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With the progress of Internet technology and the rapid development of artificial intelligence, intelligent technology products rapidly grow up. However, influenced by many factors, the marketing status of this kind of products in recent years is not ideal, for example, big data leakage and other issues. Especially in recent years, because of the greater market competition, the marketing of intelligent technology brands is facing greater problems. The purpose of this study is to analyze the current situation of brand marketing of intelligent technology under the background of artificial intelligence and propose the optimization strategy of brand marketing of such products, so as to promote the continuous improvement of brand marketing effect of intelligent technology. Based on artificial intelligence and the concept of brand marketing, through intelligent technology brand marketing investigation and experiment, the intelligent technology brand marketing status and current problems are analyzed; from marketing, product type, and product price, three angle analysis of the factors that influence the marketing and Internet marketing, product quality, product pricing, and product development is put forward from the four angles of specific strategy of optimization of smart technology products’ brand marketing. The experimental results show that the year-on-year growth rate of smart technology brand turnover is decreasing at the present stage. The increase rate of the turnover in 2019 is only 3.24%, far lower than that in 2015, and the growth rate of the turnover decreases by about 33%. At present, there are many problems in the marketing strategy of intelligent technology brands, and the optimization of marketing strategy is an urgent problem to be solved.
The new business challenges in the B2B sector are determined by connected ecosystems, where data-driven decision making is crucial for successful strategies. At the same time, the use of digital marketing as a communication and sales channel has led to the need and use of Customer Relationship Management (CRM) systems to correctly manage company information. The understanding of B2B traditional Marketing strategies that use CRMs that work with Artificial Intelligence (AI) has been studied, however, research focused on the understanding and application of these technologies in B2B digital marketing is scarce. To cover this gap in the literature, this study develops a literature review on the main academic contributions in this area. To visualize the outcomes of the literature review, the results are then analyzed using a statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. The research results classify the types of CRMs and their typologies and explore the main techniques and uses of AI-based CRMs in B2B digital marketing. In addition, a discussion, directions and propositions for future research are presented.
In the e-commerce and financial industries, AI has been deployed to achieve better customer experience, efficient supply chain management, improved operational efficiency, and reduced mate size, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving customers while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the food industry are being developed at the moment. This article discusses machine learning and artificial intelligence applications in e-commerce, corporate management, and finance. Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.
YouTube and TikTok have gained increasing recognition as social network sites to support online knowledge acquisition, sharing, and application via social media platforms in the medical field. This study examines which aspect of TikTok and YouTube stimulates doctors, nurses, and any other YouTube and TikTok in the medical setting, to rely on them as sources of knowledge acquisition and sharing to keep their medical repertoire updated. A hybrid model is designed to investigate users’ acceptance of YouTube and TikTok as social media platforms. The model focuses on four main external factors: content richness, innovativeness, satisfaction, and enjoyment. These factors are connected with two TAM constructs which are perceived ease of use and perceived usefulness. The results have shown that both YouTube and TikTok are affected by PEOU, PU, personal innovativeness, flow theory, and content richness. Both social media networks provide up-to-date sources described as useful, enjoyable, and relevant. Nevertheless, the comparative results have shown that YouTube has deeply influenced users’ medical perception and knowledge compared to TikTok. It is created for the very mere purpose of socialization and selfexpression. In contrast, YouTube is used for educational and non-educational purposes due to the type of uploaded content and time management. Therefore, TikTok developers and influencers should initiate highly specialized videos and create content that raises awareness of medical field
issues.
Artificial intelligence (AI) is (re)shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications (will) provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles , the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being.
New technologies such as virtual reality, cloud computing, big data, augmented reality, motion-sensing interaction, and artificial intelligence has brought new changes to digital marketing communication talent cultivation. Students should be trained to master the knowledge of artificial intelligence applications and have a broad international vision.
