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Is Artificial Intelligence The Best Traveler Behavior Prediction Tool
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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.
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 2018-06-11 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence Big Data Travelling by : Johnny Ch LOK
Download or read book Artificial Intelligence Big Data Travelling written by Johnny Ch LOK and published by . This book was released on 2018-06-17 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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?
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.
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 2019-06-02 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Artificial Intelligence Predicts Marketing Behavior by : Johnny Ch Lok
Download or read book Artificial Intelligence Predicts Marketing Behavior written by Johnny Ch Lok and published by . This book was released on 2020-12-22 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligent Data Gathering Tool Predicts by : Johnny Ch LOK
Download or read book Artificial Intelligent Data Gathering Tool Predicts written by Johnny Ch LOK and published by . This book was released on 2018-10-11 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The (AI) big data technological travelling customer behavioral prediction tool seems to be the best travelling behavioral prediction tool in the world are those that know every one of different country's traveler need. Their likes and dislikes which style of travelling package, preferences and travel destination changing tastes to travelling destination choices. The capacity of the human brain, however, limits us from achieving these different type of travel package sales. In this competitive travelling destination choice entertainment environment, (AI) big data machine learning enables platforms to assist the air ticket and travel package sales team by tracking the travelling consumer behaviors of each travelling customer, learning and memorizing their preferences and predicting their future travelling destination choice and travelling package design needs.Finally, I recommend that for a travel agent or airline travelling marketing platform to make their travelling customer engagement efficient and fully-functional, I should be able to: applying (AI) tools to track every travelling customer behavior across the web, connecting to a society of data sources, CRM, DMS, third-party, web travelling brands, social traveler email, click etc., aggregating and accurately cross-reference data from a variety of sources, leveraging this data to drive insights on a mass scale, as well as on an individualized basis, driving actions and automatically direct travelling customer engagement via multiple channels based on where each customer is in their travelling individual lifecycle.
Book Synopsis How Artificial Intelligence Measures Traveller Needs by : Johnny Ch Lok
Download or read book How Artificial Intelligence Measures Traveller Needs written by Johnny Ch Lok and published by . This book was released on 2019-11-21 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence Predicts Traveller Behaviors? by : Johnny Ch Lok
Download or read book Artificial Intelligence Predicts Traveller Behaviors? written by Johnny Ch Lok and published by Independently Published. This book was released on 2019-07-07 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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' travellingdestination 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 andtravelling 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.
Book Synopsis Prediction Artificial Intelligent Travel, Health, Education, Transportation, Space Exploration: Consumer Behavior by : Johnny Ch Lok
Download or read book Prediction Artificial Intelligent Travel, Health, Education, Transportation, Space Exploration: Consumer Behavior written by Johnny Ch Lok and published by Independently Published. This book was released on 2018-09-04 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction I write this book aims to let readers to feel that future artificial intelligent (AI) technology whether it could be applied to which aspects to satisfy our life need. I shall concentrate on predicting (AI) technology can be applied to these several aspects: education, business, transportation, space tourism, medical health these five aspects. This is my opinion, it is not absolute true. However, I shall follow our nowadays (AI) technological development trend to explain how I predict how (AI) technology will be invented to applied to these five aspects to satisfy consumer individual and businessman individual both needs, even I shall explain how to (AI) tool to predict consumer behavior in psychological function successfully. First part, I shall explain how and why (AI) technology can be applied to space exploration missions and space tourism development. Will artificial intelligent space exploration bring long term economic, entertainment and technological benefits? What factors will apply (AI) technology to assist space tourism leisure development? How to apply (AI) technology to solve any challenges during space travelling boats fly to space to encounter sudden accident. This part concerns whether artificial intelligent technology can be used in future space development. Firstly, I shall explain how human can apply artificial intelligent technology to space development which aspects. Then, I shall indicate how scientists need to follow what steps in order to achieve (AI) space robotic technology can be used in space technology to develop more successful. In this part, I shall also explain what benefits or strengths that (AI) space robotic technology can bring to assist space development as well as what disadvantages or weaknesses if (AI) space robotic technology can not be used to assist space development. I shall conclude whether (AI) space robotic technology will be real one tool which can assist space development, when human chooses to adapt to work and live and space tourism entertainment activities with (AI) space robots together in our future (AI) space robotic technological societies. Second part, I shall explain how future (AI) technology can be applied to business and health service aspect. The research questions: Can AI grow productivity? If AI can grow productivity, how can it raise ? If productivity raised, can it raise economic development ? How will (AI) influence human job change? Advances in artificial intelligence (AI) technology is for the progress in critical areas, such as health, education, energy, economy inclusion, social welfare and the environment. Thus, it brings this question: Which (AI) workers be instead of traditional human workers in these different new markets? In recent years, machines had been used to be human's tasks in the performance of certain tasks related to intelligence, such as aspects of image recognition. Experts also forecast that rapid progress in the field of specialized artificial intelligence will continue. Then, it also brings this question: Does (AI) exceed that of human performance on more and more tasks? If it is truth, will some of human jobs to be disappeared? (AI) will be instead of human some simple jobs, then unemployment rate to the low skillful and low educated workers will be increased.
