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Author: Johnny Ch Lok Publisher: Independently Published ISBN: 9781793111111 Category : Languages : en Pages : 254
Book Description
Part ThreeEconomy And Marketing Predictive MethodChapter OnePsychological method predictsconsumer behavior1.1Can apply economic models solve marketing changing challenges?Economists indicate economic modeling can provide a logical, data to help organize the analyst's thoughts. The model helps the economist logically isolate and sort out complicated chains of cause and effect and influence between the numerous interacting elements in an economy. There are four types of models used in economic analysis: Visual models, mathematical models, empirical models and simulation models.Visual models are simply pictures of an abstract economy: graphs will lines and curves that tell an economic story. It is one kind of micro or macro-economic method to predict consumer behavioral change. Some visual models are diagrammatic such as which flow the income thought the economy from one sector to another ( micro economic environment). It is mathematical model, when it is presented the mathematics are explained what the data analysis is or not. The model does not normally require a knowledge of mathematics, but still allow the presentation of complex relationship between economic variable.For example, the common supply-and demand model is meant to show the effect of inflationary expectations upon price and output. In this application, an increase in inflationary expectations causes demand to shift, raising prices and outputs (macro-economic environment). For another example, a very simple micro-economic model would include a supply function (explaining the behavior of products or those who supply commodities to the market), a demand curve ( explaining the behavior of purchasers) and an equilibrium equation, specifying the simple conditions that must be met if the model's equilibrium is to be satisfied. So, the variables in a model like this represent a type of economic activity (such as demand) or data ( information ) that either determines or is determined by that activity ( such as a price or interest rate variable change activity).Dynamic models, in contrast, directly incorporate time into their structure. This is usually done in economic modeling by this mathematical systems of difference of differential equations. For example, it can use a difference equation from a business cycle model, investment now depends upon changes in income in the past. Time is incorporated into the model. Dynamic models, when they can be used, sometimes better represent the business cycles, because certainly behavioral response and timing strongly shape the character of a cycle. For another example, if there is a delay between the time income is received and when it is spent. A model that can capture the delay is likely to those higher consumption desire to the consumer. It is a micro-personal behavioral consumption predict method. So, the user can experiment with an endless variety of values and assumptions to see whether results obtained are realistic or insightful. Since computers are now powerful and cheaper, the importance of dynamic simulation models should follow the future prediction time, when the consumer income receive and when it is spent to predict how much degree of the consumer's consumption desire in micro-economic view point.Another model to be applied to predict consumption behavior. It is expectations and enhanced model, it includes one or more variables based upon economic expectations about future values. For example, if consumers for whatever reason, expect the inflation rate to be much higher next year, then this year, they are said to have formed inflationary expectations. If numerical values are being used in a model and the current inflation rate is 9%, if they expect inflation to be higher next year, the variable for inflationary expectations might be given be a value if 12% or more.
Author: Johnny Ch Lok Publisher: Independently Published ISBN: 9781793111111 Category : Languages : en Pages : 254
Book Description
Part ThreeEconomy And Marketing Predictive MethodChapter OnePsychological method predictsconsumer behavior1.1Can apply economic models solve marketing changing challenges?Economists indicate economic modeling can provide a logical, data to help organize the analyst's thoughts. The model helps the economist logically isolate and sort out complicated chains of cause and effect and influence between the numerous interacting elements in an economy. There are four types of models used in economic analysis: Visual models, mathematical models, empirical models and simulation models.Visual models are simply pictures of an abstract economy: graphs will lines and curves that tell an economic story. It is one kind of micro or macro-economic method to predict consumer behavioral change. Some visual models are diagrammatic such as which flow the income thought the economy from one sector to another ( micro economic environment). It is mathematical model, when it is presented the mathematics are explained what the data analysis is or not. The model does not normally require a knowledge of mathematics, but still allow the presentation of complex relationship between economic variable.For example, the common supply-and demand model is meant to show the effect of inflationary expectations upon price and output. In this application, an increase in inflationary expectations causes demand to shift, raising prices and outputs (macro-economic environment). For another example, a very simple micro-economic model would include a supply function (explaining the behavior of products or those who supply commodities to the market), a demand curve ( explaining the behavior of purchasers) and an equilibrium equation, specifying the simple conditions that must be met if the model's equilibrium is to be satisfied. So, the variables in a model like this represent a type of economic activity (such as demand) or data ( information ) that either determines or is determined by that activity ( such as a price or interest rate variable change activity).Dynamic models, in contrast, directly incorporate time into their structure. This is usually done in economic modeling by this mathematical systems of difference of differential equations. For example, it can use a difference equation from a business cycle model, investment now depends upon changes in income in the past. Time is incorporated into the model. Dynamic models, when they can be used, sometimes better represent the business cycles, because certainly behavioral response and timing strongly shape the character of a cycle. For another example, if there is a delay between the time income is received and when it is spent. A model that can capture the delay is likely to those higher consumption desire to the consumer. It is a micro-personal behavioral consumption predict method. So, the user can experiment with an endless variety of values and assumptions to see whether results obtained are realistic or insightful. Since computers are now powerful and cheaper, the importance of dynamic simulation models should follow the future prediction time, when the consumer income receive and when it is spent to predict how much degree of the consumer's consumption desire in micro-economic view point.Another model to be applied to predict consumption behavior. It is expectations and enhanced model, it includes one or more variables based upon economic expectations about future values. For example, if consumers for whatever reason, expect the inflation rate to be much higher next year, then this year, they are said to have formed inflationary expectations. If numerical values are being used in a model and the current inflation rate is 9%, if they expect inflation to be higher next year, the variable for inflationary expectations might be given be a value if 12% or more.