Firms are developing AI-enhanced products (e.g., robots) that can tackle environmental problems through autonomous interactions with their surroundings (e.g., removing waste/pollutants, tracking invasive species) and autonomous learning, which results in improved environmental performance characteristics. Such autonomous environmental benefits of products differ from conventional, static environmental benefits, which derive from pre-purchase processes and design decisions. However, the literature still lacks knowledge of how to use such autonomous environmental benefits to attract new customers. Therefore, drawing on signaling theory, this study examines the effect of these environmental benefits on a consumer’s purchase intent and its variation across types of consumers, locations, and products. Based on hierarchical linear modeling of 1635 consumer evaluations of AI-enhanced products, this study finds that both static and autonomous perceived environmental benefits influence purchase intent positively. The effect of autonomous environmental benefits is stronger for women than for men and for products targeted at adults rather than children. The effect of static environmental benefits is stronger for men than women, for products targeted at children rather than adults, for consumers with a higher need for cognition, and in locations with a higher perceived environmental well-being.
Information technology and its implications have shown significant developments in the last decades. The internet revolution and digital marketing have created a shrinking force on mass media advertising. They have formed the backbone of personalisation, marketing automation, neuromarketing, viral marketing, voice recognition, and conversion optimisation, and thus, Artificial Intelligence (AI) has risen as the phenomenon in marketing activities. Theoretical analysis of relevant researches on AI tools and future trends, presentation of conceptual framework and methodology design, and presentation of the research findings are the objectives of the research. This study has potential limitations such as the data collection process and access to the relevant literature. AI for marketing has already become a significant part of today’s competitive world, which utilising the marketing opportunities to obtain business goals and create breakthrough using AI. Hence, this paper aims to illustrate the theoretical review on the nexus between AI and marketing, which would lead to understanding the future scope of AI and its penetration in marketing activities.
The technological evolution of recent years has put the industries on the move. Marketing has reached a point in its evolution where adapting to digital trends is an imperative. Although it seems to be a push for marketers, in fact, all automated applications and systems that are based on artificial intelligence only diminishes the complexity of classic targeting and customization processes. In many situations, the platforms used for online promotion contain algorithms for identifying the best combinations, in other situations the companies take initiative to develop and implement in-house customized systems. The authors aim to describe the current state of artificial intelligence in marketing processes and to provide a four step sequential model that uses an intelligent marketing solution that can improve the visibility of a website through keywords.
Purpose:
To examine the impact of artificial intelligence-assisted social media marketing (AISMM)on the performance of start-up businesses of small and medium enterprises (SMEs) in Saudi Arabia.
Design/methodology/approach:
A survey technique was employed whereby primary and secondary data was collected, analyzed, and interpreted. Participants involved business operators or employees of start-up businesses and SMEs in the Saudi Arabian context. Data were analyzed by using partial least square-structure equation modeling (PLS-SEM).
Findings:
AISMM, which exhibits an increasing trend among start-up businesses and SMEs in Saudi Arabia, accounts for an overall increase in the number of customers and customer bases—and an additional tertiary effect of increased profitability. AISMM increases the effective business management and SMEs performance (SMEP). Moreover, effective business management increases the SMEP Originality/value: This study is quite unique as it is investigated that AISMM practices has significant role to enhance SMEP in which effective business management playing a mediating role.
Implications:
The practitioners can get help from this study to increase the performance by decreasing various problems of marketing by using AISMM.
Artificial Intelligence (AI) is changing the healthcare world: machine learning and deep learning software will be more and more used to identify diagnosis and treatment. However, it is difficult to prefigure health professionals’ attitudes and behavior towards these new technologies, especially when doctors have to take sensitive medical decisions on the basis of a “black box” process. This contribution describes two conflicting solutions: eXplainable Artificial Intelligence (XAI) or developing transparent AI-human interface vs. AI marketing that will promote positive attitudes in health professionals and reduce their critical thinking (e.g., “it is normal to use technologies you don’t know how they work!”). We argue that the first way, namely development of AI able to collaborate with human doctors, is not only the more ethical option, but also the one affording authentic, desirable evolution of medical practice.
Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.
