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Author: Christopher Gan
Publisher:
ISBN: 9781877176814
Category : Banks and banking
Languages : en
Pages : 19
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Book Description
Author: Christopher Gan
Publisher:
ISBN: 9781877176814
Category : Banks and banking
Languages : en
Pages : 19
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Book Description
Author: Jaewoo Joo
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Steven Struhl
Publisher: Kogan Page Publishers
ISBN: 0749479566
Category : Business & Economics
Languages : en
Pages : 273
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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: Manoj Kumar Agarwal
Publisher:
ISBN:
Category :
Languages : en
Pages : 390
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Book Description
Author: Michael D. Henry
Publisher:
ISBN:
Category : Consumers
Languages : en
Pages : 424
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Book Description
Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781792998577
Category :
Languages : en
Pages : 386
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Book Description
Part TwoPsychological method predicts consumer behabiorIntroductionWhat is the standard economic model? It is the standard economic model, the way most economists think about consumer welfare and consumer choice. What is the rationality in the standard economic model? The standard economic model relies heavily on the assumption that consumers are rational. Standard economic model assumes that consumers are fully aware of all the options who have, who can always and consistently, rank whose options in accordance with their preferences, and always choose the option, who like the best option. Thus, what the assumptions of the standard economic model of consumer are? The assumptions include consumers act with full information, consumers have known preferences, consumers choose the best option available.The standard economic model of consumer behavioral prediction method advantages includes: A logically consistent theory of consumer behavior can be built, that theory can be used to make predictions about consumer behavior and those predictions can be compared with reality and those models often correspond to actual behavior of consumption reasons. What is the inconvenient truth? It includes clear evidence from psychology has shown that the rationality assumptions of standard economic model are wrong. Evidence from psychology has shown that consumers often are irrational and also who are predictably irrational. So these are wrong view point to influence how economists judge what cause consumption of behavior. Thus, it beings this question? What is mean of predictably irrational? It means that of irrational consumers were irrational in random ways, who would cancel each other out, leaving the overall outcomes determined by the behavioral consumption of rational consumers. As that case, economic theories that ignored irrational behavioral consumption would work just fine. But, psychology has shown that consumers are irrational in similar and predictable ways. Therefore, irrationality doesn't cancel out and can't be ignored to judge why the behavioral consumption has been caused.
Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781794155756
Category : Business & Economics
Languages : en
Pages : 56
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Book Description
Chapter 2⦁constructive consumer choice process Chapter 2.1⦁How constructive consumer choice process measures which attribute factor(s) can influence consumer chooses to buy any product or food in psychological view. ⦁Main Problem Being Addressed The second aspect problem is judged whether constructive consumer choice is an important process to influence any consumer to choose to buy any products. If it is a real essential choice process, how product manufacturers can reduce their negative emotion is caused to choose not buy their products during this constructive process. The second aspect main problem is researched about consumer will choose to make final decision to buy the best choice of product from among brands of products . Hence, the constructive consumer choice process is a real essential choice process to any consumer generally, when individual consumer needs to compare different brands of products to choose to buy any product. How product manufacturers can predict whose choice method to decide to prefer to make final decision to buy any products in the short days or the short time in the constructive consumer process. If manufacturers can know overall consumers' choice method, manufacturers will not design the not suitable style of products to manufacture to sell and who can know what the overall consumers' negative emotion is influenced who decide not to buy their products from their design style in order to manufacture more suitable style of product design . I shall recommend how manufacturers can predict consumer emotion in the constructive consumer choice process in psychological view.My research is concerned knowledge opinion about how to predict consumer emotion whether what attribute factor(s )are the most influence to the consumer to choose to buy the manufacturer's product in whose process of choice. If manufacturer can early know whether what the most attractive attribute factor(s) are the most influence to the consumer to decide to buy whose product, so who can concentrate on manufacturing the different kinds of products to get the most influence attribute factor to attract consumers to choose to buy whose products. Hence, the manufacturer can know what reasons can cause the consumers who do not choose to buy its products during constructive consumer choice process generally , who can know how to improve its product's design method to concentrate on producing the most acceptable satisfactory level to sell to whose consumers in .Also I believe that the manufacturer can raise sale numbers if who know what bad factors to cause whose consumers choose not to buy whose products during the constructive consumer choice process. It can increase chance to change its product innovative strategy to raise confidence to judge what attributed factors are the important influence to cause consumers who either do not choose to buy or choose to buy its innovative products.
Author: Lyle V. Jones
Publisher:
ISBN:
Category :
Languages : en
Pages : 2
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Book Description
The paper reports an application of a statistical test and multivariate model to the preferences of certain groups of respondents to the Quartermaster Food Preference Survey 006. Perferences for fourteen selected menu items were studied.
Author: Richard R. Batsell
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Moshe Unger
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Research has revealed the potential of recording consumers' eye movements to characterize and predict their decision making. In this paper, we propose the use of a deep learning architecture, called RETINA, to predict multi-alternative, multi-attribute consumer choice from eye movement data. Unlike previous research, RETINA uses the raw eye-tracking data from both eyes as input. It combines state-of-the art BERT and Metric Learning methods which capitalize on the key characteristics of the raw eye-tracking data. Using the raw data input eliminates the information loss that may result from first calculating fixations, deriving metrics from the fixations data and analysing those metrics, as is usually done in eye movement research. While Deep Learning architectures often require very large data sets, using the raw gaze data allows us to apply Deep Learning to eye tracking data sets of the size commonly encountered in academic and applied research. Using a data set with 112 respondents who made choices among four laptops, we show that the proposed architecture outperforms other state-of-the-art machine learning methods (standard BERT, LSTM, autoML, logistic regression) calibrated on raw data or fixation data. Using autoML, we provide an assessment of which features of the eye movement data contribute to the prediction of consumer choice behavior. The analysis of partial time and partial data segments reveals the ability of RETINA to predict choice outcomes well before a decision has been reached. We provide recommendations on how the proposed deep learning architecture can be used to improve marketing.
