Artificial Intelligence Influences Marketing Development

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 businesses to predict consumer behaviors. For example, one of the most common techniques is machine learning, where algorithms are used to perform tasks by learning from historical data. Another growth branch of (AI) is natural language procession.However, during 2017 year, search engines will begin to factor additional behavioral data into prediction of customer behavioral results, such as the user's history of searches and locations and previously captures conservations. Artificial intelligence will use this information to power predictive search results, e.g. predictive future consumer's choice behavioral processing for any kinds of businesses.Predictive search will improve the quality of search results, and provide new insights into consumers' behavior and the moments which matter to them. Search will give recommendation into tailored how consumer individual choice in consumption process. Several of the largest online platforms already use machine learning to improve predictive consumer behavioral search results. For example, Google's rank brain technology adds research by understanding the context in which the 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. 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. For another example, in 2016 year, Apple computer revamped its photos app to allow consumers to search for specific items in the phots, they want to find, not just dates and locations. Each photo that an intelligent phone or intelligent pad user takes goes through 11 billion computations, so that photos can understand exactly what is the photography.It seems that in future, (AI) machine learning will allow search to evolve even further.

How do you think Procter & Gamble has been able to increase its market share at a time when competition from supermarkets' own-label brands has intensified?(AI) can help it to gather data concerns that what are the factors to cause it's brand can build large market share in short time. Then, it can concentrate on keep it's internal strengths and attack it's internal weaknesses or attack it's external threats and keep it's external opportunities in ord ero keep its competitive effort in market long term.Procter & Gamble operates mainly low value consume products, such as household detergents are among the most competitive and building successful brand is key to long term profitability. Differentiating one product from another in the minds of consumers can be extremely difficult, with one packet of detergent looking very much like another and performing similarly. It seems that it can not be unique to sell in supermarket. However, then it innovated new liquid soap products, it seemed to adopt to change in consumer preference, and maintaining consistent standards when exploiting new market opportunities. Adrian, P. 2012) showed that Fairy liquid was rated as Britain's number one cleaning brand by Marketing magazine and in 2010 accounted for 3 percent share of the UK washing up liquid category by value. The brand has been a regular household feature since the name first appeared in 1898 year on a bar of soap. P&G first launched Fairy liquid in the UK market I think Procter & Gamble (P&G) has this marketing strategy to supply its soap products to supermarket retailers. A supermarket is not only supply to likely to encounter a massive range of products, such as food, drink, homecare, personal care, luxury products etc. Consumers can see categories and see how much the offering changes, the range, the packaging, the branding and advertising or promotion of any brand products sale at shelves. Hence, such as P&G manufacturer in 1960 year. At the same time, the market for washing up products was still in its infancy, with most consumers using solid soaps, and only 17 per cent of households using liquid soap. But P&G gained most from a change in consumers' habits. It educated the public of the benefits of using washing up liquid. The launch of Fairy liquid soap products involved distributing 15 million trial bottles to about 85 per cent of household in the UK. Creating early awareness and trial of the Fairy liquid soap innovative products led to Fairy gaining a market share of 27 per cent by 1969 year. It had a proud positioning as a slightly more expensive product which is better value and worth. So, it created brand values of a soft, caring, homely image by advertisement promotion. It also attempted to adopt in response to changing attitudes, for example, a commercial in 1994 year for the first time used a father instead of a mother at the kitchen sink. During the first twenty years of the brand's life, product innovation had been relatively modest. However, an increasing competitive market, customers have forced P&G to innovate in order to maintain and strength its market share.

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.

The marketing industry is being disrupted right before of our eyes, and the new technological revolution will transform our world. Artificial intelligence is set to change everything we know about marketing and how we interact with customers. Business leaders need to pay attention, or they risk being left behind. Artificial intelligence will drive a massive shift in business models, and will profoundly change how businesses and customers communicate. Staying at the forefront of these changes is essential for any business to stay competitive. Data-Driven Marketing with Artificial Intelligence is the definitive guide to understanding and using AI in marketing. It is essential reading for corporate and marketing leaders, and anyone seeking to understand how artificial intelligence will lead us into the world of tomorrow. Featuring discussions with dozens of industry leaders, it provides both an overview of how AI will continue to affect online marketing, and details of how to implement these new tools into your business. Learn how traditional marketing strategies are being replaced by autonomous, data-driven, and self-optimizing systems capable of providing more relevance to each customer, improving loyalty, and ultimately increasing the bottom line. After reading this book, you will understand these key topics: * The disruption that artificial intelligence and other emerging technologies will have on marketing, sales, and the industries that surround them * The latest AI-based software tools and what they can do for marketers today * Using big data, predictive analytics, and machine learning in marketing * How to develop and implement your own custom AI software * The risks AI hold for your job or business * How new technologies, beyond AI, will disrupt marketing even further * The legal and ethical aspects of using artificial intelligence systems Leading experts and technology CEOs believe that soon, nearly every decision we make will be influenced by AI technology. Marketing is no exception, and it will see changes faster and with wider adoption than any other. In an AI-enabled world, companies will have to adjust to new purchase patterns to stay in business. Learning about and implementing AI tools will keep your business on the forefront of the next technological revolution--while the rest play catch up. Buy the book now to jump into the world of artificial intelligence and stay one step ahead of the competition!

