AI Impacts in Digital Consumer Behavior

AI Impacts in Digital Consumer Behavior
Author: Musiolik, Thomas Heinrich
Publisher: IGI Global
Total Pages: 392
Release: 2024-03-04
Genre: Business & Economics
ISBN:
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In the ever-evolving landscape of digital innovation, businesses grapple with the challenge of deciphering dynamic consumer behavior. AI Impacts in Digital Consumer Behavior is a pioneering exploration tailored for academic scholars seeking insights into the profound influence of artificial intelligence on consumer dynamics. As businesses strive to harness the potential of data, this book serves as a beacon, offering a comprehensive understanding of the intricacies involved in tracking, analyzing, and predicting shifts in consumer preferences. This groundbreaking work not only identifies the complexities posed by the rapidly changing digital landscape but also presents a solution-oriented approach. It unveils a theoretical framework and the latest empirical research, providing scholars with a toolkit of concepts, theories, and analytical techniques. With a multidisciplinary focus on behavioral analysis, the book equips academic minds with the knowledge to navigate the challenges of the digital age. Furthermore, it addresses the ethical dimensions and ethic considerations associated with the accelerating pace of consumer behavior analysis, shedding light on the responsible use of AI technologies.

Enhancing and Predicting Digital Consumer Behavior with AI

Enhancing and Predicting Digital Consumer Behavior with AI
Author: Musiolik, Thomas Heinrich
Publisher: IGI Global
Total Pages: 464
Release: 2024-05-13
Genre: Business & Economics
ISBN:
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Understanding consumer behavior in today's digital landscape is more challenging than ever. Businesses must navigate a sea of data to discern meaningful patterns and correlations that drive effective customer engagement and product development. However, the ever-changing nature of consumer behavior presents a daunting task, making it difficult for companies to gauge the wants and needs of their target audience accurately. Enhancing and Predicting Digital Consumer Behavior with AI offers a comprehensive solution to this pressing issue. A strong focus on concepts, theories, and analytical techniques for tracking consumer behavior changes provides the roadmap for businesses to navigate the complexities of the digital age. By covering topics such as digital consumers, emotional intelligence, and data analytics, this book serves as a timely and invaluable resource for academics and practitioners seeking to understand and adapt to the evolving landscape of consumer behavior.

Artificial Intelligence Predicts Consumer Behaviors

Artificial Intelligence Predicts Consumer Behaviors
Author: Johnny Ch LOK
Publisher:
Total Pages: 183
Release: 2019-12-21
Genre:
ISBN: 9781678726157
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Hence, future (AI) technology will impact consumer technology when any retailers apply it to assist its manufacturing processes or product sale or service provision processes to satisfy consumers' needs, it means that it can help any retailers to influence positive emotion to consumers in their whole sale or consumption or purchase processes(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.

Innovative Computing

Innovative Computing
Author: Jason C. Hung
Publisher: Springer
Total Pages: 0
Release: 2023-01-19
Genre: Computers
ISBN: 9789811642609
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This book comprises select proceedings of the 4th International Conference on Innovative Computing (IC 2021) focusing on cutting-edge research carried out in the areas of information technology, science, and engineering. Some of the themes covered in this book are cloud communications and networking, high performance computing, architecture for secure and interactive IoT, satellite communication, wearable network and system, infrastructure management, etc. The essays are written by leading international experts, making it a valuable resource for researchers and practicing engineers alike.

Artificial Intelligence Big Data Gathering How Impacts Consumer Behavior

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

Artificial Intelligence How Influences Consumer Behaviors

Artificial Intelligence How Influences Consumer Behaviors
Author: Johnny Ch LOK
Publisher:
Total Pages: 301
Release: 2020-04-10
Genre:
ISBN:
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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.

Strategic Innovative Marketing and Tourism

Strategic Innovative Marketing and Tourism
Author: Androniki Kavoura
Publisher: Springer
Total Pages: 1330
Release: 2019-07-03
Genre: Business & Economics
ISBN: 3030124533
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This book covers a very broad range of topics in marketing, communication, and tourism, focusing especially on new perspectives and technologies that promise to influence the future direction of marketing research and practice in a digital and innovational era. Among the areas covered are product and brand management, strategic marketing, B2B marketing and sales management, international marketing, business communication and advertising, digital and social marketing, tourism and hospitality marketing and management, destination branding and cultural management, and event marketing. The book comprises the proceedings of the International Conference on Strategic Innovative Marketing and Tourism (ICSIMAT) 2018, where researchers, academics, and government and industry practitioners from around the world came together to discuss best practices, the latest research, new paradigms, and advances in theory. It will be of interest to a wide audience, including members of the academic community, MSc and PhD students, and marketing and tourism professionals.

