Artificial Intelligence Predicts Manufacturer Behaviors PDF Download

Author: Johnny Ch LOK
Publisher:
ISBN: 9781793052858
Category :
Languages : en
Pages : 555

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Book Description
5.(AI) and machine learning technologies make it possible to capture, process, and inter data on a massive scale effectively , then any human being could ever do. For example, Criteo's creative technology " Kinetic design" can apply insights from 1.2 billion monthly impressions to select and optimize individual branded advertisements components according to each shopper's preference and intent. This ensures more personalization and visually inspiring on brand ads. resulting in up to 12% more sales for (AI) technology advertiser clients.Moreover, advertisers can now engage and inspire shoppers on a more personal level, rendering custom ads. it real-time for every impression. So, designer continues to learn from each design's success to makeads. more and more effective over time. Furthermore, brands are increasingly using paid search on retail sites to draw attention to their products on the crowded online shelf, e.g. Google shopping is a key growth area's more users are engaging with shopping ads. and across the globe. Google shopping has become essential to retailers' marketing strategies, but is a difficult channel to apply its tool to be promoted effectively . Thus, future (AI) and machine -learning technologies can dramatically improve digital commerce performance application to apply (AI) and machine learning to digital consumer. So, future (AI) technology can be applied to digital commerce aspect, it will fall into the categories of pattern recognition, classification, prediction and consumer behavior.In conclusion, the benefits of using (AI) in digital commerce include: improved efficiency in discovering the relationships between datasets over traditional methods, which require complex modeling and coding, improved accuracy for clearly defined processes that involve a lot of manual processing, ability to deal with a large emotion of data with many attributes, for example: customer behavior data, multichannel and multi-device data , complex product data and fraud detection, more accurate analysis, such as customer segmentation sentiment, analysis and personalization frequent algorithum refreshes, such as several times a day, to capture the changes in customer and market behavior. Finally, however, a lot of types predictive consumption behavior around (AI), in particulars that driven by vendors claiming their solutions are (AI) , ready and can deliver dramatic improvements over existing technologies. Application leaders for digital commerce can be misled into believing that (AI) can solve all their problems, which is not true for n in-depth discussion of the (AI) consumers and market behavioral predictive tool and machine -learning technologies bot. Thus, (AI) prediction consumer behavioral technology can give beneficial quantitative analysis for forecasting in business and market especially in consumer behavior and in the consumer decision-making process ( consumer choice model) more effectively and efficiently.ChaptersevenIs Artificial Intelligent the most effective andaccurate consumer behavioral tool?Is (AI) the best and the most effective and accurate consumer behavioral prediction tool to compare other kinds of consumer behavioral prediction tools? Nowadays, retailing competitions are serious businessmen often find different kinds of methods to attempt to predict consumer changes. The consumer behavioral predictive methods can include as these below methods, instead of (AI) big data gathering tool.

Author: Johnny Ch LOK
Publisher:
ISBN:
Category :
Languages : en
Pages : 79

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Book Description
In consumer view point, can they apply (AI) learning machine to predict manufacturers' behavioral performance to judge whether whose products are value to buy. Nowadays, (AI) and big data are reshaping the risk in consumer privacy. For example, consumers want to hide their willingness to pay just as firms want to hide their real marginal cost, and buyers have less favorable information, say a low credit shore, prefer to withhold it just as sellers want to conceal poor product quality. So, it implies that it is possible (AI) learning machine can help customers to gather any manufacturers' past sale performance, e.g. how many complaints or appreciation from clients, product quality etc. sale data to let consumers to make judgement whether it is value to buy to compare other competitors. So, it has risk to the poor product quality of manufacturers. Otherwise, it has benefits to the good product quality of manufacturers. It also implies all manufacturers' privacy is not protected or secret when (AI) learning machine is popular to be used to predict manufacturers' behaviors by consumers.Information economists suggest that both buyers and sells have an incentive to hide or reveal private information, and these incentives are crucial for market efficiency. Data technology that reveals consumers type could facilitate a better match between product and consumer type, and data technology that helps buyers to assess product quality could encourage high quality production. Thus, (AI) big data technology can also assist consumers to gather different manufacturers' data to compare what their advantages and disadvantages of their products are. Then, consumers can make comparison to choose which brand of product is the suitable to whom to buy in these more choice consumption market. (AI) learning machine will gather similar brand their products' data to analyze to make conclusion to let consumers know or feel to make final judge to find what advantages or disadvantages of these sample brands of similar products' comparison from internet. On the other hand, it means that manufacturers can gather consumers' past purchase behaviors or purchase experience from (AI) big data gathering method to record and analyze to give opinions to let manufacturers to know what reasons or factors influence consumers choose not to buy their products from internet.

