Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior

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Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781728746418
Total Pages : 379 pages
Book Rating : 4.7/5 (464 download)

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Book Synopsis Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior by : Johnny Ch LOK

Download or read book Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior written by Johnny Ch LOK and published by . This book was released on 2018-10-13 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Big Data Gathering to Predict Travel Industry Consumer Behavior

Author : Johnny Ch Lok
Publisher : Independently Published
ISBN 13 : 9781726729819
Total Pages : 380 pages
Book Rating : 4.7/5 (298 download)

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Book Synopsis Learning Big Data Gathering to Predict Travel Industry Consumer Behavior by : Johnny Ch Lok

Download or read book Learning Big Data Gathering to Predict Travel Industry Consumer Behavior written by Johnny Ch Lok and published by Independently Published. This book was released on 2018-10-04 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.Hence, if the travelling service providers can apply the most suitable travelling consumer service needs prediction method to predict how travelling consumers' different kinds of travelling package design needs will be changed to attract their travel journey entertainment or journey public transportation service or catching air plan etc. different kinds of travelling service choice easily.

Is Artificial Intelligence The Best Traveler Behavior Prediction Tool

Author : John Lok
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.8/5 (873 download)

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Book Synopsis Is Artificial Intelligence The Best Traveler Behavior Prediction Tool by : John Lok

Download or read book Is Artificial Intelligence The Best Traveler Behavior Prediction Tool written by John Lok and published by . This book was released on 2022-06-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: I 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 individual 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 researches how to apply big data 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 assist 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.

Artificial Intelligence Big Data Travelling Consumption: Prediction Story

Author : Johnny Ch Lok
Publisher : Independently Published
ISBN 13 : 9781799117001
Total Pages : 108 pages
Book Rating : 4.1/5 (17 download)

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Book Synopsis Artificial Intelligence Big Data Travelling Consumption: Prediction Story by : Johnny Ch Lok

Download or read book Artificial Intelligence Big Data Travelling Consumption: Prediction Story written by Johnny Ch Lok and published by Independently Published. This book was released on 2019-03-08 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Future travel consumption behaviorCan (AI) big data gathering tool predict traveller individual habitual behaviour, e.g. renting travel transportation tools ?Can (AI) big data gathering tool can predict past traveller destination and travelling package choice habit and it can be intended to predict of future traveller behavior to people are creatures of habits judgement of future anywhere travelling destination choice next year or next month or next half year destination prediction ? Many of human's everyday goal-directed behaviors are performed in a habitual fashion, the transportation made and route one takes to work, one's choice of breakfast. Habits are formed when using the some behavior frequently and a similar consistency in a similar context for the some purpose whether the individual past travel consumption model will be caused a habit to whom. e.g. choosing whom travel agent to buy air ticket or traveling package; choosing the same or similar countries' destinations to go to travel; choosing the business class or normal (general) class of quality airlines to catch planes. Does habitual rent traveling car tools use not lead to more resistance to change of travel mode? It has been argued that past behavior is the best predictor of future behavior to travel consumption. If individual traveler's past consumption behavior was always reasoned, then frequency of prior travel consumption behavior should only have an indirect link to the individual traveler's behavior. It seems that renting travel car tools to use is a habit example. So, a strong rent traveling car tools useful habit makes traveling mode choice. People with a strong renting of traveling car tools of habit should have low motivation to attend to gather any information about public transportation in their choice of travelling country for individual or family or friends members during their traveling journeys. Even when persuasive communication changes the traveler whose attitudes and intention, in the case of individual traveler or family travelers with a strong renting travel car tools habit. It is difficult to change whose travel behaviors to choose to catch public transportation in whose any trips in any countries. However, understanding of travel behavior and the reasons for choosing one mode of transportation over another. The arguments for rent traveling car tools to use, including convenience, speed, comfort and individual freedom and well known. Increasingly, psychological factors include such as, perceptions, identity, social norms and habit are being used to understand travel mode choice. Whether how many travel consumers will choose to rent traveling car tools during their trips in any countries. It is difficult to estimate the numbers. As the average level of renting travel car tools of dependence or attitudes to certain travel package policies from travel agents. Instead different people must be treated in different ways because who are motivated in different ways and who are motivated by different travel package policies ways from travel agents.In conclusion, the factors influence whose traveler's individual traveller destination choice behavior The factors include either who chooses to rent traveling car tools or who chooses to catch public transportation when who individual goes to travel in alone trip or family trip. It include influence mode choice factors, such as social psychology factor and marketing on segmentation factor both to influence whose transportation choice of behavior in whose trip. So, (AI) big data can be attempted to gather past traveller transportatin tool choice, rent travelling car tools choice or catching public transportation tools choice to predict where destinaton can provide what kind of transportation tool to attract many travellers to choose to go to the place to travel.