Business shapes have been changed these days. Change is the main dominant fact that change the way of business operations running. Topics such as innovation, entrepreneurship, leadership, blockchain, mobile business, social media, e-learning, machine learning, and artificial intelligence become essential to be considered by each institution within the technology era. This book tries to give additional views on how technologies influence business and marketing operations for insuring successful institutions survival. The world needs to develop management and intelligent business scenario plans that suite a variety of crisis appears these days. Also, business and marketing intelligence should meet government priorities in individual countries and minimise the risk of business disruptions. Business intelligence - the strategies and technology companies that use it to collect, interpret, and benefit from data - play a key role in informing company strategies, functions, and efficiency. However, being essential to the success, many companies are not taking advantage of tools that can improve their business intelligence efforts. Information technology become a core stone in business. For example, the combination of machine learning and business intelligence can have a far-reaching impact on the insights the company gets from its available data to improve productivity, quality, customer service and more. This book is important because it introduces a large number of chapters that discussed the implications of different Information technology applications in business. This book contains a set of volumes which are: 1- Social Marketing and Social Media Applications, 2- Social Marketing and Social Media Applications, 3- Business and Data Analytics, 4- Corporate governance and performance, 5- Innovation, Entrepreneurship and leadership, 6- Knowledge management, 7- Machine learning, IOT, BIG DATA, Block Chain and AI, 8- Marketing Mix, Services and Branding.

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.

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.

Information economists suggest that both buyers and sells have an incentive to hide or reveal private information, and these incentives are crucial for market efficiency. Data technology that reveals consumers type could facilitate a better match between product and consumer type, and data technology that helps buyers to assess product quality could encourage high quality production. Thus, (AI) big data technology can also assist consumers to gather different manufacturers' data to compare what their advantages and disadvantages of their products are. Then, consumers can make comparison to choose which brand of product is the suitable to whom to buy in these more choice consumption market. (AI) learning machine will gather similar brand their products' data to analyze to make conclusion to let consumers know or feel to make final judge to find what advantages or disadvantages of these sample brands of similar products' comparison from internet. On the other hand, it means that manufacturers can gather consumers' past purchase behaviors or purchase experience from (AI) big data gathering method to record and analyze to give opinions to let manufacturers to know what reasons or factors influence consumers choose not to buy their products from internet.(AI) big data gathering consumer behavior prediction method can give these benefits to manufacturers and consumers both, such as: New concerns arise because (AI) technological advance which have enables reducing cost of collecting, storing, processing and using data in mass quantities extend information beyond a single transaction. These advances are often summarized by the big data, it means charge volume of transaction-level data that could identify individual consumers by itself or in combination with the datasets.The popular (AI) takes big data as in input in order to understand, predict and influence consumer behavior. Modern (AI) is used by legitimate companies, could improve management efficiency motivate innovations and better match demand and supply. But (AI) in the wrong hand, also allows the mass production of fraud and deception. Since, data can be stored, traded and used long after the transaction. Future data use is likely to grow with data processing technology, such as (AI) big data gathering consumer and manufacturer behavioral prediction method from internet channel. Thus, future (AI) big data learning machine can also help consumers to choose the best brand of manufacturer's products among different brands of manufacturers products choice to compare their past sale performance from internet. They can apply (AI) big data statistic method to gather all different manufacturers' similar products past sale data to compare their advantages and disadvantages to make the best decision to choose to buy which brand of product is the most suitable to them to buy to use. It seems (AI) big data can also help consumers to predict any manufacturers' manufacturing behaviors or manufacturing performance whether they are improving their product quality or are deteriorating their product quality. Thus, (AI) big data tool is also important to help customers to predict future the different brands of manufacturer performance will have improvement in possible.