Image Recognition in Retail Market Growth, Analysis Report, Share, Trends and Overview 2022-2028

 

The global market for image recognition in retail is projected to have considerable CAGR during the forecast period. The growing integration of AI technology in image recognition further provide significant growth opportunity to the market growth. Several companies in retail sector including e-commerce are rapidly implementing image recognition. E-commerce has increasingly become popular over the years as people find it convenient to shop from their homes or office. Factors such as saving time, free home delivery, better price, and vast variety of product offerings is gradually shifting the population’s preference towards e-commerce. However, internet shopping is associated with higher risk by consumers due to intrinsic characteristics associated with virtual stores where there is no human contact and consumers cannot physically check the quality of a product or monitor the safety and security of sending sensitive, personal, and financial information while shopping on the internet.

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The retail sector is also among the significant shareholding segments in the e-commerce including search and advertising. Image recognition is utilized in mobile applications for the identification of particular products. Also, it presents a more interactive view by making everything they see searchable. For instance, technology by CamFind API by Image Searcher Inc. that enables an advanced level of mobile commerce. CamFind assists in identification of objects such as watches, shoes, bags, and sunglasses among others and returns purchasing options to the user. Future buyers can compare products lives without visiting any other websites. Developers utilizes this image recognition API to create their own mobile commerce applications. Likewise, ViSenze also solves real-world search problems using deep learning and image recognition. Products made by ViSenze are used by online shoppers, internet retailers, and media owners for the use of product recommendation and Ad targeting.

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AI in image recognition in the retail sector provides tailored, data-driven digital merchandising solutions that eliminate retail audit costs, drive sales, boost profitability and build customer loyalty. Rapid advancements in machine learning and AI along with use of high bandwidth data services is fostering the growth of the technology. The important aspects of AI used by online retail stores include data mining, natural language processing (NLP), and machine learning. AI in image recognition has transformed the way of shopping. The retail stores are investing in AI-powered personalization engines as it assists the customer to search products online by voice command or searching with the image upload using the smartphone. Companies such as Amazon, Flipkart, and Myntra are using machine learning tools to estimate demand, maintaining stock accordingly and setting price on the basis of customer expenditure and requirements.

Global Image Recognition in Retail Market Segmentation

By Deployment Type

·         On-Premises

·         Cloud-Based

By Application

·         Vision Analytics

·         Marketing and Advertising

·         Visual Product Search

·         Others ( Security and Surveillance)

Image Recognition in Retail Market– Segment by Region

North America          

·         United States

·         Canada

Europe

·         Germany

·         United Kingdom

·         France

·         Spain

·         Italy

·         Rest of Europe

Asia-Pacific   

·         China

·         Japan

·         India

·         Rest of Asia-Pacific

Rest of the World

·         Middle East & Africa

·         Latin America

Company Profiles

·         Amazon Web Services, Inc.

·         Blippar Ltd

·         Catchoom Technologies, S.L.

·         Clarifai, Inc.

·         Deepomatic

·         Google LLC

·         IBM Corp.

·         Imagga Technologies Ltd.

·         Intelligence Retail

·         LTUTech

·         Microsoft Corp.

·         NEC Corp.

·         ParallelDots, Inc.

·         Qualcomm Technologies, Inc.

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