Artificial Intelligent Travelling Behavioral Predictive Tool

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781791372620
Total Pages : 372 pages
Book Rating : 4.3/5 (726 download)

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Book Synopsis Artificial Intelligent Travelling Behavioral Predictive Tool by : Johnny Ch LOK

Download or read book Artificial Intelligent Travelling Behavioral Predictive Tool written by Johnny Ch LOK and published by . This book was released on 2018-12-10 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: I 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.

Artificial Intelligent Consumer Behavioral Predictive Tool

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781729014158
Total Pages : 379 pages
Book Rating : 4.0/5 (141 download)

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Book Synopsis Artificial Intelligent Consumer Behavioral Predictive Tool by : Johnny Ch LOK

Download or read book Artificial Intelligent Consumer Behavioral Predictive Tool written by Johnny Ch LOK and published by . This book was released on 2018-10-20 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence Big Data Travelling Consumption Prediction

Author : Johnny Ch Lok
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781721020140
Total Pages : 130 pages
Book Rating : 4.0/5 (21 download)

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Book Synopsis Artificial Intelligence Big Data Travelling Consumption Prediction by : Johnny Ch Lok

Download or read book Artificial Intelligence Big Data Travelling Consumption Prediction written by Johnny Ch Lok and published by Createspace Independent Publishing Platform. This book was released on 2018-06-10 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 marketing 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? Can it be applied to predict travelling consumer behaviors depending on any situations or only some situations? Finally, I believe that any readers can find answers to answer above these questions in this book.

Can Apply Artificial Intelligent Tourism Prediction

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781983397752
Total Pages : 187 pages
Book Rating : 4.3/5 (977 download)

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Book Synopsis Can Apply Artificial Intelligent Tourism Prediction by : Johnny Ch LOK

Download or read book Can Apply Artificial Intelligent Tourism Prediction written by Johnny Ch LOK and published by . This book was released on 2018-07-08 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 marketing strategy to cause travelling customer number to be reduced. In my this book first part, 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? Can it be applied to predict travelling consumer behaviors depending on any situations or only some situations? Finally, I believe that any readers can find answers to answer above these questions in this book.In the second part, I shall explain whether it is possible to predict travel behavioural consumption from traditional tourism market research psychology view . In this part, I shall indicate what factors can influence travel behavioural consumption, such as climate changing, renting travel car tools choice, the country's risk and safety. Then I shall indicate what psychological factors can influence travel behavioural consumption, such as: push and pull psychological factor, expectation and motivation and attitude factor.

Artificial Intelligence Technology Predicts Travel Consumption Market

Author : Johnny Ch Lok
Publisher : Independently Published
ISBN 13 : 9781717999597
Total Pages : 130 pages
Book Rating : 4.9/5 (995 download)

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Book Synopsis Artificial Intelligence Technology Predicts Travel Consumption Market by : Johnny Ch Lok

Download or read book Artificial Intelligence Technology Predicts Travel Consumption Market written by Johnny Ch Lok and published by Independently Published. This book was released on 2018-07-31 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 marketing 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? Can it be applied to predict travelling consumer behaviors depending on any situations or only some situations? Finally, I believe that any readers can find answers to answer above these questions in this book.