Author: Johnny Ch Lok Publisher: Independently Published ISBN: 9781793284693 Category : Business & Economics Languages : en Pages : 254
Book Description
The (AI) deep-learning technology leads to performance enhancement and generalization of artificial intelligent technology. It influences the global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems, such as climate change. So, it will help agriculture farming businesses can raise any plant food: vegetable, fruit, rice which grow up very easily if farmers can apply (AI) deep-learning technology to solve environment problems to influence their plant food grow. If the whole year seasonal change is very good and it is suitable for any plant food to grow in farming land easily, e.g. rain is enough and soil is enough for any plant food to grow in the farm lands. Then, fruit, rice, vegetable etc. agriculture businesses will have much beneficial attribution to global farmers. The question is how to use deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network ( RNN model). To certify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial necessary network models. For example, the RNN model predicts the pro-environmental consumption index better than any other model. we expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly as the volume of data grows. So, deep-learning technologies could be useful in environmental forecasting to prevent damage caused by climate change to influence any rice, vegetable, tomato, potato, fruit etc. different plant food grow in any countries' farming land easily.For South Korea example, over 800 government agencies spent 2.2 trillion Korea won on eco-products in 2014 year. However, green products are rarely purchased outside these agencies. This phenomenon occurs because there is a gap between consumer attitudes and behavior, that is environmental attitude is a major factor in decision making vis-a-vis the consumption of " green" food and services ( Jorea Ministry of Environment, 2015). Therefore, it is necessary to understand those consumer attitude, that will lead to sustainability-conductive behavior and consumption.2.1Environmental consumption predictionRecently, many researchers have studied pro-environmental consumption and household indexes as well as suicide rate predictions using messages posted by internet users on Google trend, Tweets etc. channel. Whether can environmental consumption be predicted by (AI) deep-learning technological internet channel? How can impact the pro-environmental consumption attitudes of green policies? Korea scientists estimated pro-environmental attitudes using search query data provided by Google trend and confirmed through regression analysis, that pro-environmental attitude has a positive correlation with the pro-environmental attitude index. They also explained that environment-friendly attitude of residents plan an important role in policy making. In the past, most household consumption indexed were calculated through surveys, but (AI) deep-learning technological tool " big data" have recently gained research attention ( Lee et al. 2016).It seems that (AI) deep-learning technology can help agricultural export countries' farmers, e.g. US, UK, Canada, New Zealand, Australia, Japan, China, India etc. they can predict environmental behavioral consumption to any rice, tomato, potato, fruit, vegetable etc. plant food consumers. The beneficial advantages to them include as below:
Author: K. Sudhir Publisher: Emerald Group Publishing ISBN: 1802628754 Category : Business & Economics Languages : en Pages : 345
Book Description
Review of Marketing Research pushes the boundaries of marketing—broadening the marketing concept to make the world a better place. Here, leading scholars explore how marketing is currently shaping, and being shaped by, the evolution of Artificial Intelligence (AI).