Author: Johnny Ch Lok
Publisher:
ISBN:
Category :
Languages : en
Pages : 168

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Book Description
Challenge to using (AI) neural networks to predict customer behavior from big data gather tool(AI) big data gather tool will encounter the challenge: How can predict customer behavior be represented as sequential data describing the interactions of the customer with a company or an (AI) data gather system through the time, e.g. these interactions are items that the customer purchase or views ? So, every customer data gather, (AI) needs to spend time to analyze how and why to cause whose consumption behavioral choice. It is too difficult matter or judgement for (AI) learning. So, (AI) needs to spend time to learn how to analyze every customer's shopping behavior or actin in order to gather all different consumers' past shopping action information in order to help business owners to predict future its potential customer shopping behavior how to change more clear and accurate prediction. (AI) big data gather tool needs to learn to know that how to judge every customer interaction likes purchases over time can be represented with sequential data. Sequential data has the main property that the order of the information is important. Many (AI) machine learning models are not suited for sequential data, as they consider each input sample independent from previous ones. Therefore, at the end of the sequence, (AI) big data gather learn machines need to keep in their internal state of every customer purchase data, kind of product or service, price, whole year consumption times form all previous inputs, making them suitable for this type of data.However, consumer behavior can be represented as sequential data describing the interactions through the time. Examples of these interactions are the items that the user purchases or views. Therefore, the history of interactions can be modeled as sequential data, which has the particular trial that an incorporate a temporal aspect. For example, if a user buys a new mobile phone, who might purchase accessories for this mobile phone in the near future or it the user buys a electronic book or paper book, he might be interested in books by the same author. Therefore, to make accurate predictions is important to model this temporal aspect correctly. To solve this predictive challenge of consumers to buy the product. One count the number of purchased products of a particular category in the last N days, or the number of days since the last purchase.

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781790253449
Category :
Languages : en
Pages : 62

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Book Description
(AI) big data gathering consumer behavior prediction method can give these benefits to manufacturers and consumers both, such as: New concerns arise because (AI) technological advance which have enables reducing cost of collecting, storing, processing and using data in mass quantities extend information beyond a single transaction. These advances are often summarized by the big data, it means charge volume of transaction-level data that could identify individual consumers by itself or in combination with the datasets.The popular (AI) takes big data as in input in order to understand, predict and influence consumer behavior. Modern (AI) is used by legitimate companies, could improve management efficiency motivate innovations and better match demand and supply. But (AI) in the wrong hand, also allows the mass production of fraud and deception. Since, data can be stored, traded and used long after the transaction. Future data use is likely to grow with data processing technology, such as (AI) big data gathering consumer and manufacturer behavioral prediction method from internet channel. Thus, future (AI) big data learning machine can also help consumers to choose the best brand of manufacturer's products among different brands of manufacturers products choice to compare their past sale performance from internet. They can apply (AI) big data statistic method to gather all different manufacturers' similar products past sale data to compare their advantages and disadvantages to make the best decision to choose to buy which brand of product is the most suitable to them to buy to use. It seems (AI) big data can also help consumers to predict any manufacturers' manufacturing behaviors or manufacturing performance whether they are improving their product quality or are deteriorating their product quality. Thus, (AI) big data tool is also important to help customers to predict future the different brands of manufacturer performance will have improvement in possible.

Author: Johnny Ch LOK
Publisher:
ISBN: 9781729251218
Category :
Languages : en
Pages : 573

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Book Description
PrepareThis book aims to explain why and how future artificial intelligent technology ( big data gathering method) can be applied to assit businesses to predict why and when and how consumer behavior changes. I shall explain why traditional psychological and statistic and marketing methods are applied to predict consumer behaviors, human's judgement and analytical effort will be worse to compare AI machine's judgement and analytical effort. Also, I shall indicate different business organizations why they apply AI big data gathering method to help them to design any questionnaires ( surveys) questions which will be more valid and useful to conclude human's questionnaires ( surveys) design questions method.This book has these two research questions need to be answered?(1)Can apply (AI) learning machine predict consumer behaviors?(2)Can (AI) learning machine replace human marketing research method, e.g. survey or human psychological and micro and macro economic methods to predict consumer behaviors more accurate?Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans.Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately.In my this book first part, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book.