Can Apply Artificial Intelligent Tourism Prediction Tool to Predict Traveller Behaviors?

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781723982842
Total Pages : 187 pages
Book Rating : 4.9/5 (828 download)

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Book Synopsis Can Apply Artificial Intelligent Tourism Prediction Tool to Predict Traveller Behaviors? by : Johnny Ch LOK

Download or read book Can Apply Artificial Intelligent Tourism Prediction Tool to Predict Traveller Behaviors? written by Johnny Ch LOK and published by . This book was released on 2018-09-24 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: PrepareThis book has these three 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?(3)Whether is AI tourism behavioral prediction tool or traditional tourism market research method better to predict tourism market behavior?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 marketing strategy to cause travelling customer number to be reduced. In my this book first part, 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? Can it be applied to predict travelling consumer behaviors depending on any situations or only some situations? Finally, I believe that any readers can find answers to answer above these questions in this book.

Can Apply Artificial Intelligent Tourism Behavioral Prediction Tool

Author : Johnny Ch Lok
Publisher :
ISBN 13 : 9781070215464
Total Pages : 186 pages
Book Rating : 4.2/5 (154 download)

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Book Synopsis Can Apply Artificial Intelligent Tourism Behavioral Prediction Tool by : Johnny Ch Lok

Download or read book Can Apply Artificial Intelligent Tourism Behavioral Prediction Tool written by Johnny Ch Lok and published by . This book was released on 2019-05-25 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: When (AI) big data gather past every country traveler number who chose to go to which countries to travel in order to judge where destinations will be the country travelers' travelling choice destinations in the future.The factors influence where is the traveler choice, include personal safety, scenic beauty, cultural interest, climate changing, transportation tools, friendliness of local people, price of trip, trip package service in hotels and restaurants, quality and variety of food and shopping facilities and services etc. needs. So, whose factors will influence where is the individual travel's choice. It seems every traveler whose choice of travel process, will include past behavior. e.g. travelling experience, travelling habit, then to choose the best seasoned travelling action to satisfy whose travel needs. This process is the individual traveler's psychological choice process, who must need time to gather information to compare concerning of different travel packages, destination scene, climate change, transportation tools available to the destination, air ticket price etc. these factors, then to judge where is the best right destination to travel in the right time. Hence, (AI) big data can gather past different countries' climate changing data, transportation tool changing data, destination scene environment changing etc. different data to give opinions to travelling businesses whether any country's these above factors will influence about how many traveler number will be increase or decrease in the future.2.3Why can expectation, motivation and attitude factor influence travelling behavior?

Artificial Intelligence Big Data Gathering Consumer Behavior Prediction

Author : Johnny Ch Lok
Publisher :
ISBN 13 : 9781723986512
Total Pages : 734 pages
Book Rating : 4.9/5 (865 download)

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Book Synopsis Artificial Intelligence Big Data Gathering Consumer Behavior Prediction by : Johnny Ch Lok