Author: A. K. Pradeep Publisher: John Wiley & Sons ISBN: 1119484065 Category : Business & Economics Languages : en Pages : 272
Book Description
Get on board the next massive marketing revolution AI for Marketing and Product Innovation offers creatives and marketing professionals a non-tech guide to artificial intelligence (AI) and machine learning (ML)—twin technologies that stand poised to revolutionize the way we sell. The future is here, and we are in the thick of it; AI and ML are already in our lives every day, whether we know it or not. The technology continues to evolve and grow, but the capabilities that make these tools world-changing for marketers are already here—whether we use them or not. This book helps you lean into the curve and take advantage of AI’s unparalleled and rapidly expanding power. More than a simple primer on the technology, this book goes beyond the “what” to show you the “how”: How do we use AI and ML in ways that speak to the human spirit? How to we translate cold technological innovation into creative tools that forge deep human connections? Written by a team of experts at the intersection of neuroscience, technology, and marketing, this book shows you the ins and outs of these groundbreaking technological tools. Understand AI and ML technology in layman’s terms Harness the twin technologies unparalleled power to transform marketing Learn which skills and resources you need to use AI and ML effectively Employ AI and ML in ways that resonate meaningfully with customers Learn practical examples of how to reinvest product innovation, brand building, targeted marketing and media measurement to connect with people and enhance ROI Discover the true impact of AI and ML from real-world examples, and learn the thinking, best practices, and metrics you need to capture this lightning and take the next massive leap in the evolution of customer connection. AI for Marketing and Product Innovation shows you everything you need to know to get on board.
Author: Harvard Business Review Publisher: HBR Insights ISBN: 9781633697898 Category : Business & Economics Languages : en Pages : 160
Book Description
Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.
Author: Adam Bohr Publisher: Academic Press ISBN: 0128184396 Category : Computers Languages : en Pages : 385
Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Author: Dwayne Anderson Publisher: Estalontech ISBN: Category : Business & Economics Languages : en Pages : 111
Book Description
Are you looking to optimize Artificial Intelligence in Digital Marketing? Artificial Intelligence in Digital Marketing can be the golden ticket to creator success. Artificial intelligence is a hot topic for businesses. AI proficiencies are mounting the possibilities for how corporations approach real-time engagement with their customers, manage their processes, and make business continuity. As technology advances, corporations are finding new ways to innovate and expand. It is, therefore, no surprise that Artificial Intelligence in Digital Marketing is a growing trend in the online world. It is most definitely the future of digital marketing. It will bring human experiences closer to technology in the future. Creators and Marketers have built businesses and careers on and through optimizing AI in Digital marketing. Using Artificial Intelligence in Digital Marketing will assist brands in delivering a better-quality customer experience, marketing their brand well, and reaching the masses. As per the statistics, • 61% of marketers say artificial intelligence is the most crucial aspect of their data strategy. • 80% of business and tech innovators say AI already enhances efficiency. • Existing AI technology can improve business productivity by up to 40%. • 97% of mobile users are using AI-powered voice assistants • 83% of initial AI adopters have already attained substantial (30%) or moderate (53%) economic benefits AI would make digital advertising and marketing more targeted as well as accurate. It will assist in optimizing campaigns and deliver a better ROI than ever before. There are many reasons to believe that Artificial Intelligence in Digital Marketing is an integral part of today’s world. As a business owner, you must think of new and innovative ways to make people take notice of your products and services. With this awesome and up-to-date AI In Digital Marketing course ,it will enable you to develop a robust Artificial Intelligence Marketing strategy for your organization and create exclusive engagement to stand out, captivate your audience as well as raising profits exponentially. This guide will educate you about how Artificial intelligence is being used towards optimizing digital marketing campaigns by improvising almost all aspects, from understanding the customers well to analyzing the campaign performance. The most common uses of Artificial Intelligence in Digital Marketing are · Automation of the regular tasks and processes · Gain comprehensive customer insights · Understand the latest industry pattern as well as content creation trends · Personalization of marketing communication · Generate, nurture and convert leads Artificial Intelligence in Digital Marketing provides a robust and well-established platform with vast audiences and accessible intellectual machines and tools. It is time that your business should have practical Artificial Intelligence in your Digital Marketing strategy. Just use the tips and information we covered to get started. Using the strategy and information provided in our Mastery Guide, you will master the essentials of creating intelligent machines. So, consider getting our comprehensive and up-to-date guide jam-loaded with the latest and best-in-the-industry knowledge about Artificial Intelligence in Digital Marketing strategy.
