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Artificial Intelligence Big Data Travelling

Artificial Intelligence Big Data Travelling
Author: Johnny Ch LOK
Publisher:
Total Pages: 107
Release: 2018-06-17
Genre:
ISBN: 9781983193255
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This book indicates whether human technological AI (big data gathering tool) which can be applied to predict when, how and why consumer behavior will change. Does it is science story or actual fact to be applied in our future business society. Parent can learn their children to make judgement whether our future society will be either assistance by AI technological development absolutely or AI is only science story product. This book has these two research questions need to be answered?(1) Can apply (AI) learning machine predict travelling consumer behavior?(2) Can (AI) big data gathering learning machine be replaced to human travelling marketing research method, e.g. survey or traveler psychological and travelling marketing research or travelling environment micro and macro economic human judgement of traveler consumption behavior prediction methods to predict travelling consumer behaviors more accurate? Nowadays, many airline firms or travelling agents hope to apply different methods to predict travelling consumer behaviors in order to know what will be future next month, even next year travelling market destination choice and travelling package design preferable choice activities and travelling consumers travelling packages or travelling destination taste changes to help them to choose to implement what kinds of travelling marketing strategies or what are travelling packages or airline ticket prices more reasonable or more accurate range price level to attract travelers choose to the airline or travel agent to buy paper or e- ticket or help them to arrange travel package more attractive. Hence, if the travel agent or airline can apply the most suitable travelling consumer behavioral prediction method to predict how and the reasons why future travelling consumers' choice will be changed to influence their frequent travelling destination or travelling package choice. It will have more beneficial intangible advantages to compare the non-predictive travelling consumer behavioral variable changes travel agents or airlines, e.g. what will be the hot travel entertainment destinations and tangible advantages, what are the most suitable airline and hotel reasonable price range level to attract many travelers to choose to find the airline or travel agent to help them to buy air ticket or they ought know how to design their arrange travel package which will be accepted more popular for next or next year travelling customer's hot needs .Otherwise, if they applied the inaccurate traveler consumer behavioral prediction market research methods, e.g. survey, telephone questionnaire to predict how their consumers' behavioral changes. It will waste their time and money to attempt to make wrong travelling hot destinations and travelling package design to make unattractive travelling market strategy to cause travelling customer number to be reduced. In my this book, I concentrate on explain why artificial intelligence (AI) big data gathering tool will be one kind of good traveler consumer behavioral prediction tool to be chose to apply to predict traveler consumer consumption behavior concerns when and why and how their travelling behavior will change. I shall indicate some cases examples to give reasonable evidences to analyze whether (AI) big data gathering tool will be one kind suitable tool to be applied to predict when and how and why travelling consumer behavioral changes. If (AI) big data can be one kind tool to attempt to be applied to predict when and how and why travelling consumer behavioral changes. Will it make more accurate to compare other kinds of methods to predict travelling consumer behaviors, e.g. survey, telephone questionnaire? Does it have weaknesses to be applied to predict travelling consumer behaviors, instead of strengths?

Learning Big Data Gathering to Predict Travel Industry Consumer Behavior

Learning Big Data Gathering to Predict Travel Industry Consumer Behavior
Author: Johnny Ch Lok
Publisher: Independently Published
Total Pages: 380
Release: 2018-10-08
Genre: Business & Economics
ISBN: 9781726860079
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Challenges of artificial intelligence, algorithms technology and machine learning impact to consumption marketThe challenges of artificial intelligence, algorithms technology and machine learning impact to consumption market are similar to travelling entertainment consumption market. Markets have played a key role in providing individuals and businesses with the opportunity to gain from trade. If (AI) big data gather tool can predict how to change potential customer behavior in success. The challenges to consumers will face that the overall market consumption model will be dominated by the businessmen only. So, it is not fair or reasonable to consumers, because (AI) big data gather tool has controlled or dominated all consumers' minds and it has predicted how and why every kind of product or service consumer shopping model or consumption behaviors how will change.It will bring this questions: How can market designers learn the characteristics necessary to set optimal, or at least better, reserve prices after they had gather all data to conclude the analytical results of their consumers behaviors how will change? How can market designers better learn the environments of their markets?

