Using artificial intelligence to measure technological and retail industry trends

The in-depth integration of AI technology and retail becomes an irresistible trend of the new retail industry transformation.
Using artificial intelligence to measure technological and retail industry trends

The retail industry has been pushed by a wave of business intelligence (BI) over the last decade and progressively it has stepped into the field of Internet commerce dominated by big data. 

With the constant need of peoples’ desire of personalized consumption, the in-depth integration of artificial intelligence (AI) technology and retail becomes an irresistible trend of the new retail industry transformation.

AmazonGo has opened to the public, eradicating queues or long lines and become a sensation to the industry. Amazon describes it as "a new kind of store with no checkout required." 

However, some people believe that the current AI in retail industry is just a literal replacement of BI (from B to A). So what is the real AI that is needed in the retail industry?

On January 26, the second session of the monthly salon event discussing consumption upgrade was held by, art and technology consulting platform Top Tier, its affiliated Consumption Upgrade Research Institute and a new retail consulting company More Than Retail. 

Experts from fields of real estate, traditional merchandise business, science and technology, cultural consumption, market research, self-brand probed into technological innovation and in-depth integration.

Using artificial intelligence to measure technological and retail industry trends

Through the enforcement of its application usage, Fresh Hema has collects accurate profile of its customers.

AI and new retailing, what would the influence be of the combination of these two trendy words on a future shopping experience? Hong Kai, vice CEO of Nielsen China, stated that in the classification of traditional retailing markets, it is impossible to conduct in-depth analysis on consumption capability through consumers’ age, occupation, body size, personality, education, personal preference, working performance and income.

With the constant increase of cost of attracting customers online, an approach to combine online and offline patterns to meet the needs and preference of consumers, has been applied to the construction of the new membership system. For instance, Alibaba's flagship fresh food store Fresh Hema, a new retail pattern which combined online and offline shopping, and is supported by big data, showed that its area effectiveness is three to five times as much as one of the traditional stores. 

Hong analyzed that through the enforcement of application usage, Fresh Hema has collected an accurate profile of its customers. At the same time, it tracks their shopping habits and pushes precise marketing notifications. With importance laid on high-end catering of fresh food in order to meet the needs of upgraded consumption, Fresh Hema accelerates the commodity circulation both online and offline through suspension chain, digital price tag and intelligent allocation. 

Through all of those methods and strategies, a stable, recognizable, notable and interactive membership has been built up. It requires deep learning and in-depth integration of AI to apply the trend of big data to end-to-end solutions. 

Yin Xiangzhi, chief scientist of Deepbelief, honored lecturer of Microsoft and deputy secretary general of Taiwan Big Data Alliance, said that from the perspective of technology, new retailing is an integration and reconstruction of a relationship between consumers, commodities, stores and scenarios with the application of new technology and data.

No entirely reliable system of consumer identification and tracking analysis has been built up offline. As for traditional merchandise business, inadequate usage of technology would cause more severe problems like less gross margin, area effectiveness, theft, damage and unsatisfying consumer experience.

Deep learning of machine vision, which is applied by AmazonGo, can solve the problem. Its core is to establish the relationship between human vision and a machine-readable pixel. The help of deep learning enables more methods and approaches to understand consumers. However, for traditional merchandise business, there is still a long way to go.

Qiu Yuwei, representative of ultra large retail enterprises and general manager of Bailian Group's Omnichannel Digital Business, said: “Bailian Group is a huge commercial system so it takes time to realize its business transformation, which has been conducted partially within the group these years. But through the member data of Bailian membership points-collecting system, we found out that when consumers use their offline-collected-points online, it takes 52 percent of the whole consumption, implying that customers have been getting used to the new combined way of online-offline consumption. 

“If traditional offline stores want to develop new business through applications, instant chatting systems are worth a try. We suggest a legit connection of staff and consumers, which in the long run would increase the marketing transformation rate and orders.

Using artificial intelligence to measure technological and retail industry trends
Ti Gong

Deep learning of machine vision enables more methods and approaches to understand consumers.

“In IOT planning, it is very important to let consumers have more access and enrich approaches of collecting information, which lays the foundations to recognize and analyze information. At the same time, it helps to adjust the included AI analysis results for refining customers’ consumption proportion."

Bai Weiwei, founder and CEO of self-brand MR. COMPANY, noted that currently intelligent retailing plays a crucial part in offline store operations. The application of technology can help reduce cost and refine staff performance and it can even lower the requirement of recruitment.

Bai said: “The procedure of a good retailing approach is that through the in-depth investigation of consumers’ lifestyle, by intelligent systems and selected elements of consumers, business, commodities, scenarios and environments, a new retailing experience can be created. All those five elements are connected through data. In the future, to refine business, there is a chance to rely on intelligent platforms like Ali digital bank.”

Now, consumers pay more attention to the brand. An element of consumption upgrading is to constantly refine brand culture, commodity quality, aesthetic details, personal customizing, connection construction and end-to-end experience to build up a more effective business management. AI can accelerate the realization of all these elements.

Terry Tian, a special expert of the Consumption Upgrade Research Institute of Top Tier and CMO of More Than Retail, hosted the salon event. In the interactive session, he swapped views with the guests and audiences on topics about new retailing data application, brand culture building, automated stores and AI technology improvement.

The monthly salon will be a systematic seminar probing into consumption upgrade. Through a series of activities it will constantly integrate scientific and technological innovation, and cultural and art content, with traditional brands and space transformation. It will continuously discuss and report the achievement of fields like upgrading experience, multi-scenario interaction and big data dominance.

Using artificial intelligence to measure technological and retail industry trends
Ti Gong

Qiu Yuwei, general manager of Bailian Group's Omnichannel Digital Business, shares his views with guests during the event at East Gallery, an art space of

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