Artificial intelligence is the simulation of human intelligence processes by machines, which covers a huge range of capabilities such as voice/image recognition, decision making, machine learning techniques and semantic search. Through secondary research, this paper aims at educating marketers on the present and future potential of artificial intelligence by providing some real-world examples of few early-adopting firms and connecting them with AI powered technologies that can improve marketing performance and transform their businesses. Through primary research, this paper aims at studying the impact of artificial intelligence on the overall marketing landscape. In the end the paper makes a modest attempt to identify those sectors which have shown good acceptability for AI in marketing and the ones which will benefit the most.
This paper describes the current and potential relationship between digital marketing and artificial intelligence (AI), proposing, at the same time, ways of artificial intelligence (AI) engagement in app development. As a genuine branch of Marketing science, digital marketing managed to create value to the organizations and increased the engagement with the customers through electronic services. Digital era has helped industries monitor their procedures including branding, promotion, advertising, production, channel distribution etc. Based on gathered data, interactive customer experience and a digital overview of procedures and sales, business managers could make more accurate and data driven decisions. Due to the excessive amount of data which is daily generated customers journey and experience turn to become extremely complicated. Organizations invest high budgets to cover the lack of information or the potential customers which have never been mapped. The large volume of data generated lead to a chaotic environment which marketer must handle. Users data daily change and decision makers must deal with this reality. The need of use smart applications within organizations emerges to better analyze, classify, optimize and target audiences. Technology aware customers lead industries to bigger financial investments and sophisticated solutions. Based on a high complex data world, marketers must identify their needs and search for advanced technological solutions. Business world manage to implement smart apps which directly affect marketing world and decision makers. Intelligent data-based driven models could lead to customer action predictions based on dependent variables of interest. Data mining, artificial intelligence (AI), machine learning, deep learning could act complementary to marketing science. User profiling, data classification, content optimization, optimized targeted audiences, predictive models, search engine ranking factors optimizations are some of the benefits that artificial intelligence (AI) could provide and generate highly accurate results
This paper maps and describes the current and potential relationship between two different scientific branches of marketing science and computer science. It examines the interaction of digital marketing and artificial intelligence (AI) in academia, proposing, at the same time a machine learning model that could fit in several aspects of digital marketing scientific area. There are numerous scientific publications regarding artificial intelligence (AI) across the disciplines. However, refer-ring to digital marketing aspects, this number remains small. Scientific research on artificial intelligence (AI) could benefit marketing science in numerous ways. Nowadays, only a small amount of scientific research referring to digital marketing and artificial intelligence (AI) is related to specific digital marketing methods. Most of the scientific research examines generic aspects such as e-business, consumer behavior, e-commerce strategies, social media advertisement, search engines and consumer predictive modeling avoid being more related to specific marketing issues in which business world is more aware of, like consumer behavior on social media, targeted advertisements, social media marketing, conversion optimization, predictive models in online purchases, chatbots etc. Despite the vast research area and a certain number of publications, it seems that there is a lack of scientific publications regarding specifically digital marketing and artificial intelligence (AI). Never the less, there are some very extensive research attempts on specific digital marketing matters and artificial intelligence (AI) that are promising. This paper through the mapping of the current state of artificial intelligence (AI) applications on digital marketing scientific area, highlights the corner stone publications, comes up with areas of absence or lack of its presence, mentioning at the same possible reasons why that occurs and provides a machine learning model that could fit in several digital marketing occasions.
Artificial intelligence (AI) impacts numerous aspects of life in the form of smart devices and smart applications, designed to understand consumer behaviour, needs, and preferences in order to deliver customized experiences. AI has been one of the primary drivers of innovation in marketing. Market-ers are already leveraging the advantages of AI to gain valuable insights into customers, competitors and markets. Besides, AI automate tasks, reduce costs, and improve workflows. This paper examines the current and potential applications of AI within marketing by providing comprehensive overview of existing academic research.