Artificial Intelligent Travelling Behavioral Predictive Tool

Artificial Intelligent Travelling Behavioral Predictive Tool
Author: Johnny Ch Lok
Publisher:
Total Pages: 372
Release: 2019-06-02
Genre:
ISBN: 9781071337233
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So, I believe that (AI) big data gather every conclusion or result will be different, due to consumer's price and quality demand will often change to every kind of product or service supply in retail industry. So, how to shape (AI) big data gathering's analytical conclusion or result more clear. I shall recommend organizations need to build great understanding of and a stronger connection to increasingly empowered consumers before they plan and implement how to apply (AI) big data gather tool to predict consumer behavior as below: Firstly, (AI) is empowered by technology, the consumer is redefining value. The traditional measures of cost, choice and convenience are still relevant, but not control and experience are also important. Globally, consumers have access to more than 2 billion different products choice by a wide range of traditional competitors and dynamic new entrants, all experimenting with new business models and methods of client engagement. As choice increases, loyalty becomes more difficult familiarity and the consumer becomes more empowered. Businesses will have no choice and constantly innovate and disrupt themselves by meeting new technologies of high standards and expectations of consumers. So, (AI) data gather tool will need to follow different target group of consumers' needs to follow their different kinds of product design or style choice preferable to gather data in order to conclude the different target groups of consumer behavior to give opinion more clear and accurate to let businessmen to understand more clear how its customers' behavioral choice trend in the future half month, even to two years period.Secondly, businessmen need to adopt changing technologies rapidly. Technology will be the key driver of this retail industry. Industry participants will only success if they have a clear prediction to focus on how to using technology to increase the value added to consumers. They must, however, do so will I realistic assessment of their costs and benefits. Hence, (AI) big data gather technological tools will need to design to help them to gather data efficiently by these ways, such as the internet of things ( IOT), artificial intelligence (AI) machine learning, augmented reality (AR)/virtual reality (VR), digital traceability. So, future (AI) big data gather tool are predicted to be most influential customer behavioral positive emotion changing tool for retail, due to their widespread applications, ability to drive efficiencies and impact on labor in order to impact consumer behavior changing effort from negative emotion to positive.Thirdly, (AI) big data gather tool is an advanced data science of consumer behavior predictive tool. Businesses will have to bring the journey from simply collecting consumer data to using it to scale and systematize enhanced decision making across the entire value chain. When focused on their business goals, industry players should not lose sight of the impact that future capabilities and transformative business models may have on society.However, (AI) big data gather tool will encounter these challenges when any business plans and implements to apply it to predict consumer behavior in retail industry. The challenges include that as below:1.The high cost and difficulty of implementing new technologies . The (AI) big data gather tool needs capital and capabilities to be designed to implement to be applied to different retail industry users. so, expensive barriers to innovation, an organization and the skillsets of its people to support a new design of (AI) big data gather tool, highly digital technology may be required.