Download or read book Artificial Intelligence Big Data Gathering Consumer Behavior Prediction written by Johnny Ch Lok and published by . This book was released on 2018-09-24 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to analyze activity based travel demand ? Nowadays, human are concerning the traffic congestion and air quality deterioration, the supply oriented focus of transportation planning has expanded to include how to manage travel demand within the available transportation supply. Consequently, there has been an increasing interest in travel demand management strategies, such as congestion pricing that attempts to change aggregate travel demand. The prediction aggregate level, long term travel demand to understanding disaggregate level ( i.e. individual levels ) behavioral responses to short term demand policies, such as ride sharing incentives, congestion pricing and employer based demand management schemes, alternate work schedules, telecommuting limitation of travel agent traditionally work nature shall influence oriented trip based travel modelling passenger travel demand indirectly. Finally, online travel purchase will be popular to influence the number of travel behavioural consumption nowadays. Any travel package products can be sold from websites to attract travellers to choose to prebook air ticket for any trips conveniently. In the past ten years, the internet has become the predominant carrier of all types of information and transactions. Regarding travel decisions, internet has also become an important sales channels for the travel industry, because it is associated with comparably lower distribution and sales costs, but also because ir adapts to hign supply and demand dynamics in this industry. Consequently, the travel and tourism industry tries to increase the internet sale specific share of sales volumes. So, internet sale channel has changed travel consumption behavioural pattern and characteristics and travel experience. For example, Switzerland has one of the highest population-to-computer ratio in Europe. It is also one of the most highly internet penetrated countries in terms of use of the WWW on a day-to-day basis, with more than 75 percent of the population older than 14 years using the WWW daily ( ICT, 2005). The reason of booking online tourism may include: convenience, fast transaction, finding traveling package choice easily, more airline seats available. So, online booking tourism will influence the traditional tourism agents visiting of sales and air tickets and travelling package numbers to be decreased. Finally, the online booking tourism market shares will be expanded to more than traditional tourism agents visits sale market in the future one day. So, the travel agents who still use the traditional tourism visiting sale channel which ought raise whose features to compare to differ to online tourism sale channel if these traditional touriam agents want to keep competitive ability in tourism industry for long term.

Artificial Intelligence Big Data Gathering Predicts Consumer Behavior

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781723837197
Total Pages : 488 pages
Book Rating : 4.8/5 (371 download)

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Book Synopsis Artificial Intelligence Big Data Gathering Predicts Consumer Behavior by : Johnny Ch LOK

Download or read book Artificial Intelligence Big Data Gathering Predicts Consumer Behavior written by Johnny Ch LOK and published by . This book was released on 2018-09-19 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter Two(AI) tool technical innovation in cruise tourism immediate positive emotion influence to cruise travelling consumers Can apply (AI) tool to cause positive emotion to cruise tourism consumers? Cruise tourism industry is the most influential emotion industry example to influence cruise travelling consumers' travelling entertainment choice. I shall indicate some evidences how it's innovation will influence cruise travelling consumers' emotion to be changed to positive from negative immediately as well as to prove how the cruise traveler higher utility feeling to the cruise tourism provider is not the main factor to influence whom to choose the cruise provider to consume whose cruise journey service arrangement. Nowadays, cruising has become one of the fastest growing sectors within tourism, cruise service providers need have themselves unique different entertainment service arrangement to satisfy every cruise travelling consumer individual needs in order to attract every one to choose whose cruise arrangement easily, e.g. meals, activities, entertainment and varied destinations create one-stop holiday shop, reasonable competitive ticket fare. Hence, it seems it is one exciting emotion industry. If the cruise service provider can bring positive emotion to influence many cruise travelling consumers immediately. The, even it change higher service fare to compare other similar cruise service providers. I believe it won't influence them to choose other similar cruise service providers if it can often bring immediate positive emotion to its cruise clients during they are staying in its cruises or during they have left its cruises, but they will often remember or won't forget to enjoy their cruise service provider's happing time forever. Hence, if (AI) tool can be attempted to help cruise entertainment providers to arrange different cruise journeys for varied destinations , to arrange different entertainment facilities, to arrange the different taste food to satisfy different countries age cruise consumers' needs. Then, the (AI) tool will assist the cruise providers to bring positive emotion to let every different countries age cruise consumers to feel satisfactory in order to choose to the cruise providers' cruise entertainment service more attractively.2.1 How can apply (AI) tool to predict cruise service providers bring positive emotion to their clients? Future, (AI) tool can help any cruise providers to design these kinds of any one entertainment service arrangement to satisfy the cruise provider's customers' needs.There are different special interests cruising , such as wellness at sea, freighter cruises, river cruises. It has increased the attractiveness of cruising: Romance is for lover cruise traveler target, luxury is for rich cruise traveler target, exotica is for enjoyment exciting feeling traveler target. So, every kind of cruise traveler target will have different kind of cruise entertainment service to satisfy their needs. If the cruise service provider can provide the right and attractive cruise entertainment service to satisfy the specific cruise target. Then, it will bring the positive emotion to the specific cruise target consumers more easily. Cruise travel was shaped for mass tourism. Prices have been very differently segmented. There are basically four types of markets ( Biederman, 2008):⦁ Contemporary market: On board fun and amenities are playing important role and destinations have secondary importance.⦁ Premium market: This category is more expensive than the contemporary category and where the destination has same importance as on board amenities.⦁ Luxury market: It was once dominant type of cruise tourism, but now it has only a small portion of the industry. Generally, it is the most expensive cruise category and usually it takes longer than average cruise days.