Global trends are shaped through disruptive innovation and the new technology of Artificial Intelligence (AI) represents a big part of it. The aim of the research paper is to assess the impact of AI on global trends, by offering a macro-perspective upon the industry environment, economy and employment and a micro-perspective upon finance, marketing and management. The practical approach is conducted through qualitative methods of research, based on purposive sampling. The effects of AI in a company once it is deployed will be explored in-depth, i.e. the different make-up of the workforce population, a flattened hierarchy and the need for employee democracy etc. The research paper will conclude by emphasizing how machines can help people thrive by creating an AI-friendly environment for people and a people-friendly environment for AI
AI, robotics, and machine learning are impacting the field of sales and marketing in an unprecedented way. A perfect storm is brewing! On one hand, online retail stores like Amazon are crushing the bricks and mortar stores. Sales and marketing professionals in bricks and mortar stores are facing a grim future. On the other hand, AI, robotics, and machine learning are replacing sales and marketing professionals in online stores. In fact, salespersons and marketers are predicted to be among the first to be replaced by robots. In a face-to-face environment, human may still prefer to interact with another human. In a virtual environment or a non-face-to-face context, human would care less about who or what is on the other end of the line or responding to his/her online queries. This research will be a longitudinal case study to analyze the evolving nature of sales and marketing field.
Purpose: In this invited paper, the authors aim to offer an integrated marketing communications (IMC) framework for understanding how disparate customer touchpoints impact consumer engagement and profitability in an omni-channel environment. For each aspect of the framework, the authors recommend areas for further research.
Design/methodology/approach: The authors review literature linking personal and electronic channels of communication in an omni-channel context to consumer engagement, with an emphasis on channel and message unity.
Findings: Five major research areas were identified: research that better links omni-channel and IMC theory and practice; conceptual and empirical research that helps operationalize the consumer-brand engagement construct, including its antecedents and consequences; build understanding of off- and on-line consumer-brand touchpoints and how they may enhance engagement and profitability; how omni-channel IMC best monetizes buyer-seller relationships; and omni-channel IMC in other consumer decision contexts.
Practical implications: The emergence of omni-channel marketing is breaking down the silos across available consumer-brand touchpoints. The intersection of effective omni-channel marketing and IMC strategic and tactical initiatives offers marketers an opportunity to engage their customers and to form pro table relationships.
Originality/value: The authors proposed an omni-channel IMC Framework and a research agenda for advancing the field. As this is a new area of inquiry, the authors argue for the development of other comprehensive frameworks and general omni-channel IMC conceptualizations.
Today, Internet and the Web platform have a remarkable impact on making information more reachable and transformable. This form of digital, brave world has influenced the life greatly. Because of this, functions of the digital world have been started to be used in many different works and tasks performed as especially people-oriented. Marketing is one of the trendiest works and nowadays, the concept of content marketing has taken a place in the heart of digital world supported marketing approaches. Currently, there is a remarkable research interest in this new form of digital marketing process, which aims to improve attractiveness of content via effective digital objects that may be useful over popular communication environments like social media. Objective of this study is to focus on the content marketing and point an alternative improvement way, which is based on an important research field: artificial intelligence. The study introduces some example models of intelligent content marketing approaches in order to improve functions of content marketing more and more, thanks to the artificial intelligence currently shaping the modern life of the future.
Knowledge-based information systems are advanced tools in the hands of the marketer, enabling him to take evidence-based decisions in complex situations. In this paper, advanced data analysis, neural networks and knowledge representation technologies are brought together towards an intelligent information system for tourist destination marketing. In previous work, knowledge engineering methods were proposed for the extraction and modeling from market survey data of factors, associations, clusters and hidden patterns that explain a market phenomenon or customer behavior. The feasibility of managing these findings in a Knowledge-Base, as reusable, sharable and machine understandable knowledge was shown using preliminary results from primary surveys on the tourism of Thessaloniki. In the current work, we present the continuation of these developments, including: (a) the final results of the survey on the tourism of Thessaloniki, (b) a refined Knowledge Base filled with real and validated content derived from the analysis of the full-scale survey data, (c) the extension of the methods with an artificial neural network classifier and (d) the deployment of an inference engine and a query mechanism in order to exercise the knowledge content for decision support. Pilot trials showed that the intelligent system was able to assist users who are not experts in analysis to solve typical destination marketing problems.
Purpose
The purpose of this study is to determine which digital marketing strategies are preferred by Millennials and are effective in influencing their behavior. There is potential growth and value in using digital marketing aimed at Millennials, but the marketing strategies must be perceived positively by this online generation.