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.
Book Synopsis Artificial Intelligence Consumer Behavioral Prediction Machine by : Johnny Ch Lok
Download or read book Artificial Intelligence Consumer Behavioral Prediction Machine written by Johnny Ch Lok and published by . This book was released on 2019-11-22 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.-Adventure/exploration: It refers relatively long cruises with special and exotic places where the destination is the main purpose of the trip.-European cruise travel: Duration takes more five days than worth American travel duration. There is a tendency on European market during the years that duration of travel is getting shorter. This short demand of is explained with the strong demand of customers ( Hensen, 2003). Beside this, it is most likely that cruise companies try to convince tourists with short haul travels instead of long term cruise trips for more expenditure.Thus, I believe that even, the cruise service provider charges higher ticket which won't influence cruise consumers who do not choose its entertainment service on its cruises. If it can arrange the attractive cruise entertainment facilities and destination journey arrangement, staying days arrangement to satisfy different specific cruise target market needs absolutely in order to bring whose emotion to be positive to it's service provision. Then, the cruise service provider will attract many potential cruise clients to choose its cruise service absolutely. Otherwise, if it only bring negative emotion to its cruise clients, it will not attract many potential cruise clients to choose it or loses its old cruise clients, even, its cruise ticket price is needed to decreased in order to raise competitive effort.In conclusion, I believe that future (AI) tools need to learn how to bring cruise consumers to arise individual immediate or expected positive emotion, this positive emotion consideration is more important to compare to how to reduce cruise ticket price in order to attract cruise clients in global cruise competitive cruise industry.
Book Synopsis Artificial Intelligence and Machine Learning in the Travel Industry by : Ben Vinod
Download or read book Artificial Intelligence and Machine Learning in the Travel Industry written by Ben Vinod and published by Springer Nature. This book was released on 2023-05-26 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.
Book Synopsis Artificial Intelligence Predicts Consumer Behavioral Tool Business Journey by : Johnny C. H. Lok
Download or read book Artificial Intelligence Predicts Consumer Behavioral Tool Business Journey written by Johnny C. H. Lok and published by Independently Published. This book was released on 2018-11-21 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, 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.
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.
Book Synopsis Artificial Intelligence How Influences E-Publish and E-Tourism by : Johnny Ch Lok
Download or read book Artificial Intelligence How Influences E-Publish and E-Tourism written by Johnny Ch Lok and published by Independently Published. This book was released on 2021-04-16 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can (AI) big data gather data to predict when climate will change to influence poor travelling behaviours?(AI) big data tool can predict the flexibility of human travelling behavioral change is at least the result of one such mechanism, our ability to travel mentally in time and entertain potential future. Understanding of the impacts is holidays, particularly those involving travel. Using focus groups research to explores tourists' awareness of the impacts of travel own climate change, examines the extent to which climate change features in holiday travel decisions and identifies some of the barriers to the adoption of less carbon intensive tourism practices. The findings suggest many tourists don't consider climate change when planning their holidays. The failure of tourists to engage with the climate change to impact of holidays, combined with significant barriers to behavioral change, presents a considerable challenge in the tourism industry. In the future, computer (AI) big data tool can attempt to predict when the country's climate change to influence travelers to choose to go to the country to travel, e.g. next month or next half year or next year hot travelling destinations. Tourism is a highly energy intensive industry and has only recently attracted attention as an important contributions to climate change through greenhouse gas emissions. It has been estimated that tourism contributes 5% of global carbon dioxide emissions. There have been a number of potential changes proposed for reducing the impact of air travel on climate change. These include technological changes, market based changes and behavioral changes. However, the role that climate change plays in the holiday and travel decisions of global tourists. How the global tourists of the impacts travel has on climate change to establish the extent to which climate change, considerations features in holiday travel decision making processes and to investigate the major barriers to global tourists adopting less carbon intensive travel practices. It will bring this question: Will tourists aware the 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 consumption behavior 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.Mention of holidays and travel were deliberately avoided in the recruitment process, so as not to create a connection factor to influence traveler's individual mind. However, the dismissal of alternative transportation modes can be conceived as either a structural barrier, in the sense that flying is perhaps the only realistic option to reach long-haul holiday destination, or a perceived behavioral control barriers in that an individual perceives flying as the only option open to whom.