Author: Johnny C. H. Lok
Publisher: Independently Published
ISBN: 9781723774508
Category :
Languages : en
Pages : 572

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Book Description
Prepare This book has these two research questions need to be answered? (1) Can apply (AI) learning machine predict consumer behaviors? (2) Can (AI) learning machine replace human marketing research method, e.g. survey or human psychological and micro and macro economic methods to predict consumer behaviors more accurate? Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans. Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately. In my this book first part, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book. In my this book second part, I shall explain why and how human can possible apply (AI) tool to predict consumer individual emotion. I shall indicate case studies to explain how consumer individual better or worse emotion how to influence whose consumption behavior in different situation. Finally, I shall indicate evidences to conclude how and why (AI) tool that can be used to predict consumer individual emotion and it will have direct relationship to influence consumption behavior, as well as how (AI) tool can assist businessmen to judge whether what reasons case the customer does not choose to buy its product, it is possible because the product high price factor, poor product quality or poor staff service performance or attitude etc. different factors to influence the consumer decides to choose to buy the other product consequently, when the (AI) tool can confirm consumer has good or bad emotion to judge what factors are the causes his decision making at the moment.

Author: Johnny Ch Lok
Publisher:
ISBN: 9781661975272
Category :
Languages : en
Pages : 184

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Book Description
Information economists suggest that both buyers and sells have an incentive to hide or reveal private information, and these incentives are crucial for market efficiency. Data technology that reveals consumers type could facilitate a better match between product and consumer type, and data technology that helps buyers to assess product quality could encourage high quality production. Thus, (AI) big data technology can also assist consumers to gather different manufacturers' data to compare what their advantages and disadvantages of their products are. Then, consumers can make comparison to choose which brand of product is the suitable to whom to buy in these more choice consumption market. (AI) learning machine will gather similar brand their products' data to analyze to make conclusion to let consumers know or feel to make final judge to find what advantages or disadvantages of these sample brands of similar products' comparison from internet. On the other hand, it means that manufacturers can gather consumers' past purchase behaviors or purchase experience from (AI) big data gathering method to record and analyze to give opinions to let manufacturers to know what reasons or factors influence consumers choose not to buy their products from internet.(AI) big data gathering consumer behavior prediction method can give these benefits to manufacturers and consumers both, such as: New concerns arise because (AI) technological advance which have enables reducing cost of collecting, storing, processing and using data in mass quantities extend information beyond a single transaction. These advances are often summarized by the big data, it means charge volume of transaction-level data that could identify individual consumers by itself or in combination with the datasets.The popular (AI) takes big data as in input in order to understand, predict and influence consumer behavior. Modern (AI) is used by legitimate companies, could improve management efficiency motivate innovations and better match demand and supply. But (AI) in the wrong hand, also allows the mass production of fraud and deception. Since, data can be stored, traded and used long after the transaction. Future data use is likely to grow with data processing technology, such as (AI) big data gathering consumer and manufacturer behavioral prediction method from internet channel. Thus, future (AI) big data learning machine can also help consumers to choose the best brand of manufacturer's products among different brands of manufacturers products choice to compare their past sale performance from internet. They can apply (AI) big data statistic method to gather all different manufacturers' similar products past sale data to compare their advantages and disadvantages to make the best decision to choose to buy which brand of product is the most suitable to them to buy to use. It seems (AI) big data can also help consumers to predict any manufacturers' manufacturing behaviors or manufacturing performance whether they are improving their product quality or are deteriorating their product quality. Thus, (AI) big data tool is also important to help customers to predict future the different brands of manufacturer performance will have improvement in possible.

Author: Johnny Ch Lok
Publisher: Createspace Independent Publishing Platform
ISBN: 9781721070879
Category :
Languages : en
Pages : 64

View

Book Description
Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans. Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately. In my this book, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book.