Design/methodology/approach
Data were collected over a three‐year period (2009 to 2011) by surveying Millennials at a prominent southwest US university.
Findings
Results indicate that Millennials have a definite preference for certain forms of online advertising. There are digital marketing strategies that are considerably more effective in grabbing the attention of Millennials, motivating repeat visits to a web site, and encouraging Millennials to write online reviews.
Practical implications
By taking into consideration the preferences of Millennials, the effectiveness of online communications and digital marketing aimed at this market segment can be increased. Recommendations are made regarding types of online advertising to use, how companies should design their web sites, how to motivate repeat visits to web sites, and how to encourage Millennials to write online reviews. Millennials are reading and being influenced by online reviews.
Originality/value
Even though online advertising is the fastest growing advertising medium, only a small percentage of total advertising expenditures are currently being devoted to the internet. There is potential growth and value in using digital marketing aimed at Millennials, but the marketing strategies must be perceived positively by this generation.
In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions. This paper shows how marketing educators can introduce AI and ML concepts in their marketing classes and incorporate a cloud-based platform (AzureML Studio) by teaching students to create ML models for customer churn prediction. The results showed that the assignment improved student’s learning. The students also reported other positive outcomes as reflected in the perceived career preparation, traditional learning goals, use of time, and overall satisfaction.
Artificial intelligence (AI) is globally acknowledged as an innovative
technology. Today, many corporations and individuals are making an effort to harness the capabilities of AI in almost all sectors viz. healthcare, education, manufacturing, smart cities, agriculture, etc. The concept of ‘Smart Factories’ and ‘Industry 4.0’ has prompted many global enterprises to use automation and intelligent robots to improve manufacturing and enhance the quality of the finished product and overall productivity. Indeed, artificial intelligence is a vital tool to augment manufacturing by facilitating the R & D, enhancing the quality,
reducing the errors, and maintaining the supply chain by projecting demand forecasting and simulation of outcomes to foster higher margins in a stiff competition. However, the requirement is to build an industry that should be compliant with such disruptive changes and a workforce compatible enough to create a collaborative environment for both men and technology to work productively. Thus to conduct this study, several keywords and their combinations were used and explored using Google Scholar. The relevant articles, papers, and journals obtained were examined, and data pertinent to the study were collected, examined, and aggregated. Furthermore, specific changes in the current policies and the working parameter were also suggested. In this paper, the scope of artificial intelligence and its applications in today's manufacturing sector of India is discussed. Here, the Indian manufacturing sector's present status is focused primarily, the limitations are identified and how they can be dealt with.
Purpose
This paper aims to present an exploration of possible associations between the Jungian archetypes frequently used in marketing and three well-known products based on artificial intelligence (AI), namely, Sophia, Alexa and Articoolo.
Design/methodology/approach
The study conducted emotionalist interviews to gather thick data from 11 participants on how they conceptualize these AI-based products. In the absence of any existing relevant hypotheses, this paper attempts to build theory using a case study approach and qualitative analysis of interview narratives.
Findings
Despite the human attributes ascribed to these products, participants were principally concerned with their purpose, efficiency and the degree of trust which they felt could be accorded to the product. Anthropomorphism emerged as significant with participants making some associations with common archetypes traditionally exploited in marketing and this suggests a possible means of enhancing consumer trust in AI products.
Originality/value
Little research has been conducted on the marketing of AI and this study presents a timely identification of some potentially significant issues. As AI is intended to mimic some aspects of human intelligence, the role of the archetype in creating a personality to enhance trust may prove crucial in securing consumer confidence.
Purpose
The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations.
Design/methodology/approach
To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation using a survey by considering 308 usable respondents.
Findings
The results of this study show that perceived usefulness and perceived ease of use directly impact the behavioral intention of the employees to adopt an AI integrated CRM system in organizations. Also, these two exogenous factors impact the behavioral intention of the employees to adopt an AI integrated CRM system mediating through two intermediate variables such as utilitarian attitude (UTA) and hedonic attitude (HEA). The proposed model has achieved predictive power of 67%.