Author: Johnny Ch Lok Publisher: Independently Published ISBN: 9781794160682 Category : Business & Economics Languages : en Pages : 254
Book Description
Chapter TwoWhat is (AI) deep learning techniques to forecast environment behavioral consumptionThe (AI) deep-learning technology leads to performance enhancement and generalization of artificial intelligent technology. It influences the global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems, such as climate change. So, it will help agriculture farming businesses can raise any plant food: vegetable, fruit, rice which grow up very easily if farmers can apply (AI) deep-learning technology to solve environment problems to influence their plant food grow. If the whole year seasonal change is very good and it is suitable for any plant food to grow in farming land easily, e.g. rain is enough and soil is enough for any plant food to grow in the farm lands. Then, fruit, rice, vegetable etc. agriculture businesses will have much beneficial attribution to global farmers. The question is how to use deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network ( RNN model). To certify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial necessary network models. For example, the RNN model predicts the pro-environmental consumption index better than any other model. we expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly as the volume of data grows. So, deep-learning technologies could be useful in environmental forecasting to prevent damage caused by climate change to influence any rice, vegetable, tomato, potato, fruit etc. different plant food grow in any countries' farming land easily.For South Korea example, over 800 government agencies spent 2.2 trillion Korea won on eco-products in 2014 year. However, green products are rarely purchased outside these agencies. This phenomenon occurs because there is a gap between consumer attitudes and behavior, that is environmental attitude is a major factor in decision making vis-a-vis the consumption of " green" food and services ( Jorea Ministry of Environment, 2015). Therefore, it is necessary to understand those consumer attitude, that will lead to sustainability-conductive behavior and consumption.2.1Environmental consumption predictionRecently, many researchers have studied pro-environmental consumption and household indexes as well as suicide rate predictions using messages posted by internet users on Google trend, Tweets etc. channel. Whether can environmental consumption be predicted by (AI) deep-learning technological internet channel? How can impact the pro-environmental consumption attitudes of green policies? Korea scientists estimated pro-environmental attitudes using search query data provided by Google trend and confirmed through regression analysis, that pro-environmental attitude has a positive correlation with the pro-environmental attitude index. They also explained that environment-friendly attitude of residents plan an important role in policy making. In the past, most household consumption indexed were calculated through surveys, but (AI) deep-learning technological tool " big data" have recently gained research attention ( Lee et al. 2016).It seems that (AI) deep-learning technology can help agricultural export countries' farmers, e.g. US, UK, Canada, New Zealand, Australia, Japan, China, India etc. they can predict environmental behavioral consumption to any rice, tomato, potato, fruit, vegetable etc. plant food consumers. The beneficial advantages to them include as below:
Author: Jim Sterne Publisher: John Wiley & Sons ISBN: 1119406331 Category : Business & Economics Languages : en Pages : 373
Book Description
A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. This book will not teach you to be a data scientist—but it does explain how Artificial Intelligence and Machine Learning will revolutionize your company's marketing strategy, and teach you how to use it most effectively. Data and analytics have become table stakes in modern marketing, but the field is ever-evolving with data scientists continually developing new algorithms—where does that leave you? How can marketers use the latest data science developments to their advantage? This book walks you through the "need-to-know" aspects of Artificial Intelligence, including natural language processing, speech recognition, and the power of Machine Learning to show you how to make the most of this technology in a practical, tactical way. Simple illustrations clarify complex concepts, and case studies show how real-world companies are taking the next leap forward. Straightforward, pragmatic, and with no math required, this book will help you: Speak intelligently about Artificial Intelligence and its advantages in marketing Understand how marketers without a Data Science degree can make use of machine learning technology Collaborate with data scientists as a subject matter expert to help develop focused-use applications Help your company gain a competitive advantage by leveraging leading-edge technology in marketing Marketing and data science are two fast-moving, turbulent spheres that often intersect; that intersection is where marketing professionals pick up the tools and methods to move their company forward. Artificial Intelligence and Machine Learning provide a data-driven basis for more robust and intensely-targeted marketing strategies—and companies that effectively utilize these latest tools will reap the benefit in the marketplace. Artificial Intelligence for Marketing provides a nontechnical crash course to help you stay ahead of the curve.
Author: Stuart Russell Publisher: Createspace Independent Publishing Platform ISBN: 9781537600314 Category : Languages : en Pages : 626
Book Description
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Author: Johnny Ch Lok
Author: Johnny Ch Lok
Author: Johnny Ch Lok
Author: K. Sudhir
Author: A. K. Pradeep
Author: Harvard Business Review
Author: Adam Bohr
Author: Dwayne Anderson
Author: Johnny Ch Lok
Author: Jim Sterne
Author: Stuart Russell