Management approaches in the field of smart
Hosein Rahimi kolour; Rahim Mohammad khani
Abstract
The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising ...
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The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising and sales, professionals now have the tools to completely redefine the current understanding of branding, marketing, advertising and sales. The growing popularity of the Internet and the increased use of mobile devices are generating massive amounts of consumer data that feed artificial intelligence-based systems. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive study in terms of its purpose, application, and in terms of data collection. The statistical population of the research was managers and experts in the field of digital marketing and IT in the field of advertising and sales, who were selected using the snowball sampling method. In the qualitative part, the tools for collecting information were library and articles review, interviews, and in the quantitative part, questionnaires. In the qualitative part of the data analysis method, using the theme analysis that was compiled with MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test. According to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales. The findings of the research can have important results for marketers and activists in the field of advertising and sales. Among the consequences of the application of artificial intelligence and machine learning, we can mention things such as understanding, recognizing and revealing consumer needs and desires, classifying target advertisements, intelligent evolution of commercial advertisements, innovation in sales, development of sales channels, and optimization of the fields of using artificial intelligence in advertising agencies Keywords:: artificial intelligence, machine learning, big data, advertising and salesIntroductionMost of the research on the use of artificial intelligence and machine learning in advertising and sales has been done in the last four years. The gap between AI research, the application of AI and machine learning in advertising and sales is still significant. Theoretical findings still need to be supported by real tools and software solutions. In the academic context, most researchers either focus on describing one or two of the newest solutions available on the market or mention very generalized application areas and focus on AI as a phenomenon and the main object of study. There is little research on the results of the general implementation of artificial intelligence in advertising and sales and the results of the implementation of specific artificial intelligence tools. Studies have been conducted on the applications and challenges of the application of artificial intelligence and machine learning in marketing, international marketing and marketing strategies. The innovation of the current study is that despite the exponential development of artificial intelligence and related technologies, its emerging application in various production environments, none of the previous studies have addressed the consequences and results of the application of artificial intelligence and machine learning in a qualitative manner in advertising and sales; Therefore, to cover the issues raised above, we intend to answer the following research question. What areas of artificial intelligence and machine learning are used in advertising and sales? What are the existing solutions based on artificial intelligence and related technologies such as machine learning in the field of advertising and sales development and optimization? Literature ReviewArtificial intelligence is a computer science technology that teaches computers to understand and imitate human communication and behavior. Today, around the world, artificial intelligence has become a hot topic in many sciences and public discussions in society; Because it seems to expand and challenge human cognitive capacity. It is obvious that artificial intelligence will become an integral part of every business organization worldwide in the long run. One of the definitions of artificial intelligence is to teach computers to learn, reason and adapt (Bardo Eritav et al., 2020). Artificial intelligence is supposed to simulate human intelligence in order to support or even expand human abilities (Ote, 2019). In other definitions, the possession of machines with rational and human thinking and action has been emphasized (Berry Hill et al., 2019; Zahouri et al. Moghadam, 2020). Machine learning (ML) is a process that uses observations or data, such as direct experience or instruction, to recognize patterns in data without human intervention, allowing you to make better decisions in the future. The goal of ML is to enable computers to learn automatically "on their own," without human intervention or assistance, so that systems can adjust their actions accordingly. Today, most AI applications use ML in marketing activities, from personalizing product offers to helping discover the most successful advertising channels, estimating churn rates or customer lifetime value, and creating superior customer groups (Tiwari et al., 2021; Shissel et al., 2020). Compared to traditional advertising production, artificial intelligence technology has increased the effectiveness of advertising production and marketing, and has made brand marketing more humane, accurate and effective, and has improved the effectiveness of advertising communications and information call rates. Advertising production using artificial intelligence technology can categorize, combine information sources, quickly generate new ideas, and implement intelligent marketing (Deng et al., 2019). MethodologyThis research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive study in terms of its purpose, application, and in terms of data collection. The tools of data collection in the qualitative part of the library review were articles and semi-structured interviews with 18 managers and experts in the field of digital marketing and IT in the field of advertising and sales, who were selected using the snowball sampling method. The method of data analysis in the qualitative section, using theme analysis, which was compiled with MAXQDA software and using the coding method. In the quantitative part, purposeful sampling with 35 digital marketing experts and information gathering through a questionnaire, the analysis method was based on Kendall's correlation test. ResultsAccording to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales. The findings of the research can have important results for marketers and activists in the field of advertising and sales. Among the consequences of the application of artificial intelligence and machine learning, we can mention things such as understanding, recognizing and revealing consumer needs and desires, classifying target advertisements, intelligent evolution of commercial advertisements, innovation in sales, development of sales channels and optimization of the fields of using artificial intelligence in advertising agencies. Discussion and ConclusionThe rapid development of modern technology, especially artificial intelligence, has led to the creation of powerful solutions to take advertising and sales to a whole new level. With the increased use of social media and the Internet, the amount of data available on customer behavior and customer communication is immense. Although research on the use of artificial intelligence and related technologies is still limited due to the novelty of the topic, this paper reviews existing research on innovation, the use of social media with AI, machine learning, and big data capabilities to provide opportunities to increase advertising effectiveness and Sales have been linked. Using artificial intelligence, it is possible to gain a clearer view of consumer behavior on social media that leads to brand preferences. Artificial intelligence-based systems that work in digital marketing environments focus on machine learning and big data techniques and use data-driven marketing strategies to guide and collect customer knowledge data and evaluate activity performance; Therefore, by using systems based on artificial intelligence and machine learning, facilitate decision-making processes, understanding user behavior and responses, innovation strategies, sales forecasting, understanding social network strategies, customer orientation and optimization of activities and strategic advertising planning in digital environments.Advertising and sales systems based on artificial intelligence can add value to the business, as well as turn the application of artificial intelligence and machine learning in advertising and sales into a sustainable strategy that can guide the steps a company takes to succeed in its marketing strategies, such as content analysis and optimization. social; performance analysis and media selection; Budget analysis and optimization; Identifying and evaluating target groups; Predicting reactions; monitoring the competition, it needs to realize; Therefore, the application and new uses of advertising and sales system based on artificial intelligence seem necessary for companies
Data science, intelligence and future analysis
Mohammad Hasan Maleki; Seyed Morteza Mortazavi; Shahriar Shirooyehpour; Mohammad Javad Zare Bahnamiri
Abstract
AbstractThis research has been done with the aim of developing Iran's banking scenarios with an emphasis on big data. The current research is practical in terms of orientation and exploratory in terms of the goal. It is also mixed in terms of its philosophical, pragmatic and methodological foundations. ...
