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Author: Johnny Ch LOK Publisher: ISBN: Category : Languages : en Pages : 167
Book Description
Challenges of artificial intelligence, algorithms technology and machine learning impact to consumption marketMarkets have played a key role in providing individuals and businesses with the opportunity to gain from trade. If (AI) big data gather tool can predict how to change potential customer behavior in success. The challenges to consumers will face that the overall market consumption model will be dominated by the businessmen only. So, it is not fair or reasonable to consumers, because (AI) big data gather tool has controlled or dominated all consumers' minds and it has predicted how and why every kind of product or service consumer shopping model or consumption behaviors how will change.It will bring this questions: How can market designers learn the characteristics necessary to set optimal, or at least better, reserve prices after they had gather all data to conclude the analytical results of their consumers behaviors how will change? How can market designers better learn the environments of their markets?In response to these challenges, artificial intelligence (AI ) and machine learning are important tools for market design. For example, retailers and marketplaces , such as eBay, Amazon and many others are mining their vast amounts of data to identity patterns that help them create better shopping experiences for their clients and increase the efficiency of their markets. By having better prediction tools, these and their companies can predict and better manage dynamic consumption market environments. The improved forecasting that (AI) and machine learning algorithms provide help marketplaces and retailers better anticipate consumer demand and producer supply as well as help target products and activities for segmented markets. Another important application of (AI) 's strength in improving forecasting to help markets operate more efficiently is in electricity market example. To operate efficiently, electricity marker makers can attempt to apply (AI) machine learning tool to follow every household family electricity consumers' past electricity consumption record to judge ( predict) how it will be every family's forecasting in the year.
Author: Johnny Ch LOK Publisher: ISBN: Category : Languages : en Pages : 167
Book Description
Challenges of artificial intelligence, algorithms technology and machine learning impact to consumption marketMarkets have played a key role in providing individuals and businesses with the opportunity to gain from trade. If (AI) big data gather tool can predict how to change potential customer behavior in success. The challenges to consumers will face that the overall market consumption model will be dominated by the businessmen only. So, it is not fair or reasonable to consumers, because (AI) big data gather tool has controlled or dominated all consumers' minds and it has predicted how and why every kind of product or service consumer shopping model or consumption behaviors how will change.It will bring this questions: How can market designers learn the characteristics necessary to set optimal, or at least better, reserve prices after they had gather all data to conclude the analytical results of their consumers behaviors how will change? How can market designers better learn the environments of their markets?In response to these challenges, artificial intelligence (AI ) and machine learning are important tools for market design. For example, retailers and marketplaces , such as eBay, Amazon and many others are mining their vast amounts of data to identity patterns that help them create better shopping experiences for their clients and increase the efficiency of their markets. By having better prediction tools, these and their companies can predict and better manage dynamic consumption market environments. The improved forecasting that (AI) and machine learning algorithms provide help marketplaces and retailers better anticipate consumer demand and producer supply as well as help target products and activities for segmented markets. Another important application of (AI) 's strength in improving forecasting to help markets operate more efficiently is in electricity market example. To operate efficiently, electricity marker makers can attempt to apply (AI) machine learning tool to follow every household family electricity consumers' past electricity consumption record to judge ( predict) how it will be every family's forecasting in the year.
Author: Johnny Ch Lok Publisher: ISBN: Category : Languages : en Pages : 168
Book Description
Challenges of artificial intelligence, algorithms technology and machine learning impact to consumption marketMarkets have played a key role in providing individuals and businesses with the opportunity to gain from trade. If (AI) big data gather tool can predict how to change potential customer behavior in success. The challenges to consumers will face that the overall market consumption model will be dominated by the businessmen only. So, it is not fair or reasonable to consumers, because (AI) big data gather tool has controlled or dominated all consumers' minds and it has predicted how and why every kind of product or service consumer shopping model or consumption behaviors how will change.It will bring this questions: How can market designers learn the characteristics necessary to set optimal, or at least better, reserve prices after they had gather all data to conclude the analytical results of their consumers behaviors how will change? How can market designers better learn the environments of their markets?In response to these challenges, artificial intelligence (AI ) and machine learning are important tools for market design. For example, retailers and marketplaces, such as eBay, Amazon and many others are mining their vast amounts of data to identity patterns that help them create better shopping experiences for their clients and increase the efficiency of their markets. By having better prediction tools, these and their companies can predict and better manage dynamic consumption market environments. The improved forecasting that (AI) and machine learning algorithms provide help marketplaces and retailers better anticipate consumer demand and producer supply as well as help target products and activities for segmented markets. Another important application of (AI) 's strength in improving forecasting to help markets operate more efficiently is in electricity market example. To operate efficiently, electricity marker makers can attempt to apply (AI) machine learning tool to follow every household family electricity consumers' past electricity consumption record to judge ( predict) how it will be every family's forecasting in the year.