Research limitations/implications
By the help of the technology acceptance model and motivational theory, the predictors of behavioral intention to adopt AI integrated CRM systems in organizations were identified. The effectiveness of the model was strengthened by the consideration of two employee-centric attitudinal attributes such as UTA and HEA, which is claimed to have provided contributions to the extant literature. The proposed theoretical model claims a special theoretical contribution as no extant literature considered the effects of leadership support as a moderator for the adoption of an AI integrated CRM system in Indian organizations.
Practical implications
The model implies that the employees using AI integrated CRM system in organizations must be made aware of the usefulness of the system and the employees must not face any complexity to use the system. For this, the managers of the concerned organizations must create a conducive atmosphere congenial for the employees to use the AI integrated CRM system in the organizations.
Originality/value
Studies covering exploration of the adoption of AI integrated CRM systems in Indian organizations are found to be in a rudimentary stage and in that respect, this study claims to have possessed its uniqueness.
The fifth industrial revolution is known as Industry 5.0 and is being evolved to focus on the personalised demand of customers. This industrial revolution is required to provide better interaction among human and machines so to achieve effective and faster outcomes. It provides a new era of personalisation and solves complex problems. Digital technologies provide a new paradigm in manufacturing and eliminate repetitive jobs. It applies human intelligence to understand the requirement of a human operator. The data in manufacturing can be analysed using machine learning and artificial intelligence (AI). This paper discusses the development of all industrial revolutions and differentiates between Industry 4.0 and Industry 5.0. Further, it identifies the significant elements and capabilities of Industry 5.0 in the manufacturing field. This paper finally identifies seventeen critical components of Industry 5.0 and discussed them briefly. Intelligent machines used in this revolution are efficiently used to solve real problems. It provides higher accuracy and speeds up the industrial automation with the help of critical thinking of human resources. Industry 5.0 provides computing power to the industry, which is to facilitate the digital manufacturing systems and are built to communicate with other systems. Thus, with mass personalisation, there is customer delight with higher value addition through Industry 5.0.
In this chapter you will see how Artificial Intelligence can support lead prediction and lead profiling to identify look-alike audiences. The possibilities to develop individualized recommendations and media plans based on AI will be discussed, too. A relevant use case of Artificial Intelligence is conversational commerce—leading from voice first to voice only. We can use AI systems for sentiment analyses and for content creation and content distribution, too. The detection of fake accounts and fake news poses a great challenge for AI processes.
"Introduction to Optimum Design " is the most widely used textbook in engineering optimization and optimum design courses. It is intended for use in a first course on engineering design and optimization at the undergraduate or graduate level within engineering departments of all disciplines, but primarily within mechanical, aerospace and civil engineering. The basic approach of the text is to describe an organized approach to engineering design optimization in a rigorous yet simplified manner, illustrate various concepts and procedures with simple examples, and demonstrate their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB are featured throughout as learning and teaching aids. The 3rd edition has been reorganized and enhanced with new material, making the book even more appealing to instructors regardless of the level they teach the course. Examples include moving the introductory chapter on Excel and MATLAB closer to the front of the book and adding an early chapter on practical design examples for the more introductory course, and including a final chapter on advanced topics for the purely graduate level course. Basic concepts of optimality conditions and numerical methods are described with simple and practical examples, making the material highly teachable and learnable.Applications of the methods for structural, mechanical, aerospace and industrial engineering problems.Introduction to MATLAB Optimization Toolbox.Optimum design with Excel Solver has been expanded into a full chapter.Practical design examples introduce students to usage of optimization methods early in the book. New material on several advanced optimum design topics serves the needs of instructors teaching more advanced courses.
This chapter reviews the many new tools the advent of AI agents has placed into the hands of marketers and consumers alike. AI agents are redefining the roles and rules of the marketing game. While once marketing was the interaction between marketing professionals and customers, AI agents are taking key roles on both sides. The chapter shows how Artificial Intelligence Marketing Agents have begun to proffer the marketer unprecedented marketing research and communication capabilities. However, at the same time AI applications are empowering the consumer to bypass or question the corporate marketing message. Virtual Personal Shopping Assistants (VPSAs) can learn consumers' tastes, predict their needs, and optimize their product purchases. VPSAs are able to instantly match a consumer's need against all accessible products that meet the consumer's expectations and price points. The chapter concludes with recommendations for marketers to recognize the primary role that the consumer's VPSA will play in the marketing game and adjust their marketing efforts accordingly.