Artificial Intelligence Predicts Marketing Behavior

Artificial Intelligence Predicts Marketing Behavior
Author: Johnny Ch Lok
Publisher:
Total Pages: 182
Release: 2020-12-22
Genre:
ISBN:
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How can apply (AI) to provide travelling businesses with better-informed decisions I shall explain how (AI) big data gathering technology can provide travelling businesses with better-informed decisions to drive top-line growth, deliver meaningful experience for travelling customers and smooth their path along the travelling consumer journey. The widely understood definition of (AI) involves the ability of machines or computers to learn human thinking, reasoning and decision-making abilities. So, such as (AI) learning machine system can attempt to learn travelling consumer's travel destination or travel package thinking, judgement of their reasons why they choose to go to the destination to travel or why they choose to buy the travel package and learn how and why they make their past travelling decisions from their past travel big data gathering.A Narrative science study in 2015 year identified that (AI) was being used primarily in voice recognition, machine learning virtual assistants and decision support. This study also highlighted the many branches of (AI) and that techniques and their definition are used interchangeably. It is possible that (AI) can be used to gather big data, then to analyze to help travel businesses to predict travelling consumer travel destination and travel package choice behaviors. For example, one of the most common techniques is traveler machine learning, where algorithms are used to perform tasks by learning from the airline or travel agent whose past all travelers' travelling destination choice and travel package choice historical data. However, during 2017 year, search engines will begin to find what additional factors can influence past traveler personal travelling destination and travelling package travelling behavioral data into prediction of future travelling customer behavioral results, such as the online traveler (user's) history of travelling data searches, such as anywhere are the most popular travelling locations or travelling destinations and previously captures conservations. Artificial intelligence will use this past travelling destinations and travelling package information to power predictive search results, e.g. predictive future travelling consumer's choice behavioral processing for where will be their preferable travelling destination choice and how to design travelling package to satisfy future travelling clients' needs.Predictive search will improve the quality of online travelling search results, and provide new insights into travelling consumers' travelling destination and package behavior and the moments which matter to them. Search will give recommendation into tailored how travelling consumer individual travelling destination choice in travelling decision making process. Several of the largest online platforms already use (AI) travelling machine learning to improve predictive travelling consumer behavioral search results. For example, Google's rank brain technology adds research by understanding the context in which the travelling consumer has entered it. Over time, rank brain will learn further from user behaviors Amazon's DSSTNE ( pronouned destiny) learns from shoppers' purchasing habits and consumption behavior to offer better product recommend actions, which Amazon can offer before a consumer has entered anything into the search bar. Such as (AI) big data can gather past online travelers' e-ticket purchase transactions to conclude that online traveler's travelling choice habits and online traveler consumption behavior to offer better travelling destinations and travelling package opinions to travel agents or airlines. However, this technology is not independent of human input. For example, Google engineers will periodically retain the rank brain system to improve the models it uses.

Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior

Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior
Author: Johnny Ch LOK
Publisher:
Total Pages: 379
Release: 2018-10-13
Genre:
ISBN: 9781728746418
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The challenges of (AI) big data gather shapingthe future of retail for consumer industriesThe future of retail for consumer industries' (AI) big data gather challenges are similar to future travelling industry's entertainment consumption challenges. Another challenge of (AI) big data gather is that how to shape the consumer behavior to let business owner to feel or know oe predict. It means that how it express it's conclusion or opinion for every consumer behavior after it had gather all big data in any data gather period, e.g. three months, half year or one year consumer shopping model data gather period.Because every kind of industry, consumers will continue to demand price and quality change , with a wide range of convenient fulfilment options among of different kinds of products or services supply. Overall, the (AI) big data gather procedure gives opinion concerns every time retail experience will become more exciting, simple and convenient, depending on the consumer's ever-changing needs. So, I believe that (AI) big data gather every conclusion or result will be different, due to consumer's price and quality demand will often change to every kind of product or service supply in retail industry. So, how to shape (AI) big data gathering's analytical conclusion or result more clear. I shall recommend organizations need to build great understanding of and a stronger connection to increasingly empowered consumers before they plan and implement how to apply (AI) big data gather tool to predict consumer behavior as below:Firstly, (AI) is empowered by technology, the consumer is redefining value. The traditional measures of cost, choice and convenience are still relevant, but not control and experience are also important. Globally, consumers have access to more than 2 billion different products choice by a wide range of traditional competitors and dynamic new entrants, all experimenting with new business models and methods of client engagement. As choice increases, loyalty becomes more difficult familiarity and the consumer becomes more empowered. Businesses will have no choice and constantly innovate and disrupt themselves by meeting new technologies of high standards and expectations of consumers. So, (AI) data gather tool will need to follow different target group of consumers' needs to follow their different kinds of product design or style choice preferable to gather data in order to conclude the different target groups of consumer behavior to give opinion more clear and accurate to let businessmen to understand more clear how its customers' behavioral choice trend in the future half month, even to two years period.Secondly, businessmen need to adopt changing technologies rapidly. Technology will be the key driver of this retail industry. Industry participants will only success if they have a clear prediction to focus on how to using technology to increase the value added to consumers. They must , however, do so will I realistic assessment of their costs and benefits. Hence, (AI) big data gather technological tools will need to design to help them to gather data efficiently by these ways, such as the internet of things ( IOT), artificial intelligence (AI) machine learning, augmented reality (AR)/virtual reality (VR), digital traceability. So, future (AI) big data gather tool are predicted to be most influential customer behavioral positive emotion changing tool for retail , due to their widespread applications , ability to drive efficiencies and impact on labor in order to impact consumer behavior changing effort from negative emotion to positive.

