Smart Retail Market by application (Visual Marketing, Smart Label, Smart Payment System, Intelligent System (Intelligent Vending Machine, Smart Cart, AR/VR, Interactive Kiosk), Robotics, Analytics), and Geography - Global Forecast to 2023
“Smart retail market to grow at CAGR of 24.12% from 2018 to 2023”
The smart retail market is projected to reach USD 38.51 billion by 2023 from USD 13.07 billion in 2018, at a CAGR of 24.12%. Growth in this market can largely be attributed to factors such as the increasing adoption of smartphones, growing need for better customer service and shopping experience, decreasing cost of electronic components, and emerging new technologies create new revenues for retailers. However, data security and privacy concerns with new advanced technologies are expected to restrict the growth of the said market to a certain extent.
“Market for direct-view fine-pixel LED in digital signage to register highest growth during forecast period”
The smart retail market for direct-view fine-pixel led in digital signage is expected to grow at fastest CAGR. The increasing innovation in direct-view fine-pixel LED displays and their technological advantages are driving the growth of the large format display (LFD) market. The market is also driven by factors such as growing availability of highly bright HD LFDs offering better stability, flexibility, sustainability, and environment resistance; rising demand for brighter and power-efficient LFDs in several applications; and increasing applications for digital signage.
“Market for machine learning in artificial intelligence for smart retail technology to hold largest share during forecast period”
The market for machine learning technology in artificial intelligent is expected to hold the largest a market during the forecast period. Machine learning enables systems to automatically improve their performance with experience. This aims to develop a computer program/algorithm that can access data and use it to train itself with no human intervention. Machine learning is expected to account for the largest market share throughout the forecast period. This is attributed to the enormous availability of data, also called big data, and increasing adoption of machine learning by enterprises and federal agencies to gain useful insights.
“North America to grow at highest CAGR from 2018 to 2023”
North America is notable for its high adoption of smart retail. The retail product manufacturers’ focus on manufacturing precise, faster, and qualitative automated equipment and products. Other than extensive globalization, the entry of global players in this region is among the major reasons for the growth of the smart retail market in North America. The market in North America is also characterized by high infrastructure costs and strict policies and regulatory frameworks. Companies such as IBM (US), Google (US), Microsoft (US), NVIDIA (US), Intel (US), and Amazon (US) are complementing to the growth of the market in this region.
Breakdown of primary participants’ profile:
By Company Type: Tier 1 = 25%, Tier 2 = 30%, and Tier 3 = 45%
By Designation: C-Level Executives = 45%, Directors = 35%, and Others = 20%
By Region: North America = 40%, Europe = 30%, APAC = 25%, and RoW = 5%
Key players operating in this market are Intel (US), IBM (US), NVIDIA (US), Samsung Electronics (South Korea), Microsoft (US), Goggle (US), PTC (US), Amazon (US), Cisco System, (US), and NXP Semiconductors (Netherlands).
Various market segments have been covered in this report. These include application and geography. It also gives a detailed view of the market across 4 main regions: North America, Europe, APAC, and RoW.
Reasons to Buy the Report:
This report includes statistics pertaining to the smart retail market in terms of application and region, along with their respective market sizes.
Major drivers, restraints, opportunities, and challenges for the smart retail market have been provided in detail in this report.
The report includes illustrative segmentation, analysis, and forecast for the smart retail market based on its segments and subsegments.
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