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AI in Retail Market Outlook by Revenue & Regional Analysis Forecast To 2024

Author : Ronak Bora | Published Date : 2020-09-10 

North America accounts for over 35% share in the AI in Retail market. The increasing investments in the AI technology, the early adoption, and the presence of tech giants, such as AWS, Microsoft, Google and IBM, in the country also foster the market growth. Asia Pacific AI in Retail market is anticipated to grow significantly with a CAGR of more than 45%. The growth of the market is driven by e-commerce industry in the region. Furthermore, the investment in the AI by the Chinese technology giants Alibaba and Baidu also fuel the market growth.

The solutions market is estimated to lead the AI in Retail market with over 85% share in the revenue. The growing demand for the advanced analytics solutions among the retailers to extract the consumer data is propelling the growth. Services market is anticipated to grow at a CAGR of over 45% during the forecast timeline. The increasing demand among the retailers for the third-party services is the driving the growth.

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Recommendation engine is the most used solution among the retailers accounting for more than 35% share in the revenue. The growth of the recommendation engine market is credited to the rising demand for the personalized shopping experience among the customers. Furthermore, the demand for the visual search solutions is estimated to grow at a CAGR of more than 45% during 2018-2024.

Natural Language Processing (NLP) leads AI in Retail market with over 40% share in the revenue. The growing demand to provide the enhanced customer experience is the major force augmenting the market growth. Machine learning and deep learning technology are anticipated to surpass the NLP technology during the forecast period with a CAGR of over 42%. The market is driven by the increasing investments in machine learning and deep learning technology.

Endorsed with a rich portfolio of digitally empowered consumers, artificial intelligence in retail market has been acclaimed as one of the most progressive verticals in the recent years. In the year 2017, this business space registered a global share of USD 650 million and is forecast to record a CAGR of 40% over 2018-2024, which is indeed humongous. With retail space becoming affluent with more number of e-commerce platforms as well as startups with huge technology appetite, the adoption of AI in retail market is bound to proliferate. Add to it, the technology is witnessing a slew of opportunities in sync with the hi-tech trends including the like of blockchain, AR, and Internet of Things establishing its footprints in the retail domain. Amidst all these tech interventions, the focal point of this consumer-driven industry remains delivering unrivaled shopping experience to the customers as well as ensuring optimal management of resources and stocks.

One of the classic examples of leveraging the AI technology is of Baidu, China’s renowned artificial intelligence market player. The company grabbed the headlines with its attempt to promote its AI technology in aiding convenience stores manage their food stock more efficiently and resourcefully. For the record, the search engine giant has been supporting Ping++, an AI service provider since September 2017 to work with Today, convenience store chain with regard to a fresh food project. Under the terms, the company utilizes PaddlePaddle, a deep learning platform by Baidu along with its CTR model. Reportedly, the implementation of these technologies had a positive impact on the sales, post which Baidu introduced this model to dozens of stores for testing. In testing stores, the profits surged by 20% and food waste dropped by 30%. The instance vividly portrays the huge scope of AI in retail market. Since the past two years Baidu has invested over USD 1.5 billion in AI research in addition to its investment of USD 200 million for the development of a new R&D facility.

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The companies that serve AI-powered solutions in the retail sector are Google, Microsoft, IBM, AWS, Baidu, Intel, Oracle, SAP, Salesforce.com, Nvidia, Interactions, CognitiveScale, Lexalytics, Inbenta Technologies, NEXT IT, RetailNext, Sentient Technologies, Visenze, and BloomReach.

Table of Contents (ToC) of the report:

Chapter 1.   Methodology & Scope

1.1.  Methodology

1.1.1. Initial data exploration

1.1.2. Statistical model and forecast

1.1.3. Industry insights and validation

1.1.4. Scope,

1.1.5. Definition

1.1.6. Methodology and research parameters

1.2.  Data Sources

1.2.1. Primary

1.2.2. Secondary

Chapter 2.   Executive Summary

2.1.  AI in Retail Msrket 360º synopsis, 2013 - 2024

2.1.1. Business trends

2.1.2. Regional trends

2.1.3. Component trends

2.1.4. Solution trends

2.1.5. Service trends

2.1.6. Technology trends

2.1.7. Application trends

Chapter 3.   AI in Retail Market Insights

3.1.  Introduction

3.2.  Industry segmentation

3.3.  Industry landscape, 2013-2024

3.4.  Industry ecosystem analysis

3.5.  Industry evolution

3.6.  Market news

3.7.  Technology & innovation landscape

3.7.1. Gesture recognition

3.7.2. Virtual mirrors

3.7.3. Chatbots

3.7.4. Video analytics

3.7.5. Robots

3.8.  Regulatory landscape

3.8.1. Health Insurance Portability and Accountability Act (HIPAA)

3.8.2. Payment Card Industry Data Security Standard (PCI DSS)

3.8.3. North American Electric Reliability Corp. (NERC) Standards

3.8.4. Federal Information Security Management Act (FISMA)

3.8.5. The Gramma-Leach-Bliley Act (GLB) Act of 1999

3.8.6. Sarbanes-Oxley Act of 2002

3.8.7. General Data Protection Regulation (GDPR)

3.9.  Use cases

3.9.1. Sales & CRM application

3.9.2. Customer recommendations

3.9.3. Logistics & delivery

3.9.4. Payment service

3.10.      Industry impact forces

3.10.1.  Growth drivers

3.10.1.1.     Growing investment in AI

3.10.1.2.     Increasingly empowered consumer

3.10.1.3.     Disruptive technologies

3.10.1.4.     Advent of new business models

3.10.1.5.     Advancement in data science

3.10.2.  Industry pitfalls & challenges

3.10.2.1.     Limited public-private partnership to address social implications directly

3.10.2.2.     Privacy issues associated with the use of AI Growth potential analysis

3.11.    Growth potential analysis

3.12.     Porter's analysis

3.13.     PESTEL analysis

Browse complete Table of Contents (ToC) of this research report @ https://www.gminsights.com/toc/detail/artificial-intelligence-ai-retail-market

About Author

Ronak Bora

Ronak Bora

A graduate in Electronics Engineering, Ronak writes for fractovia and carries a rich experience in digital marketing, exploring how the online world works from a technical and marketing perspective. His other areas of interest include reading, music, and sport. [email protected] | https://twitter.com/RonakBora26

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