Advanced Shopping Technology Market Insight
Advanced shopping technology allows streamlining the customer shopping experience by providing customers personalization and convenience services during shopping. With technological advances, online shopping and buying through smart devices are getting adopted increasingly. Consumer behavior is driven by technological change and based on increased expectations of convenience. For instance,Amazon Go stores allowcustomers to enter the store, pick-up items, and leave without queuing or checking out, while payment is automatically made through the Amazon Go app. This technology uses a combination of computer vision, deep learning, and sensor fusion technology to automate the payment and checkout process.
Impact of COVID-19 on Advanced Shopping Technology Market:
- Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost 180+ countries around the globe with the WHO declaring it a public health emergency. The global impacts of the disease are already starting to be felt, and expected to significantly affect the advanced shopping technology market in 2020.
- Governments have imposed lockdowns to contain spread of the virus, which have enforced people to sit at home. Some applications have seen rise in the lockdown and corona virus pandemic such as digital transactions, online shopping apps, and e-commerce apps. Increased use of such apps is expected to propel the market growth.
Overall, the present scenario is expected to have a positive impact on e-commerce and boost the market growth.
Top impacting factors: market scenario analysis, trends, drivers, and impact analysis
The increasing adoption of advance technology in retail allowscustomers to opt for self-checkout machines which isconvenient, simple, and consumes less time, driving the growth of advanced shopping technology market. Also, to save time customers wants to scan items themselves rather than interacting with cashiers thus, advanced shopping technology market is growing. Increasing competition form online shopping is encouraging brick & mortar style retail stores to adopt new technology to provide seamless experience to customers. However, high cost of implementation involved with integrating technology to traditional stores is restraining the growth of advanced shopping technology market. Contrarily, organization are adopting latest technologies such as big data, data analytics, artificial intelligence, machine learning, internet of things (IoT), and cloud is expected to drive the growth of advanced shopping technology market. Also, increasing proliferation and technological advancements in the domain of retail is increasing spending capacity of consumer which is expected to propel the growth of advanced shopping technology market in forecasted years.
Machine learning, Bluetooth low energy, and radio-frequency identification (RFID) in retail stores
Retail businesses are observing to usage big data and demand forecasting powered by machine learning to optimize supplier and customer relationship management, manufacturing and logistics processes, and running smart marketing campaigns. Compared to traditional forecasting methods, machine learning approaches are more flexible implement in the stores. Technological advancements feature offers retail machine learning based demand forecasting services. In addition, retail store are developing advance shopping technology which is drivenby advancement in radio-frequency identification (RFID) and Bluetooth low energy, deploying low-power consumption solutions through stores. For instance, fashion AI, a technology built by Alibaba Group, generates personalized mix-and-match apparel recommendations for shoppers as they move around stores. They can then rapidly find items that fit their sensitivities. Also, natural language processingdriven chatbots arereceiving smarter day-by-day and deliveringpersonalized practices in consumer service, offering recommendations and deals, providing easy navigation, and tracking orders.
Artificial intelligence and robots in retail stores
Robots, in combination with artificial intelligence, are expected to help shops cut costs and progress store processes throughcleaning, transforming goods from shelves, following, and breakdown of shelf inventory, lastmile delivery. For instance, autonomous last-mile delivery robots, like Amazon Scout, will deliver products directly to a customer’s door. Shelf auditing and Aerial drone’s robots, driven by AI-powered computer vision, scan and sense shelves, providing autonomous observing for inventory management and assessing of goods within the store.Customer service in-store robots, like Pepper and LoweBot , offer personalized responses to queries, help to discovery goods, navigate clients within the store, and even collectinformation for a better understanding of consumers’ preferences. The machine learning-powered robots, like SmartSight, offers record issues and report about their position and nature to a human.Many stores likely usage robots in the future for the progression of in-store operations, a load concession of store employees, and gathering data about customers, growing the demand of advanced shopping technology market.
Key benefits of the report:
- This study presents the analytical depiction of the global advanced shopping technologyindustry along with the current trends and future estimations to determine the imminent investment pockets.
- The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the global advanced shopping technologymarket share.
- The current market is quantitatively analyzed to highlight the market growth scenario.
- Porter’s five forces analysis illustrates the potency of buyers & suppliers in the market.
- The report provides a detailed global market analysis depending on competitive intensity and how the competition will take shape in coming years.
Advanced Shopping Technology Market Report Highlights
| Aspects | Details |
 By Technology |
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 By Application |
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 By Region |
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 Key Market Players | Walmart, Oracle, Alibaba, Amazon, Microsoft, Google, Epicor, IBM, Apple, SAP |
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