Ai Ai is revolutionizing Omni channel marketing

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Ai Ai is completely changing Omni channel marketing, overturning multiple industries

ai Ai Ai is completely changing Omni channel marketing, overturning multiple industries

21:48 source: Changsha Niuer education/

original title: Ai Ai is completely changing Omni channel marketing, overturning multiple industries

scenario 1: in 2020, more than 30% of CIOs will take AI and machine learning as one of the top five investment priorities

scenario 2:40% of the digital transformation projects will be supported by cognitive computing and artificial intelligence, providing key and timely insight into the new mode of operation and cash realization

scenario 3: Spark enables enterprises to discover secrets in 10billion pieces of data

five years ago, I may have vaguely noticed that artificial intelligence will develop rapidly. Now, the era of automation through AI, machine learning and robot work has completely arrived

AI is revolutionizing Omni channel marketing

all great Omni channel strategies are based on accurate customer roles, insight into how they change, and how supply chain and it need to scale and change. Artificial intelligence and machine learning are bringing about an all-round change in these three core dimensions with unprecedented insight and contextual intelligence

here are 10 ways for AI and machine learning to innovate the omni channel strategy, starting with the role of customers, their expectations, and how customer care, it infrastructure and supply chain need to maintain responsive growth

1. AI and machine learning enable brands, retailers and manufacturers to define customer roles, their purchase preferences and journeys more accurately. Today, leading Omni channel retailers are successfully using artificial intelligence and machine learning to personalize the customer experience to the role level. They combine brand, activity and product preferences, location data, content browsing, transaction history, and most importantly, channel and communication preferences to create precise roles for each key customer group

2. Now, through artificial intelligence and machine learning, considering brand and channel preferences, previous purchase history and price sensitivity, price optimization can be achieved through roles. Brands, retailers and manufacturers said that with the rapid development of artificial intelligence and machine learning algorithms, price optimization and management applications based on cloud computing are easier to use than ever before, and this part of consumer demand shows rigidity and stronger functions. Easier to use and more powerful applications, as well as the need to better manage and optimize Omni channel pricing, are driving rapid innovation in this field

3. Taking advantage of insights gained from AI and machine learning, Omni channel leaders are redesigning their IT infrastructure and integration to expand the customer experience. The success of Omni channel requires it infrastructure, which can scale rapidly to respond to changes in customer preferences while providing scalable scale. Every area of the supply chain of brands, retailers or manufacturers, from supplier induction, quality management and strategic procurement to freight yard management, dock scheduling, manufacturing and implementation, needs to be coordinated around customers. Among them, the leading C3 solution provides a courtyard management system (YMS) and dock scheduling system based on Web, which can integrate ERP, supply chain management (SCM), warehouse management system (WMS) and many other systems through API

4. Omni channel leaders are relying on artificial intelligence and machine learning to digitize their supply chain, achieve punctual performance, and drive faster revenue growth. To make any Omni channel strategy successful, the supply chain needs to be designed to perform well in terms of market launch time and customer launch time. 54% of retailers adopting Omni channel strategy said that the main goal of their supply chain digitalization is to provide a better customer experience. 45% said their main goal was to digitize the supply chain by adding AI and machine learning driven intelligence

5. Artificial intelligence and machine learning algorithms make it possible to create propensity models based on roles, which are invaluable in predicting which customers will take action on bundling or pricing proposals. By definition, propensity models rely on predictive analysis, including machine learning, to predict the likelihood of specific customers taking action on bundle sales or pricing proposals, e-mail campaigns, or other calls to action that lead to purchases, upward sales, or cross selling. Propensity model has been proved to be very effective in increasing customer retention rate and reducing churn rate. Nowadays, every enterprise that is outstanding in the omni channel field relies on the propensity model to better predict how customers' preferences and past behaviors will lead to future purchases

6. Combining machine learning based pattern matching with product based recommendation engine, mobile based applications are being developed in which shoppers can try on clothes they are interested in. Machine learning is good at pattern recognition, and artificial intelligence is very suitable for creating recommendation engines. Together, these engines have spawned a new generation of shopping applications, and consumers can try on almost any dress. This application can understand what shoppers like best, and can also evaluate the image quality in real time, and then recommend that a variety of crystalline phases can also be purchased in 2D on the Internet or in stores

7. 56% of brands and retailers said that AI and machine learning enhanced order tracking and traceability are critical to providing a good customer experience. Today, through the use of artificial intelligence and machine learning technology, through the order tracking of each channel, combined with the prediction of distribution and shortage, operational risk is being reduced. AI driven tracking and tracking is very valuable in finding inefficiencies in the process, which will reduce the time for market-oriented and customer-oriented

8. Gartner predicts that by 2025, when bulldozers, hookers and trucks shuttle back and forth, the operational efficiency of customer service institutions that embed AI into the customer engagement center platform will be improved by 25%, which will completely change the process of customer service. Due to the lack of real-time contextual data and insight, customer service is often the place where Omni channel strategies fail. There are a large number of use cases in customer service. Artificial intelligence and machine learning can improve the overall full channel performance. Amazon has taken the lead in using artificial intelligence and machine learning technology to determine when a given customer role needs to talk to a real-time agent. Similar strategies can also be created to improve the performance of intelligent agents, virtual personal assistants, chat robots, and natural language (NLP). There are also opportunities to improve knowledge management, content discovery, and field service routing and support

9. AI and machine learning can improve marketing and sales efficiency by tracking purchase decisions and understanding why specific roles buy and others don't. Marketing has been analysis driven. With the rapid development of artificial intelligence and machine learning, the market will be able to distinguish the reasons and locations for the success or failure of their Omni channel strategies for the first time. By using machine learning to determine the list of further customers and potential customers, and using the relevant data on the web, the prediction model including machine learning can better predict the ideal customer profile about Bayer materials technology. The prediction score of each Omni channel sales director can better predict potential new sales, and help sales prioritize time, sales efforts and sales strategies

10. Predictive content analysis, supported by artificial intelligence and machine learning, is improving sales proximity by predicting which content will guide customers to buy. By using machine learning to analyze the behavior of previous potential customers and buyers in the way of personas, we can gain insight into what content needs to be personalized and when it needs to be sold. Predictive content analysis has proved to be very effective in B2B sales scenarios, and is expanding to consumer products

AI subverts many industries

the birth of new industries is bound to pour out many new jobs, and many new jobs such as AI programmers, big data analysts, e-sports, etc. are gradually maturing. According to the 2018 China AI development report released by the China Science and Technology Policy Research Center of Tsinghua University, as of June 2018, the number of Chinese AI enterprises has reached 1011. The data predicts that by 2020, the scale of China's AI core industry will exceed 150billion yuan, driving the scale of related industries to exceed 1trillion yuan


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