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AbstractThis research has been done with the aim of developing Iran's banking scenarios with an emphasis on big data. The current research is practical in terms of orientation and exploratory in terms of the goal. It is also mixed in terms of its philosophical, pragmatic and methodological foundations. To carry out the research in the first stage, 20 key drivers of the research were extracted through literature review and interviews with banking and technology experts. After screening with the fuzzy Delphi method, 8 factors were removed and the rest were evaluated with the Marcus decision making technique. The findings of the research show that the two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. According to the findings of the research, considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.IntroductionFinancial innovations has been challenged the banking sector and can improve it. They cover a variety of financial businesses such as online lending, asset management platforms, trading management, mobile payment platforms, etc. All these services generate a large amount of data every day (Hasan et al, 2020: 1). Analyzing this volume of data is difficult, giving rise to the concept of "big data" (Munawar et al, 2020: 2). Big data as one of the important fields of future technology has attracted the attention of various industries (Raguseo & Vitari, 2018: 5206). In general, big data refers to a large volume of structured or unstructured data that is generated and stored at a high speed (Dicuonzo et al, 2019: 41). Big data has found its position in the banking industry; Because of the useful data they have stored in recent years (Rakhman et al, 2019: 1632). Recent applications of big data in banking have been for improving customer relationship management, marketing, optimizing strategic management and human resources (Parmar, 2018: 33; Hassani et al, 2018: 2). Therefore, it can be said that nowadays big data plays a major role in providing financial and banking services, and the realization of its potential benefits in banking is more from technical aspects and affects the organizational structure of banking and mobilizes a large number of different actors (Diniz et al, 2018: 151- 152). With changes in customer expectations and increased competition, the banking industry is no longer able to ignore technological innovations in the banking sector. Due to the numerous applications and benefits of big data in various industries, including the banking industry, and it's becoming more widespread in the future, this technology is becoming a prominent research topic (Phan & Tran, 2022: 6.)Research Question(s)What are the plausible scenarios for banking in Iran with an emphasis on big data? Literature ReviewMany studies conducted in the field of banking and big data deal with the role of big data in improving the performance of the banking industry (for instance: Shakya & Smys, 2021; Gonsalves & Jadhav, 2020; Hung et al, 2020; Parmar, 2018). Also, another part of the studies conducted with a future research approach in the banking sector without focusing on innovative financial technologies and specifically big data (for instance: Baumgartner & Peter, 2022; Eskandari et al,2020). The focus on innovative banking and financial technologies with a Futures Studies approach has been weak (for instance: Maja & Letaba, 2022; Murinde et al, 2022; Hajiheydari et al, 2021; Broby, 2021; Harris & Wonglimpiyarat, 2019). And the role of big data in the Futures Studies of the banking industry has been seen to be very limited due to the relatively large amount of data available in banks and its effect on performance and gaining a competitive advantage (for instance: Valero et al, 2020). Therefore, despite the studies conducted in the field of banking and big data, some of these researches have paid attention to the present time, and the researches conducted in the future of the banking industry have also been without focusing on the role of big data. Now, the most important theoretical gap in research is the lack of studies on the future of banking in Iran with an emphasis on big data. MethodologyThe current research is pragmatism due to the use of qualitative and quantitative methods from the perspective of philosophical foundations. It is also exploratory in terms of purpose due to the identification of drivers and practical in terms of direction due to the application of the results in the analysis of the future of banking in Iran. In the current research, two methods of literature review and interviews with experts are used to identify drivers, both of which are qualitative methods. According to Popper, the interview tool is based on the expert dimension. The literature review is evidence-based and uses articles and scientific texts to identify factors. Fuzzy Delphi, which is semi-quantitative and evidence-based, is used to screen and determine key drivers that require great accuracy. Then, to determine the key uncertainties, the MARCOS technique is used based on the importance and uncertainty indicators of the Global Business Network (GBN) approach, which is a quantitative and evidence-based technique. Finally, interviews with focus groups are used to write the scenario, which is a qualitative method based on the expert dimension. The theoretical community of the research includes academic experts and managers of the banking sector and are aware of new banking and financial technologies (Fintechs) and specifically big data. The selection of the participants is based on their knowledge and nobility of the research topic and the importance of their presence in the research, and finally 15 people were selected by purposeful sampling using the snowball method. Experts have at least 10 years of relevant work experience and a master's degree. ConclusionThis research has clarified the situation of this area by identifying the shaping factors and drivers of the future of banking in Iran. Two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. Considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.Keywords: Futures Studies, Driver, Scenario Planning, Banking, Big Data.