Artificial Intelligent Future Development

Author : Johnny Ch Lok
Publisher :
ISBN 13 : 9781082485527
Total Pages : 366 pages
Book Rating : 4.4/5 (855 download)

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Book Synopsis Artificial Intelligent Future Development by : Johnny Ch Lok

Download or read book Artificial Intelligent Future Development written by Johnny Ch Lok and published by . This book was released on 2019-07-25 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why does travelling market seem to similar to vehicle market which can apply (AI) learning tool to predict travellingconsumer behaviors?Artificial intelligence refers to complex in vehicle market and travelling entertainment market which is very seem to be applied to predict consumer behaviors.(AI) machine learning that posses the same characteristics of human intelligence and that have all our sense, all our reason and think just like human vehicle buyer who prefer vehicle purchase choice or travelling consumer who prefer travelling package or travelling destination and airline choice. 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. So, it can be attempted to gather data concerns that travelling consumer past travelling destination choice and air ticket price choice and different travelling package, e.g. high, middle, or low class hotel and foods supply and entertainment places choice in their past travelling journeys.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. Thus, (AI) big data can gather all these past traveler consumption behavioral choice data to make reference to analyze whether how many travelers will choose to go to the specific travelling destination in any time by the past traveler number record to different travelling destinations, then it can gather the past air ticket sale price to different destinations and past travelling package design to different destinations in order to analyze whether it is the cheap airline ticket price factor or attractive travelling package factor or attractive travelling entertainment etc. in order to predict which factor is the most potential influential factor to they choose to go to the destination to travel in different time within one year. Then, traveler agent or airline can collect these big data to judge how to design their package to attract travelers to go to anywhere to travel or what the main factor influence most of them to choose to visit the destination to travel.For example, travel agents or airlines can apply "Deep learning" breaks down tasks in ways that enables machines to assist them to predict when travelling consumer choice will be changed and why their travelling choice will change and how their travelling choice will change with increasingly complex tasks. So, such as why (AI) technology can be applied to predict how travelling consumer behavior changes to bring to judge whether anywhere will be many travelling consumers who will prefer to choose travelling hot destinations next year or next month. Then, travel agents and airlines can gather overall past travelling consumer data to analyze and conclude the more accurate prediction of different travelling destinations to the number of traveler. Then, they can choose how much air ticket price is more reasonable to charge to the travelling destination or how to design the travelling package which can bring more attractive to the prediction number of different travelling destination travelers in order to achieve to raise the different travelling destination number next year. Thus, (AI) big data machine learning can help airlines or travel agents to solve how to design any attractive travelling package challenge.

Artificial Intelligence Predicts

Author : Johnny Ch Lok
Publisher :
ISBN 13 : 9781077869318
Total Pages : 188 pages
Book Rating : 4.8/5 (693 download)

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Book Synopsis Artificial Intelligence Predicts by : Johnny Ch Lok