As its name suggests, computing with words, CW, is a methodology in which words are used in place of numbers for computing and reasoning. The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa. Thus, as an approximation, fuzzy logic may be equated to CW.
There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers. And second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW.
In CW, a words is viewed as a label of a granule, that is, a fuzzy set of points drawn together by similarity, with the fuzzy set playing the role of a fuzzy constraint on a variable. The premises are assumed to be expressed as propositions in a natural language. For purposes of computation, the propositions are expressed as canonical forms which serve to place in evidence the fuzzy constraints that are implicit in the premises. Then, the rules of inference in fuzzy logic are employed to propagate the constraints from premises to conclusions.
At this juncture, the techniques of computing with words underlie -- in one way or another -- almost all applications of fuzzy logic. In coming years, computing with words is likely to evolve into a basic methodology in its own right with wide-ranging ramifications on both basic and applied levels.
This paper discusses the nature of the omni-channel customer and how this is driving change in the physical retail space. It considers the need for new technologies to support store staff and also new metrics to measure store performance.
In today’s maturing consumer markets, emphasis is shifting from straightforward sales to a more holistic approach to customer life cycle management, with a stronger emphasis on how sales are generated and service provided all along the customer journey. Effectively managing these different marketing, sales and service channels poses a signifi cant challenge. Companies need new strategies, structures, processes and tools to deliver customer value across all channels. A multi-channel, integrative customer model that delivers customer value and significant return on investment (ROI) requires both a strong understanding of customer preferences and behaviours and a robust IT architecture that supports the overarching customer relationship management (CRM) strategy. Even those organizations that have embraced the need for sophisticated multi-channel orchestration often still fall short in their execution. We offer strategic guidelines in four areas to ensure success with multi-channel orchestration. First, marketing operations need to explicitly define a channel strategy with respect to customer segmentation, the channel journey (how and where sales and services are delivered) and targeted incentives that reward multi-channel sales and service support. Secondly, marketers need to optimize the online channel, which is fast becoming the primary platform for accessing product and service information, and completing an ever-growing number of transactions. It is also the entry hub to other touch points; customers go online to contact the company or to fi nd store locations or telephone numbers. Thirdly, companies need to build an IT foundation that underpins their CRM strategy. This CRM IT architecture needs to enable the transformation from vertical, single-channel operations to true horizontal business processes that deliver cross-channel integration. Fourthly, marketers can learn by example. Companies across the consumer products spectrum are experimenting with the multi-channel experience. Marketers should broadly consider the best practices that they could adapt to their own industry.
Purpose
The purpose of this paper is to present the augment of the authors, who are IBM consultants, that companies need to meld social media programs with customer relationship management (CRM). This new paradigm – Social CRM – recognizes that instead of just managing customers, the role of the business is to facilitate collaborative social experiences and dialogue that customers value.
Design/methodology/approach
Social media holds enormous potential for companies to get closer to customers and, by doing so, increase revenue, cost reduction and efficiencies. However, using social media as a channel for customer engagement will fail if the traditional CRM approaches are not reinvented,
Findings
According to IBM research, there is a large perception gap between what the customers seek via social media and what companies offer. Consumers are far more interested in obtaining tangible value, suggesting businesses may be confusing their own desire for customer intimacy with consumers' motivations for engaging.
Practical implications
To reinvent the company's CRM strategy it is important to recognize social media is a game changer and the customer is in control; make the customer experience seamless – across social media and other channels; start thinking like a customer – if one isn't sure what customers value, they should be asked; and monetize social media, if that is what customers want.
Originality/value
Social CRM is a new approach that recognizes consumers have strong opinions about relationships as customers being managed in a social media context and that their willingness to engage with companies should not be assumed or taken for granted.