Artificial Intelligence Big Data Gathering Predicts Consumer Behavior

Artificial Intelligence Big Data Gathering Predicts Consumer Behavior
Author: Johnny Ch LOK
Publisher: Independently Published
Total Pages: 488
Release: 2018-09-19
Genre:
ISBN: 9781723836688
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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. Readers can understand why and how (AI) tool can be attempt to be applied to predict customer emotion and it can influence positive or negative consumption behavior to the product clearly in this part.

Artificial Intelligence Future Development

Artificial Intelligence Future Development
Author: Johnny Ch LOK
Publisher:
Total Pages: 368
Release: 2019-07-07
Genre:
ISBN: 9781079014471
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impacts that their holidays and travel have on climate changes to influence their travelling decision?When, it comes to understand individual traveler's behavioral change, wide range of conceptual theories have been developed, utilizing various social, psychological, subjective and objective variables in order to model travel consumption behavior. These theories of travel behavioral change operate at a number of different levels, including the individual level, the interpersonal level and community level. Whether pro-environmental behavior can be used to predict travel consumptionbehavior in a climate change. However, the question of what determines pro-environmental behavior in such a complex one that it can not be visualized through one single framework or diagram.Despite the potentially high risk scenario for the tourism industry and the global environment, the tourism and climate change ought have close relationship.However, (AI) big data tool can be applied to find what factors to influence the time of travelers' travelling choices. What are the important factors and variables which can limit tourism? e.g. money, time, family problem, extreme hot or cold weather change, air ticket price, journey attraction etc. variable factors.

Artificial Intelligence Predicts Consumer Behavioral Tool Business Journey

Artificial Intelligence Predicts Consumer Behavioral Tool Business Journey
Author: Johnny Ch Lok
Publisher: Independently Published
Total Pages: 62
Release: 2018-11-23
Genre:
ISBN: 9781790253449
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(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.

