پديد آورندگان :
يزدان پرست، مرتضي دانشگاه تهران - دانشكده مديريت، تهران، ايران , جامي پور، مونا دانشگاه حضرت معصومه (س) - گروه مديريت، قم، ايران , جعفري، محمدباقر دانشگاه تهران - پرديس فارابي - دانشكده مديريت و حسابداري - بخش مديريت صنعت و فناوري، قم، ايران
چكيده فارسي :
كاربردهاي هوش مصنوعي در بازاريابي برخط انقلابي در اين عرصه ايجاد كرده است كه همين موضوع توجه بسياري از سرمايه‌گذاران و مديران بازاريابي را به اين حوزه جلب كرده است. با اين وجود كمتر پژوهشي به اين موضوع پرداخته و هدف از اين پژوهش شناسايي كاربردهاي هوش مصنوعي در جنبه‌هاي مختلف بازاريابي برخط و اولويت‌بندي اين كاربردهاست. اين پژوهش با استفاده از روش آميخته صورت گرفته و در مرحله كيفي از طريق ده مصاحبه نيمه ساختاريافته با خبرگان حوزه بازاريابي آنلاين، هوش مصنوعي و بازاريابي الكترونيكي و با روش تحليل مضمون، چهل كاربرد هوش مصنوعي در بازاريابي برخط احصا و در چهار بخش بر مبناي آميخته بازاريابي دسته‌بندي شده‌اند. در مرحله كمي، با استفاده از پرسشنامه و با روش تجزيه‌وتحليل بهترين-بدترين، كاربردهاي شناسايي‌شده در مرحله كيفي، با استفاده از نظرات خبرگان صنعت و دانشجويان دانشگاه‌هاي مطرح ايران كه آشنا با حوزه هوش مصنوعي و بازاريابي برخط بوده‌اند، اولويت‌بندي شده‌اند. در انتها چهار حوزه طراحي محصول و خلق ارزش، قيمت‌گذاري و طراحي هزينه‌ها، تبليغات و اطلاع‌رساني به مشتريان و توزيع محصول به‌عنوان چهار حوزه كلي شناسايي شد كه 40 كاربرد يافت شده در اين چهار حوزه تقسيم و اولويت‌بندي شدند كه از ميان آن‌ها شخصي‌سازي تبليغات متناسب با رفتار پيشين كاربران و تحليل احساسات مشتريان در رابطه با تبليغات داراي بالاترين اولويت و همچنين توزيع نيروهاي توزيع در شعب توزيع متناسب با پيش‌بيني فشار كاري هر كدام از شعب داراي كمترين اولويت بود.
چكيده لاتين :
Introduction: The application of artificial intelligence (AI) in online marketing has created a revolution in this field, which has attracted the attention of many investors and marketing managers in this field. AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in marketing efforts where speed is essential. Since little research has been done on this issue, and the purpose of this research is to identify the applications of artificial intelligence in various aspects of online marketing and prioritize these applications.
The present research is thematically one of the first studies that applies artificial intelligence in online marketing. There has been no comprehensive and continuous view of the entire chain of attraction, persuasion, sales, and after-sales services. Thus, this research provides a comprehensive view of its audience by examining all the members of it.
Methods: This research was been conducted using a mixed method. In the qualitative stage, there were ten semi-structured interviews with experts in online marketing, artificial intelligence, and e-marketing. With the content analysis method, forty applications of artificial intelligence in online marketing were enumerated in four sections. They were categorized based on the marketing mix. The marketing mix refers to the set of actions or tactics that a company uses to promote its brand or product in the market. a typical marketing mix of price, roduct, Promotion, and Place.
In the quantitative stage, using a questionnaire and the best-worst analysis method, the applications identified in the qualitative stage have been prioritized using the opinions of industry experts and the students of prominent Iranian universities familiar with artificial intelligence and online marketing. Best Worst Method (BWM) is a multi-criteria decision-making (MCDM) based on a systematic pairwise comparison of the decision criteria used to evaluate a set of alternatives with respect to a set of decision criteria. The salient feature of the BWM is that it uses a structured way to generate pairwise comparisons, which leads to reliable results.
Results and Discussion: Product design and value creation, pricing and cost design, advertising and customer information, and product distribution were identified as four general areas in which 40 applications were classified and prioritized. Finally, nine applications were identified in the field of product design and value creation, nine applications in the field of pricing and cost design, and 16 applications in the field of advertising and customer information. In the field of sales place and product supply method, six applications were identified and ranked. Making advertisements in accordance with the previous behavior of users and customers' feelings in relation to advertisements had the highest priority. Also, the distribution of forces in distribution branches in accordance with the forecast of work pressure in each branch had the lowest priority.
Conclusion: This study suggests that, in order to use artificial intelligence in online marketing, various types of artificial intelligence should be applied in the marketing mix matrix. A comprehensive review of the most appropriate solutions for the organization is needed to maximize the effectiveness and efficiency of the solutions in the organization.
Considering the prioritization of different parts of the marketing mix, it should be noted that businesses should pay special attention to the use of artificial intelligence applications in online marketing in the field of advertising and customer information. Then, a focus should be placed on product design and value proposition, which leads to market knowledge and user interaction. After these two areas, pricing and cost design have almost the same weight as product design and value proposition, which shows their equal importance in this sector. The developed framework provides a comprehensive view of artificial intelligence in online marketing and helps organizations comprehensively identify solutions and prioritize them. Therefore, using the methods found here can solve a large percentage of marketing problems and guide businesses to achieve their marketing goals.
This research can also be used as a tool to comprehensively evaluate the performance of organizations in using artificial intelligence in online marketing. By designing a scoring model based on the weights obtained in this study, it is easy to determine the status of companies in different industries and with different conditions in the use of artificial intelligence in online marketing and in accordance with the priorities. This approach will also help many traditional companies that have some online marketing techniques to design and optimize their marketing system as optimally as possible.