Expert systems have become the more acceptable face of the much mooted “artificial intelligence” of the 1980s. A survey of UK organizations was undertaken in order to determine the usage rate and the main applications of expert systems. The responses indicate that very little use is being made of the available technology, and that where expert systems are used, they are often utilized in routine roles. Interviews were carried out with 12 high-level managers in medium and large organizations to ascertain the possible results of poor or little use of expert systems and why organizations are reluctant to use them. The consensus is that, on a global scale, UK firms may lose sustained competitive advantage if they do not make the best use of the technology available.
There are three basic concepts that underlie human cognition: granulation, organization and causation. Informally, granulation involves decomposition of whole into parts; organization involves integration of parts into whole; and causation involves association of causes with effects. Granulation of an object A leads to a collection of granules of A, with a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality. For example, the granules of a human head are the forehead, nose, cheeks, ears, eyes, etc. In general, granulation is hierarchical in nature. A familiar example is the granulation of time into years, months, days, hours, minutes, etc. Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques. Crisp IG, however, does not reflect the fact that in almost all of human reasoning and concept formation the granules are fuzzy (f-granular). The granules of a human head, for example, are fuzzy in the sense that the boundaries between cheeks, nose, forehead, ears, etc. are not sharply defined. Furthermore, the attributes of fuzzy granules, e.g., length of nose, are fuzzy, as are their values: long, short, very long, etc. The fuzziness of granules, their attributes and their values is characteristic of ways in which humans granulate and manipulate information. The theory of fuzzy information granulation (TFIG) is inspired by the ways in which humans granulate information and reason with it. However, the foundations of TFIG and its methodology are mathematical in nature. The point of departure in TFIG is the concept of a generalized constraint. A granule is characterized by a generalized constraint which defines it. The principal types of granules are: possibilistic, veristic and probabilistic. The principal modes of generalization in TFIG are fuzzification (f-generalization); granulation (g-generalization); and fuzzy granulation (f.g-generalization), which is a combination of fuzzification and granulation. F.g-generalization underlies the basic concepts of linguistic variable, fuzzy if-then rule and fuzzy graph. These concepts have long played a major role in the applications of fuzzy logic and differentiate fuzzy logic from other methodologies for dealing with imprecision and uncertainty. What is important to recognize is that no methodology other than fuzzy logic provides a machinery for fuzzy information granulation.. :To Didier Dubois and Henri Prade, who have contributed in so many major ways to the development of fuzzy logic and its applications.
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Variously called enterprises resource planning (ERP) systems,
enterprise-wide systems, or enterprise business system, these
comprehensive, package software solutions seek to integrate the complete
range of a business's processes and functions in order to present a
holistic view of the business from a single information and IT
architecture. The critical success factors for ERP implementation
include top management support, a clear business vision and issues
specific to ERP such as ERP strategy and software configuration.
However, some of the more important factors are the issue related to
re-engineering business processes and the integration of various core
processes to the ERP system
As its name suggests, computing with words (CW) is a methodology
in which words are used in place of numbers for computing and reasoning.
The point of this note is that fuzzy logic plays a pivotal role in CW
and vice-versa. Thus, as an approximation, fuzzy logic may be equated to
CW. There are two major imperatives for computing with words. First,
computing with words is a necessity when the available information is
too imprecise to justify the use of numbers, and second, when there is a
tolerance for imprecision which can be exploited to achieve
tractability, robustness, low solution cost, and better rapport with
reality. Exploitation of the tolerance for imprecision is an issue of
central importance in CW. In CW, a word is viewed as a label of a
granule; that is, a fuzzy set of points drawn together by similarity,
with the fuzzy set playing the role of a fuzzy constraint on a variable.
The premises are assumed to be expressed as propositions in a natural
language. In coming years, computing with words is likely to evolve into
a basic methodology in its own right with wide-ranging ramifications on
both basic and applied levels
How digital marketing operations can transform business
- Edelman
Value Creation in Connectionist Artificial Intelligence-A Research Agenda
- Schmidt
Value Creation in Connectionist Artificial Intelligence-A Research Agenda
- R Schmidt
- A Zimmermann
- M Moehring
- B Keller
Adaptation in natural and artificial systems
- J H Holand