Author: Johnny Ch LOK Publisher: ISBN: Category : Languages : en Pages : 79
Book Description
Why can (AI) be applied to predict consumer behaviors?Artificial intelligence refers to complex in vehicle market, machine learning that posses the same characteristics of human intelligence and that have all our sense, all our reason and think just like human do. Besides, machine learning is the practice of using algorithms to collect and examine data, learn from it, and then make a determination or prediction about something in the world.The machine is " trained" using large amounts of data and algorithms that give it the ability to learn how to automatically perform a task with increasing accuracy. Otherwise, deep learning is primarily based on artificial neural networks inspired by our understanding of the biology of human's brains.Deep learning breaks down tasks in ways that enables machines to assist us with increasingly complex tasks, driverless cars, better preventive healthcare and more accurate product recommendation ( including vehicle recommendations). So, such as why (AI) technology can be applied to predict how vehicle consumer behavior changes to bring to judge whether vehicle consumer will like what kinds of vehicle styles next year. Then, vehicle manufacturers can gather overall vehicle consumer data to analyze and conclude the more accurate vehicle design direction for next year any new design vehicle manufacturing products.Thus, (AI) machine learning can help vehicle manufacturers to solve how to design any new vehicle products challenge. A vehicle is both one of the most important and carefully considered purchases the majority of people will ever make in their lifetime. It is also a purchase that tends to be fundamentally tied to a person's identify and view of themselves. As the same time, vehicle consumers changing lifestyles result in changing vehicle needs, e.g. the young sport car enthusiast matures into the family driver. Automotive dealers need to remember that vehicle customers and prospects are individual human beings with risk, complex and ever-changing lives factors, these factors will influence every vehicle consumer why who feels has vehicle purchase need, and how who choose to buy the first vehicle if who decided to buy the first vehicle.The (AI) technological customer behavioral prediction tool seems to be the best vehicle salespeople in the world are those that know every one of their vehicle customers. Their likes and dislikes which style of vehicle design, preferences and changing tastes to vehicle choices. The capacity of the human brain, however, limits us from achieving this type of vehicle sales and frequent turnover at vehicle dealerships often results in the further loss of vehicle salespeople along with their vehicle customer relationships and knowledge. In this competitive vehicle environment, machine learning enables platforms to assist the vehicle sales team by tracking the vehicle consumer behaviors of each vehicle customer, learning and memorizing their preferences and predicting their future vehicle purchase needs.Finally, I recommend that for a vehicle dealerships marketing platform to make their customer engagement efficient and fully-functional, I should be able to: applying (AI) tools to track every vehicle customer behavior across the web, connecting to a society of data sources, CRM, DMS, third-party, web vehicle brands, social email, click etc., aggregating and accurately cross-reference data from a variety of sources, leveraging this data to drive insights on a mass scale, as well as on an individualized basis, driving actions and automatically direct customer engagement via multiple channels based on where each customer is in their individual lifecycle.
Author: Steven Struhl Publisher: Kogan Page Publishers ISBN: 0749479566 Category : Business & Economics Languages : en Pages : 273
Book Description
The ability to predict consumer choice is a fundamental aspect to success for any business. In the context of artificial intelligence marketing, there are a wide array of predictive analytic techniques available to achieve this purpose, each with its own unique advantages and disadvantages. Artificial Intelligence Marketing and Predicting Consumer Choice serves to integrate these widely disparate approaches, and show the strengths, weaknesses, and best applications of each. It provides a bridge between the person who must apply or learn these problem-solving methods and the community of experts who do the actual analysis. It is also a practical and accessible guide to the many remarkable advances that have been recently made in this fascinating field. Online resources: bonus chapters on AI, ensembles and neural nets, and finishing experiments, plus single and multiple product simulators.
Author: Johnny Ch Lok Publisher: Createspace Independent Publishing Platform ISBN: 9781721070879 Category : Languages : en Pages : 64
Book Description
Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans. Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately. In my this book, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book.
Author: Johnny Ch LOK Publisher: ISBN: 9781983067570 Category : Languages : en Pages : 63
Book Description
Prepare 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 manufactuers' 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 behaviral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book.
Author: Johnny Ch LOK Publisher: ISBN: 9781729251218 Category : Languages : en Pages : 573
Book Description
PrepareThis book aims to explain why and how future artificial intelligent technology ( big data gathering method) can be applied to assit businesses to predict why and when and how consumer behavior changes. I shall explain why traditional psychological and statistic and marketing methods are applied to predict consumer behaviors, human's judgement and analytical effort will be worse to compare AI machine's judgement and analytical effort. Also, I shall indicate different business organizations why they apply AI big data gathering method to help them to design any questionnaires ( surveys) questions which will be more valid and useful to conclude human's questionnaires ( surveys) design questions method.This book has these two research questions need to be answered?(1)Can apply (AI) learning machine predict consumer behaviors?(2)Can (AI) learning machine replace human marketing research method, e.g. survey or human psychological and micro and macro economic methods to predict consumer behaviors more accurate?Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans.Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately.In my this book first part, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book.