Artificial Intelligence Influences: Marketing Strategy

Artificial Intelligence Influences: Marketing Strategy
Author: Johnny Ch Lok
Publisher: Independently Published
Total Pages: 400
Release: 2019-03-27
Genre: Business & Economics
ISBN: 9781091760240
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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.2.Slow pace of cultural change. Consumers need to adapt or accept (AI) new technology consumption model in the traditional retail industry. The rate of change is outpacing the ability of businesses to keep up. (AI) big data gather tool needs to be designed to adopt in new or evolved business model requires, in most cases, a new level of customer behavioral predictive machine operation will impact to influence any retail businesses' consumer behavioral changes at a minimum, an organization's structure, capabilities, culture and decision making. If the retail business expects to apply (AI) big data gather tool to predict how to change its consumer behaviors and how their consumption behaviors will tend to change in order to achieve to change their positive emotion from negative emotion before they choose to buy its product or consume its service in success.6.3Challenge to using (AI) neural networks to predict customer behavior from big data gather tool(AI) big data gather tool will encounter the challenge: How can predict customer behavior be represented as sequential data describing the interactions of the customer with a company or an (AI) data gather system through the time, e.g. these interactions are items that the customer purchase or views ? So, every customer data gather, (AI) needs to spend time to analyze how and why to cause whose consumption behavioral choice. It is too difficult matter or judgement for (AI) learning. So, (AI) needs to spend time to learn how to analyze every customer's shopping behavior or actin in order to gather all different consumers' past shopping action information in order to help business owners to predict future its potential customer shopping behavior how to change more clear and accurate prediction. (AI) big data gather tool needs to learn to know that how to judge every customer interaction likes purchases over time can be represented with sequential data. Sequential data has the main property that the order of the information is important. Many (AI) machine learning models are not suited for sequential data, as they consider each input sample independent from previous ones. Therefore, at the end of the sequence, (AI) big data gather learn machines need to keep in their internal state of every customer purchase data, kind of product or service, price, whole year consumption times form all previous inputs, making them suitable for this type of data.

Artificial Intelligence Predicts Consumer Behaviors

Artificial Intelligence Predicts Consumer Behaviors
Author: John Lok
Publisher: Independently Published
Total Pages: 78
Release: 2021-09-10
Genre:
ISBN:
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To apply (AI) learning machine technology to understand customer online purchase behavior, it will raise business e-commerce successful chance: For example, (AI) learning machine can help businesses to gather data to analyze to determine whether short-term or long-term signals in the online consumer behavior that indicate higher purchase intents to let every online business to know. (AI) learning machine can find that online users with long-term purchasing intent tend to save and click through on more content. However, as online users approach the time of purchase their activity becomes more topically focused and actions shift from saves to searches from online consumption channel. Then, (AI) learning machine will further find that the brand product purchase signals in online behavior can exist weakness before an online purchase is made and can also be traced across different online purchase categories. Finally, (AI) learning machine synthesize these insights in predictive models of online user purchasing intent to the brand of product. Taken together, it's work identifies a set of general principles and signals that can be used to model online user purchasing intent across many online content discovery applications. Thus, (AI) learning machine can help online businesses to gather any online users' click online behaviors data to judge whether there are how many online users will choose to find their online business websites to make final decisions to buy their products from online channels. Then, it will give opinions to help the online businesses to let it to judge whether what are the important website factors will help its online business to attract many online consumers, e.g. designing unattractive website issue, online unattractive product photos issue, unclear website color issue, unclear website advertisement message, contents and words impressions issue, lacking image movement frequent attractive seeing issue etc. different website factors. Thus, online digital channel will be one good choice to apply (AI) learning machine to help businesses to predict consumer behaviors.

Artificial Intelligence And Marketing Research Difference To Predict Consumer Behaviors

Artificial Intelligence And Marketing Research Difference To Predict Consumer Behaviors
Author: Johnny Ch Lok
Publisher:
Total Pages: 184
Release: 2019-12-22
Genre:
ISBN: 9781679249341
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To apply (AI) learning machine technology to understand customer online purchase behavior, it will raise business e-commerce successful chance: For example, (AI) learning machine can help businesses to gather data to analyze to determine whether short-term or long-term signals in the online consumer behavior that indicate higher purchase intents to let every online business to know. (AI) learning machine can find that online users with long-term purchasing intent tend to save and click through on more content.However, as online users approach the time of purchase their activity becomes more topically focused and actions shift from saves to searches from online consumption channel. Then, (AI) learning machine will further find that the brand product purchase signals in online behavior can exist weakness before an online purchase is made and can also be traced across different online purchase categories. Finally, (AI) learning machine synthesize these insights in predictive models of online user purchasing intent to the brand of product. Taken together, it's work identifies a set of general principles and signals that can be used to model online user purchasing intent across many online content discovery applications. Thus, (AI) learning machine can help online businesses to gather any online users' click online behaviors data to judge whether there are how many online users will choose to find their online business websites to make final decisions to buy their products from online channels. Then, it will give opinions to help the online businesses to let it to judge whether what are the important website factors will help its online business to attract many online consumers, e.g. designing unattractive website issue, online unattractive product photos issue, unclear website color issue, unclear website advertisement message, contents and words impressions issue, lacking image movement frequent attractive seeing issue etc. different website factors. Thus, online digital channel will be one good choice to apply (AI) learning machine to help businesses to predict consumer behaviors.Can apply artificial intelligent learning machine " big data" gathering method to predict manufacturers' behavioral performance ?