Author: Johnny Ch Lok
Publisher:
ISBN:
Category :
Languages : en
Pages : 66

View

Book Description
In the future, (AI) will bring their benefits to influence customers to build positive emotions to any retailers in these aspects as below:1.Future (AI) big data gather tool will be an area of compute science that deals with giving machines, the ability to seem like they have human intelligence. In short, it is the power of a machine to copy intelligent human behavior. For example, machine learning algorithms are being integrated into analytics and customer relationship management platforms to uncover information on how to better serve customers, chat bots have been incorporated into websites to provide immediate service to customers.2.(AI) adoption continue to rise with chat bots taking the lead. Due to increasing ease of deployment, instant availability and improved quality, chat bots will become more and more common to manage customer service queries and to make intelligent purchase recommendations. Also, retailers can engage this kind of technology to answer continue questions and supplement customer support with chat-based shopping experience. So, (AI) and declines personalized, customized and localized experiences to customers. (AI) will be applied across the entire retail product and service cycle, firm manufacturing to post-sale customer service interactions. Hence, retailers can use (AI) to its fullest potential will be also to influence purchases in the moment and anticipate future purchases, guiding shoppers towards the right products in a regular and highly personalized manner.3.(AI) technology can rise the conscious customers. Customers are demanding an increased interest in the ethical practice of the brands they buy from. Todays, customers have a well-developed sense of what is solely intended to drive sales. This has lead to a rise in consumers ho make values based judgements about what to buy and where to shop. These consumers believe their purchase habits have an impact on the world. To win customers, retailers need have good conscious to predict consumers' desire. Future, (AI) data gather technology will be a good consumer behavior predictive tool to predict about for years will now become customer expectations and will have drastically changed the path to purchase. So, (AI) data gather tool is the predictive consumer expectations tool on every interaction, they have these brands.4.Future (AI) can be impacted to influence consumer behaviors by its potential to free up time, enhance, quality, and enhance personalization. The industries include: Healthcare industry can apply (AI) to support diagnosis by detecting variations in patient data, early identification of potential pandemics, imaging diagnostics; automat industry can apply (AI) to autonomous fleets to ride sharing, semi-autonomous features, such as driver assist, engine monitoring and predictive, autonomous maintenance; financial service industry can apply (AI) to design the suitable personalized financial planning, fraud detection and anti-money laundering and automation of customer operation; transportation and logistics industry can apply (AI) to autonomous trucking and delivery, traffic control and reduced congestion and enhanced security; technology, media and telecommunications industry can apply (AI) to search media, and recommendation, customized content creation and personalized marketing and advertising to attract retailers to promote; retail and consumer industry can apply (AI) to design personalized production, anticipating customer demand, inventory and delivery management; energy industry can apply (AI) to read and record smart metering, more efficient grid operation and storage and predictive maintenance; manufacturing industry can apply (AI) to enhance monitoring and auto-correction of processes, supply chain and production optimization and on-demand production.

Author: Johnny Ch LOK
Publisher:
ISBN:
Category :
Languages : en
Pages : 79

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Book Description
Why can (AI) be applied to predict consumer behaviors?Artificial intelligence refers to complex in vehicle market, machine learning that posses the same characteristics of human intelligence and that have all our sense, all our reason and think just like human do. Besides, machine learning is the practice of using algorithms to collect and examine data, learn from it, and then make a determination or prediction about something in the world.The machine is " trained" using large amounts of data and algorithms that give it the ability to learn how to automatically perform a task with increasing accuracy. Otherwise, deep learning is primarily based on artificial neural networks inspired by our understanding of the biology of human's brains.Deep learning breaks down tasks in ways that enables machines to assist us with increasingly complex tasks, driverless cars, better preventive healthcare and more accurate product recommendation ( including vehicle recommendations). So, such as why (AI) technology can be applied to predict how vehicle consumer behavior changes to bring to judge whether vehicle consumer will like what kinds of vehicle styles next year. Then, vehicle manufacturers can gather overall vehicle consumer data to analyze and conclude the more accurate vehicle design direction for next year any new design vehicle manufacturing products.Thus, (AI) machine learning can help vehicle manufacturers to solve how to design any new vehicle products challenge. A vehicle is both one of the most important and carefully considered purchases the majority of people will ever make in their lifetime. It is also a purchase that tends to be fundamentally tied to a person's identify and view of themselves. As the same time, vehicle consumers changing lifestyles result in changing vehicle needs, e.g. the young sport car enthusiast matures into the family driver. Automotive dealers need to remember that vehicle customers and prospects are individual human beings with risk, complex and ever-changing lives factors, these factors will influence every vehicle consumer why who feels has vehicle purchase need, and how who choose to buy the first vehicle if who decided to buy the first vehicle.The (AI) technological customer behavioral prediction tool seems to be the best vehicle salespeople in the world are those that know every one of their vehicle customers. Their likes and dislikes which style of vehicle design, preferences and changing tastes to vehicle choices. The capacity of the human brain, however, limits us from achieving this type of vehicle sales and frequent turnover at vehicle dealerships often results in the further loss of vehicle salespeople along with their vehicle customer relationships and knowledge. In this competitive vehicle environment, machine learning enables platforms to assist the vehicle sales team by tracking the vehicle consumer behaviors of each vehicle customer, learning and memorizing their preferences and predicting their future vehicle purchase needs.Finally, I recommend that for a vehicle dealerships marketing platform to make their customer engagement efficient and fully-functional, I should be able to: applying (AI) tools to track every vehicle customer behavior across the web, connecting to a society of data sources, CRM, DMS, third-party, web vehicle brands, social email, click etc., aggregating and accurately cross-reference data from a variety of sources, leveraging this data to drive insights on a mass scale, as well as on an individualized basis, driving actions and automatically direct customer engagement via multiple channels based on where each customer is in their individual lifecycle.