Author: Johnny Ch LOK Publisher: ISBN: 9781793052858 Category : Languages : en Pages : 555
Book Description
5.(AI) and machine learning technologies make it possible to capture, process, and inter data on a massive scale effectively , then any human being could ever do. For example, Criteo's creative technology " Kinetic design" can apply insights from 1.2 billion monthly impressions to select and optimize individual branded advertisements components according to each shopper's preference and intent. This ensures more personalization and visually inspiring on brand ads. resulting in up to 12% more sales for (AI) technology advertiser clients.Moreover, advertisers can now engage and inspire shoppers on a more personal level, rendering custom ads. it real-time for every impression. So, designer continues to learn from each design's success to makeads. more and more effective over time. Furthermore, brands are increasingly using paid search on retail sites to draw attention to their products on the crowded online shelf, e.g. Google shopping is a key growth area's more users are engaging with shopping ads. and across the globe. Google shopping has become essential to retailers' marketing strategies, but is a difficult channel to apply its tool to be promoted effectively . Thus, future (AI) and machine -learning technologies can dramatically improve digital commerce performance application to apply (AI) and machine learning to digital consumer. So, future (AI) technology can be applied to digital commerce aspect, it will fall into the categories of pattern recognition, classification, prediction and consumer behavior.In conclusion, the benefits of using (AI) in digital commerce include: improved efficiency in discovering the relationships between datasets over traditional methods, which require complex modeling and coding, improved accuracy for clearly defined processes that involve a lot of manual processing, ability to deal with a large emotion of data with many attributes, for example: customer behavior data, multichannel and multi-device data , complex product data and fraud detection, more accurate analysis, such as customer segmentation sentiment, analysis and personalization frequent algorithum refreshes, such as several times a day, to capture the changes in customer and market behavior. Finally, however, a lot of types predictive consumption behavior around (AI), in particulars that driven by vendors claiming their solutions are (AI) , ready and can deliver dramatic improvements over existing technologies. Application leaders for digital commerce can be misled into believing that (AI) can solve all their problems, which is not true for n in-depth discussion of the (AI) consumers and market behavioral predictive tool and machine -learning technologies bot. Thus, (AI) prediction consumer behavioral technology can give beneficial quantitative analysis for forecasting in business and market especially in consumer behavior and in the consumer decision-making process ( consumer choice model) more effectively and efficiently.ChaptersevenIs Artificial Intelligent the most effective andaccurate consumer behavioral tool?Is (AI) the best and the most effective and accurate consumer behavioral prediction tool to compare other kinds of consumer behavioral prediction tools? Nowadays, retailing competitions are serious businessmen often find different kinds of methods to attempt to predict consumer changes. The consumer behavioral predictive methods can include as these below methods, instead of (AI) big data gathering tool.
Author: Johnny Ch Lok Publisher: ISBN: 9781077870437 Category : Languages : en Pages : 188
Book Description
What methods can predict future travel behavioural consumptionHow to use qualitative of travel behavioural method to predict future travel consumption. I also suggest to use qualitative of travel behavioural method to predict future travel consumption. Methods such as focus groups interviews and participant observer techniques can be used with quantitative approaches on their own to fill the gaps left by quantitative techniques. These insights have contributed to the development of increasingly sophisticated models to forecast travel behavior and predict changes in behavior in response to change in the transportation system. First, survey methods restrict not only the question frame but the answer frame as well, anticipating the important issues and questions and the responses. However, these surveys methods are not well suited to exploratory areas of research where issues remain unidentified and the researched seek to answer the question "why?". Second, data collection methods using traditional travel diaries or telephone recruitment can under represent certain segments of the population, particularly the older persons with little education, minorities and the poor. Before the survey, focus group for example can be used to identify what socio-demographic variables to include in the survey, how best to structure the diary, even what incentives will be most effective in increasing the response rate. After the survey, focus, focus groups can be used to build explanations for the survey results to identify the "why" of the results as well as the implications. One Asia Pacific survey research result was made by tourism market investigation before. It indicated the travel in Asia Pacific market in the past, had often been undertaken in large groups through leisure package sold in bulk, or in large organized business groups, future travelers will be in smaller groups or alone, and for a much wider range of reasons. Significant new traveler segments, such as female business traveler. The small business traveler and the senior traveler, all of which have different aspirations and requirements from the travel experience. Moreover, Asia tourism market will start to exist behaviors in the adoption of newer technologies, a giving the traveler new ways to manage the travel experience, creating new behaviors. This with provide new opportunities for travel providers. The use of mobile devices, smartphones, tablets etc. and social media are the obvious findings to become an integral part of the travel experience. Thus, quality method can attempt to predict Asia Pacific tourism market development in the future. However, improving the predictive power of travel behavior models and to increase understanding travel behavior which lies in the use of panel data( repeated measures from the same individuals). Whereas, cross-sectional data only reveal inter-individual differences at one moment in time, panel data can reveal intra-individual changes over time. In effect, panel data are generally better suited to understand and predict ( changes in ) travel behavior. However, a substantial proportion was also observed to transition between very different activity/travel patterns over time, indicating that from one year to the next, many people renegotiated their activity/travel patterns.
Author: Johnny C. H. Lok Publisher: Independently Published ISBN: 9781790161348 Category : Languages : en Pages : 62
Book Description
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.