Download or read book Artificial Intelligence Predicts written by Johnny Ch Lok and published by . This book was released on 2019-07-03 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter FourHow can apply (AI) digital channel ( big data gathering method) predict travelling consumer behaviors?(AI) big data digital channel can be applied to help travelling businesses to evaluate whether how much the e-ticket price and travelling package price is the most attractive or reasonable to persuade travelling consumers feel it is the most reasonable price to choose to buy the airline's e-tickets or the travel agent's travelling package product from internet channel . It helps travelling consumers to feel which airlines or travelling agents which ought change their e-ticket and/or travelling package price to let travelling consumers to choose to buy the airline e-ticket or the travelling agent's travelling package products from internet channel. It can be applied to predict whether how many travelling consumer numbers can be increased or decreased when the airline e-ticket price is variable or the travelling agent travelling package price is variable . It aims to give opinions to help any online airlines or travelling agents to judge whether which e-ticket or travelling package price is the most reasonable to let travelling consumers to accept to choose to buy which airline's e-tickets or traveling agent's package products more attractive.Thus, (AI) e-ticket or e-travelling package price measurement technology can be preference to be applied online communication ecommerce and mobile phone internet platform aspect. As traveling businesses can enter their past e-ticket or travelling package prices data and past travelling customer number data into computer or mobile. Then, (AI) price measurement technology can gather these data to analyze these e-ticket or travelling package product prices and past travelling customer number to compare their e-ticket and/or travelling package prices variable changing range level to find their e-ticket and /or travelling package price variable difference to measure to make conclusion about every travelling package or/and e-ticket product's price variable changing will influence how many travelling customer number increase or decrease changing to choose to sell their different kinds of travelling package or e-ticket products more accurate. Then, (AI) price measurement software will help them to analyze all past e-ticket and/or travelling package price variable changing data to compare whether which e-ticket and/or travelling package price range can let travelling customers to feel it is more reasonable and attractive to influence them to choose to buy their e-ticket or travelling package product among different airlines and travel agent choices. Because any e-ticket or travelling package product's price is one important factor to influence travelling consumers to choose to buy the airline's e-tickets or travelling agent's travelling package products.

Artificial Intelligent Industry Ethic Behavior

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781651768334
Total Pages : 468 pages
Book Rating : 4.7/5 (683 download)

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Book Synopsis Artificial Intelligent Industry Ethic Behavior by : Johnny Ch LOK

Download or read book Artificial Intelligent Industry Ethic Behavior written by Johnny Ch LOK and published by . This book was released on 2019-12-27 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can apply (AI) big data gathering method predict senior age will be main travelling target? In the past, Germany government had established tourism survey analysis to analyze survey data in order to arrive at reliable conclusions on future trends in travel behavior. To aim to find how demographic change will influence the tourism market and how the industry can adapt to those changes. The travel analysis provided data on tourism consumer behavior, including attitudes, motives and intentions. Since, 1970 year, it is based on a random sample, representative for the population in private households aged 14 years or older. Then, a continuous high scientific standard combined with a national and international users makes the travel analysis a useful tool and reliable source for tourism industry and policy decisions. It aimed to gather statistical data. e.g. on the age structure and on demographic trends, quantitative and qualitative analysis with time series data from the travel analysis. It shows e.g. not only the future volume , quite different from today's seniors, or how who will travel of family holidays will change, e.g. single parents of low, but grandparents of growing significance for tourism. Demographic change is said to be one of the important drivers for new trends in consumer traveling change behavior in most European countries ( e.g. Lind 2001). Because the growing number of senior citizens in the European Union and other industrialized countries, such as the USA and Japan, looks to become one of the major marketing challenges for the tourism industry. United Nations statistics predict that the share of people being 60 age or older will grow dramatically in the coming future, and is expected to rise from 10 percent of the world population in 2000 year to more than 20 percent in 2050 year ( United Nations Population Division, 2001). From its statistic, some data showed that travel propensity increased throughout life until the age of about 50 years of age and was then kept stable until very late in life 75 age. The most important results is that the travel propensity when getting older is not going down between 65 and 75 age of course, the overall development of this variable is influenced by a lot of other factors which are responsible for quite a variation over time. It is now possible to suggest that the general pattern of travel propensity is one of the key indicators for holiday life cycle travel behavior, includes three stages. The growth stage tends to increase from early adult hood until 45 age old or when reaching some 80%. The next stage is stabilization from the ages of around 50 age, until 75 age old, starting with a lower increase. Finally, the decrease stage is a slight decrease occurs once people reach the more advanced age of 75 age to 85 age old ( Lohmann & Danielsson 2001). So, it seems Germany government tourism prediction to future travelers' behavior indicated these findings, such as on how future senior generations will travel, who had used survey data to examine the patterns of travel behavior of a generation getting older and applied the findings to draw conclusions on the future. Also, it predicted that on the future of family trips, family segmentation will be the travel behavior patterns in the future. These findings together with the statistical data on demographic change allowed for a better understanding of the coming tends in family holidays. It's aim developed in consumer behavior related to demographic change and predicted what will happen future of tourism one had to consider other influences and drivers as well, for example, trends on the supply side. e.g. low cost airlines or in travelling consumption behavior in general whether how the past may provide a key to predict travel patterns of senior citizens to the future.