Artificial Intelligence Big Data Gathering How Impacts Consumer Behavior

Artificial Intelligence Big Data Gathering How Impacts Consumer Behavior
Author: Johnny Ch LOK
Publisher: Independently Published
Total Pages: 572
Release: 2018-09-21
Genre:
ISBN: 9781723901041
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How to apply online psychological advertising method to predict passenger behavioral consumption?Online advertising can give relevance information to represent the similarity between advertisement and queries. These existing online advertisement works mainly focused on interpreting advertisements clicks in term of what consumers seek. ( i.e. relevance information) and how consumers choose to watch TV or magazine or online advertisement etc. from different promotion media. ( historically to know the product is selling on the market through advertising information). However, few of manufacturers or sellers attempted to understand why consumers chose to watch the advertising from TV or magazine or internet etc. different media.Why can MTR can choose online advertisement to predict passenger behavior? Online Advertisement can be as a commercial search engine for manufacturers or sellers to gather data to concern how behavioral consumption is. The online advertisement's each observations motivate who to systemically model to test what each consumer individual psychological desire in order for a precise prediction on behavioral consumption after online advertisement promotion from internet media.Today, internet is one kind of effective psychological advertising promotion method. For example, an online advertisement system, sponsored search has been one of the most important business models for commercial web search engines. It generates most of the revenue of search engines by presenting to users sponsored search results, i.e. advertisements (ads), along with organic search results. To deliver the most interesting ads to the users, a sponsored search system consists of technical components, including query-to-ads matching, online click prediction for matched ads, online click probability and auction to determine the ranking, placement, and pricing of the remaining ads. To aim to attempt to predict behavioral consumption for any kinds of product sale from online advertisement media.In today's industry, generalized second price auction (GSP) is the most widely-used auction mechanism , in which the price that an advertiser has to pay depends on the predicted online click probability of the online buyers, whose own ads as well as the bid price and predicted online click probability of the ads ranked in the next position. The online sponsored search systems typically employ a machine learning model top predict the probability that an online user clicks an advertising from internet. However, in practical sponsored search system. There are many ads without adequate historical click through data, even after query levels. Then online ads can been click improved prediction accuracy to consumer individual behavioral consumption when each click is occurred to the seller individual website. For example, online ads, such as : free Nike coupons ad. It shows " Go-Get_couptons.com/Nike, Download and print Nike coupons ( 100% Free)" ; another Nike-sales prices ad. It shows www.calibex.com, clothing, latest fashions and styles on sale. Buy Nike Fast!" ; another Perfume.com official site ad. It shows "www.perfume.com, 10,000 + brand name perfumes and colognes-up to 80% off retail!" ; another Luxury English Perfume Ad. It shows " www.florislondon.com, shop online for luxury perfumes for men, women and the home". Above of these are example online ads. For two queries, "Nike" and "Perfume" , and two ads under the same query field similar relevance to the query.

Artificial Intelligent Consumer Behavioral Predictive Tool

Artificial Intelligent Consumer Behavioral Predictive Tool
Author: Johnny Ch LOK
Publisher:
Total Pages: 379
Release: 2018-10-20
Genre:
ISBN: 9781729014158
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PrepareI write this book aim to let readers to judge whether it is possible to predict future travel behaviour from past travel behaviour for travel agents benefits as well as big data gathering technology can be applied to predict travel consumption behavior if travel agents can gather any past travel consumer data to predict future travel consumption behavior from AI ( big data gathering tool). This book is suitable to any readers who have interest to predict any individal or family or friend groups of travel target's psychological mind to design the different suitable travel packages to satisfy their needs from big data gathering tool prediction method in possible.This book researchs how to apply big dta gathering tool to predict future travel consumer behavior from past travel consumer data. This book first part 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 in entertainment industry, e.g. cruise travel and vehicle leisure activities. If AI , big data gathering tool can be applied to predict such as leisure market consumption behavior, it is possible that future big data gathering tool can be used to gather past travel consumer behavioral data in order to conclude more accurate information to predict future travel behavioral need changes.This book has these two research questions need to be answered?(1)Can apply (AI) learning machine predict future travelling consumer behaviors from past travelling consumer behavioral data gathering?(2)Can (AI) learning machine replace human marketing research method, e.g. survey or human psychological and micro and macro economic methods to predict future travelling consumer behavioral need changes more accurate in travelling industry?This book second part aims to explain why and how future artificial intelligent technology ( big data gathering method) can be applied to predict why and when and how travelling consumer behavioral need changes in travelling industry. 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 in travel industryNowadays, many businessmen or marketing research professional hope to apply different methods to predict travelling consumer behavioral needs in order to know what will be future travelling market activities changes to help them to choose to implement what kinds of travelling service 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 travelling consumer needs changes to influence whose travelling behavioral consumption for every travels season changes.