ارائه مدل کارکردی هوش مصنوعی و یادگیری ماشین در بازاریابی عصبی

Aim and Introduction: Today, with the new developments in technology and the growth of smart technologies, there have been extensive changes in all industries. Every business needs to keep up with this growing trend of technology and technology in order to survive, grow and maintain itself. Among the discussed technologies of the day are artificial intelligence and machine learning, which have entered all scientific fields, including marketing. Today, there is no field of application in which artificial intelligence-based solutions are not used. Artificial intelligence deals with human intelligence and how it is represented in computers. Due to their enormous analytical power, artificial intelligence techniques are often used in various research problems that cannot be computed by traditional computational approaches. Despite its relative age in the market, recent advances in AI technology are already driving many industries to success. The artificial intelligence umbrella includes subfields such as machine learning and deep learning, which produce real-world applications of artificial intelligence such as voice and image recognition. Machine learning is an aspect of artificial intelligence that uses computer programs to automatically learn and improve the experience without explicit programming. When a new technology enters society, people must respond appropriately, or the technology will be discarded. The more we are surrounded by advanced technology, the greater the need for technology's human touch. This also applies to marketing. Marketing is currently the fourth largest area of use of AI and the sixth largest adopter of AI technology, with about 2.55% of the entire industry invested in it. The purpose of this research is to present a functional model of artificial intelligence and machine learning in neuromarketing.
Methodology: This research is a type of qualitative research, which is descriptive in terms of purpose and application, and in terms of data collection. The statistical population of the research were experts in the field of artificial intelligence and marketing. The data collection tool of this research was an interview that was conducted with 12 people who are experts in the field of artificial intelligence and marketing. In the qualitative part of the data analysis method, the approach was thematic analysis. In this article, Brown and Clark's 6-step method was explained as a step-by-step and comprehensive process for analyzing the theme. The first stage: getting to know the data, which includes frequent reading of the data and active reading of the data. Second step: creating initial codes. Codes describe a feature of the data that is of interest to the analyst. The third step: Searching for themes includes categorizing different codes in the form of potential themes and sorting the basic codes in the form of specific themes. The fourth step: Reviewing the themes, which includes two stages of review and refinement, where the validity of the themes is checked in relation to the data set, and organizing themes are formed. The fifth step is defining and naming the themes and starts when there is a satisfactory map of the themes. The sixth step: preparation of the report. The writing of the final report takes place when well-worked and well-developed themes have been created.
Finding: After analyzing the interviews, initial coding was done, and finally 132 initial codes were identified and monitored. Then, the primary codes were classified according to the degree of connection with each other into subsequent categories that included sub-themes. In this research, 32 sub-themes were identified by the researcher after monitoring and categorizing the primary codes. In the bad stage, sub-themes were assigned to larger groups that included the main themes. which included 10 main themes. The findings of this research classified the functions of artificial intelligence and machine learning into 10 main themes, including neural measurement, neural branding, neural pricing, neural advertising, consumer needs, consumer behavior, integrated digital marketing, sales, product, and post-purchase.
Discussion and coclusion: One of the most important functions of artificial intelligence and machine learning is an intelligent measurement tool that can be used in marketing research and laboratory research. This tool is made possible to receive data in the form of neural signal activity and images from a person's brain and interactions between people and external environments such as machines. Another function of artificial intelligence and machine learning is neurobranding. Neurobranding is one of the newest directions of neuromarketing that studies people's neurophysiological reactions to different brands with the help of special test equipment. Many researches have used neural tools to investigate the neural response of consumer behavior towards brands. AI techniques can measure fair and premium pricing and advertising. Hence, artificial intelligence techniques can be applied to measure how consumers perceive, experience and respond to different price levels. Also, artificial intelligence and machine learning techniques can help researchers and marketers to design desirable products by studying the reaction of the consumer's mind to product features before putting them on the market. Artificial intelligence techniques can be used to measure and evaluate the effectiveness of advertisements. Techniques are used to measure consumers' visual attention to advertisements such as videos, experiments, and images. Additionally, AI tools can be used to measure emotional responses to advertisements. Among other functions of artificial intelligence and machine learning in neuromarketing are studies related to consumer behavior. Also, smart and advanced search facilities that can easily expose products and services to potential customers. New products such as smart mirrors and smart showcases are also among other developments that artificial intelligence has brought about in this field. Also, artificial intelligence provides marketers with useful information after the customer makes a purchase.