimize decision-making processes. AI-driven analytics are increasingly being adopted for real-time demand sensing, dynamic pricing, and personalized recommendations, reflecting the latest market trends.
By End-User:The end-user segmentation includes Fashion Retail, Grocery Retail, Electronics Retail, Home & Furniture Retail, Online Marketplaces, and Others.Fashion Retailis the dominant segment, as brands increasingly utilize AI to predict trends, personalize customer experiences, and manage inventory effectively.Grocery Retailis also growing rapidly, driven by the need for efficient supply chain management, demand sensing, and customer insights. Electronics and furniture retailers are adopting AI for assortment optimization and omnichannel strategies, while online marketplaces leverage AI for personalized recommendations and dynamic pricing.
The India AI in Retail Demand Forecasting Market is characterized by a dynamic mix of regional and international players. Leading participants such as TCS (Tata Consultancy Services), Infosys, Wipro, HCL Technologies, Tech Mahindra, Fractal Analytics, Mu Sigma, Manthan Systems, Algonomy, BigBasket, Flipkart, Amazon India, Reliance Retail, Myntra, Dunzo contribute to innovation, geographic expansion, and service delivery in this space.
The future of the AI in retail demand forecasting market in India appears promising, driven by technological advancements and evolving consumer expectations. As retailers increasingly embrace digital transformation, the integration of AI with IoT and big data analytics will enhance inventory management and customer insights. Furthermore, the anticipated government support for digital initiatives is likely to foster innovation, enabling retailers to leverage AI for improved operational efficiency and customer engagement, ultimately reshaping the retail landscape.
| Segment | Sub-Segments |
|---|---|
| By Type | Predictive Analytics Prescriptive Analytics Descriptive Analytics Others |
| By End-User | Fashion Retail Grocery Retail Electronics Retail Home & Furniture Retail Online Marketplaces Others |
| By Region | North India South India East India West India |
| By Technology | Machine Learning Natural Language Processing Computer Vision Chatbots & Conversational AI Others |
| By Application | Inventory Management Sales Forecasting Customer Behavior Analysis Dynamic Pricing In-Store Optimization Others |
| By Investment Source | Private Investments Government Funding Venture Capital Corporate Innovation Funds Others |
| By Policy Support | Subsidies for AI Development Tax Incentives for Retail Innovation Grants for Technology Adoption Regulatory Sandboxes for AI Pilots Others |
| Scope Item/Segment | Sample Size | Target Respondent Profiles |
|---|---|---|
| AI Adoption in E-commerce | 100 | eCommerce Managers, Data Analysts |
| Demand Forecasting in Brick-and-Mortar Retail | 80 | Store Managers, Inventory Planners |
| Consumer Behavior Insights | 60 | Marketing Directors, Customer Experience Managers |
| Supply Chain Optimization Strategies | 50 | Supply Chain Managers, Operations Managers |
| Impact of AI on Retail Sales | 70 | Financial Analysts, Business Development Managers |
The India AI in Retail Demand Forecasting Market is valued at approximately USD 215 million, driven by the increasing adoption of AI technologies in retail operations, enhancing inventory management, dynamic pricing, and customer experience.
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