ارائه مدل کارکردی هوش مصنوعی و یادگیری ماشین در بازاریابی عصبی

Aim and Introduction: Today, the advancements in technology and the growth of smart technologies have led to significant changes across all industries. Every business must keep pace with the growing trend of technology to survive, thrive, and sustain itself. Among the prominent technologies today are artificial intelligence and machine learning, which have permeated all scientific fields, including marketing. Today, there is no field of application where artificial intelligence-based solutions are not utilized. Artificial intelligence focuses on human intelligence and its representation in computers. Due to their enormous analytical capabilities, artificial intelligence techniques are frequently employed in various research problems that traditional computational methods cannot solve. Despite being relatively new to the market, recent advancements in AI technology are already propelling numerous industries toward success. The field of artificial intelligence encompasses subfields such as machine learning and deep learning, which yield practical applications like voice and image recognition. Machine learning is a branch of artificial intelligence that utilizes computer algorithms to automatically learn from data and enhance performance without explicit programming. When new technology is introduced into society, it is essential for people to respond appropriately; otherwise, the technology may be discarded. As we become increasingly surrounded by advanced technology, the need for a human touch in technology becomes even more significant. This also applies to marketing. Currently, marketing is the fourth largest area of AI application and the sixth largest adopter of AI technology, with approximately 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 the field of neuromarketing.
Methodology: This research is a type of qualitative research that is descriptive in both its purpose and application, as well as in its data collection methods. The statistical population for this research comprised experts in the fields of artificial intelligence and marketing. The data collection method employed was an interview conducted with 12 individuals who are specialists in these areas. In the qualitative section of the data analysis method, the approach utilized was thematic analysis. This article explains Brown and Clarke's six-step method as a detailed and systematic process for analyzing themes. The first stage involves familiarizing oneself with the data, which includes both frequent and active reading. The second step is to create initial codes. These codes describe features of the data that are of interest to the analyst. The third step, for Themes, categorizing various codes into potential themes and organizing the basic codes into specific themes. The fourth step involves reviewing the themes, which consists of two stages: review and refinement. During this process, the validity of the themes is assessed in relation to the data set, and the themes are organized. The fifth step entails defining and naming the themes, which begins once a satisfactory map of the themes has been established. The sixth step is the preparation of the report. The final report is written once well-developed and thoroughly researched themes have been established.
Finding: After analyzing the interviews, initial coding was conducted, resulting in the identification and monitoring of 132 initial codes. Subsequently, the primary codes were classified into categories based on their degree of interconnection, which included various sub-themes. In this research, the researcher identified 32 sub-themes after monitoring and categorizing the primary codes. During the analysis phase, these sub-themes were organized into larger groups that encompassed the main themes, which included 10 overarching themes. The findings of this research categorize the functions of artificial intelligence and machine learning into ten main themes: neural measurement, neural branding, neural pricing, neural advertising, consumer needs, consumer behavior, integrated digital marketing, sales, product development, and post-purchase analysis.
Discussion and Conclusion: One of the most important functions of artificial intelligence and machine learning is their ability to serve as intelligent measurement tools, applicable in both marketing research and laboratory studies. This tool enables the collection of data in the form of neural signal activity and images from an individual's brain, as well as interactions between people and external environments, such as machines. Another function of artificial intelligence and machine learning is neurobranding. Neurobranding is one of the latest developments in neuromarketing, which examines individuals' neurophysiological responses to various brands using specialized testing equipment. Many researchers have utilized neural tools to investigate the neural responses of consumer behavior toward brands. AI techniques can assess both fair and premium pricing, as well as advertising effectiveness. Hence, artificial intelligence techniques can be utilized to assess how consumers perceive, experience, and respond to various price levels. Also, artificial intelligence and machine learning techniques can assist researchers and marketers in designing appealing products by analyzing consumer reactions to product features before they are launched in the market. These AI techniques can also be employed to measure and assess the effectiveness of advertisements. Techniques are employed to measure consumers' visual attention to advertisements, including videos, experiments, and images. Additionally, AI tools can be utilized to assess emotional responses to advertisements. Other applications of artificial intelligence and machine learning in neuromarketing include studies focused on consumer behavior. Additionally, sophisticated and advanced search capabilities that can effectively showcase products and services to potential customers. New products, such as smart mirrors and smart showcases, are among the many advancements that artificial intelligence has introduced in this field. Additionally, artificial intelligence offers marketers valuable insights after a customer completes a purchase.