Artificial Intelligence Big Data Gathering

Author : Johnny Ch LOK
Publisher :
ISBN 13 : 9781723902260
Total Pages : 572 pages
Book Rating : 4.9/5 (22 download)

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Book Synopsis Artificial Intelligence Big Data Gathering by : Johnny Ch LOK

Download or read book Artificial Intelligence Big Data Gathering written by Johnny Ch LOK and published by . This book was released on 2018-09-21 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many researchers have focused on identifying the lifestyle of the consumers to have better information about them. This study used the lifestyle analysis to identify market segments. Otherwise, some consumption psychologists believe to apply life style analysis for market segmentation, the developed of product strategy and the developed of the most appropriate communication strategy. They suggested successful retailers based on general application of lifestyle analysis have begun to implement a portfolio management approach which focuses on the needs of the key target markets. So, lifestyle segmentation can provide a valuable insight into the task of creating an effective brand identity. The study of lifestyle often provides fresh insights into the market and gives a more dimensional view of the target consumers. The marketing managers may be able to develop improved multi-dimensional views of key market segments, uncover new product opportunities obtain better product position, develop improved advertising communications based on a richer more life-like portrait of the target consumer and generally improve overall marketing strategy. These consumption psychologists assume that the members of any target client groups are all similar. The first hypothesis is people differ in their lifestyle they can be grouped into segments and the second hypothesis is people belonging to lifestyle segments differ in their demographics.Thus, such as airline ticket every consumer who can either choose to buy electronic airline ticket from internet or airline shop. In travel consumption environment, a travel consumer chooses a travel agent package or a airline brand , which indicates a maximum possibility of the definition of whose lifestyle identity. Alternatively, a travelling person makes a choice in a travel consumption environment in order to define actualize whose lifestyle identity if through the travel agent package products or airline brands chosen. It can be assumed that the travelling individual's consumption behavior can be predicted from an understanding of how who represents whose would be himself/herself of the details of choosing lifestyle system are known from internet survey or questionnaire method. Thus, digital internet is one good channel to research travel consumer lifestyle to predict whose consumption style.In economic view point, demand is a model of travel consumer behavior. It attempts to identify the factors that influence the choices that are made by travel consumers. In microeconomics, the objective of the travel consumer is to maximize the utility that can be derive given their travel choice preferences, income, the airline ticket prices relates travel package products and services for which the travel demand function in derived.Utility is the capacity of a travel package product or service to satisfy a traveler' want. It can explain the phenomenon of travelling value. Since utility is subjective and can't be observed and measured directly. The objective in microeconomics is to maximize the satisfaction or utility of traveler individuals given their travel package preferences, incomes and the airline ticket prices of travel package products or services who buy or consume in travel market. Thus, total travel utility of more or less travel satisfaction degree be caused by traveler consumer behavior. It is the traveler consumption psychological result ( effect) and it has close relationship with travel agent service.