دانشگاه اصفهان تحقیقات بازاریابی نوین 2228-7744 13 3 2023 11 22 Designing a B2B Digital Marketing Implementation Model with an Emphasis on Artificial Intelligence-based Customer Relationship Management طراحی مدل پیاده‌سازی بازاریابی دیجیتال بنگاه به بنگاه با تأکید بر مدیریت ارتباط با مشتری مبتنی بر هوش مصنوعی 133 158 28054 10.22108/nmrj.2023.138898.2952 FA محمد باشکوه اجیرلو دانشیار مدیریت بازرگانی، دانشکدۀ علوم اجتماعی، دانشگاه محقق اردبیلی، ایران، اردبیل رحیم محمدخانی دانشجوی دکتری مدیریت بازرگانی، دانشکدۀ علوم اجتماعی، دانشگاه محقق اردبیلی، ایران، اردبیل Journal Article 2023 09 12 Abstract Introduction: The use of digital marketing as a communication and sales channel has led to the need and use of customer relationship management (CRM) systems based on artificial intelligence for the proper management of company information. Purpose: The purpose of this research is to design a business-to-business marketing implementation model with an emphasis on customer relationship management based on artificial intelligence Methods: This study is a type of mixed research with a qualitative-quantitative approach, which is a survey study in terms of its purpose, application, and data collection. In the qualitative part of the data collection tool, a semi-structured interview with 18 specialists and experts in the field of business-to-business digital marketing and university professors who were selected by snowball method. The quantitative part includes a targeted sampling with 35 digital marketing experts and data gathered through a questionnaire. In the qualitative part of the data analysis method, the Grounded theory approach is used based on the Strauss and Corbin method, which is compiled using the MAXQDA software and the coding method. In the quantitative part, the validation method is based on the Kendall correlation test. Findings: The findings of the research (6 general categories, 25 subcategories, and 173 main concepts) include presenting a model that includes causal, contextual, and intervening conditions, along with the introduction of the central phenomenon and the presentation of strategies for implementing business-to-business digital marketing and identifying its consequences. Conclusions: Company managers should pay attention to factors such as customer relationship management based on artificial intelligence (analytical, collaborative, and operational) to implement digital marketing from business to business and try to weaken the inhibiting factors and strengthen the positive and influential factors. Introduction Considering the number of customers and a large volume of transactions and the possibility of increasing profitability in business-to-business business compared to business-to-consumer business, it seems necessary to do more research on business-to-business digital marketing. Therefore, in the current research, we theoretically use the typology of three types of customer relationship management: analytical customer relationship management (analyzing customer data and improving their experience) collaborative customer relationship management (for sharing information and internal collaboration organization), and operational customer relationship management (for customer contact management). These customer relationship management systems in business-to-business digital marketing can use artificial intelligence to improve data and analyze new patterns by analyzing user data in digital environments. The innovation of the current study is that despite the exponential development of artificial intelligence and its emerging application in various production environments, none of the previous studies have addressed issues in business-to-business digital marketing. Methodology This research is a type of mixed research with a qualitative-quantitative approach, which is a survey study in terms of its purpose, application, and data collection. In the qualitative part of the data collection tool, a semi-structured interview was conducted with 18 specialists and experts in the field of business-to-business digital marketing and university professors who were selected using the snowball method. In the quantitative part, purposeful sampling is done with 35 digital marketing experts and information gathering through a questionnaire. In the qualitative part of the data analysis method, the data theory approach is used based on the Strauss and Corbin method, which was developed using the MAXQDA software and the coding method. In the quantitative part, the validation method is based on the Kendall correlation test. In this research, to meaningfully interpret the factors affecting the implementation of digital marketing from business to business, the personal views and experiences of experts, senior marketing managers of companies, IT specialists, and university professors have been examined. The data were collected through deep and semi-structured interviews with 18 experts and specialists in the field of digital marketing who have at least 10 years of experience in the field of marketing, sales, advertising, and digital marketing, as well as university professors who have records of working and teaching in the field of marketing and marketing. They had digital skills and were capable in terms of having knowledge-oriented indicators. Findings Analyzing the opinions and views of the research participants about the implementation of business-to-business digital marketing with an emphasis on artificial intelligence-based customer relationship management led to the presentation of a qualitative model whose causal factors include 9 categories: 1) management factors, 2) communication and information technology, 3) factors related to digital marketing, 4) company strategies, 5) technological measures, 6) customer-related measures, 7) innovation ecosystem readiness, 8) digital transformation, and 9) digital adoption. According to the findings of the research, the components of cultural and social factors, legal factors, electronic word-of-mouth marketing, digital infrastructure and capabilities, customer orientation, human resources, and strategic management including environmental factors and the governing platform for the development of business-to-business digital marketing strategies are also important. The main types of customer relationship management can be applied according to how they are implemented in companies. Accordingly, types of customer relationship management (analytical, operational, and collaborative) are those types of customer relationship management systems that are created, customized, and structured for large companies that have the capacity and need to develop management platforms for their information. The impact of contextual categories, intervening factors, and the application of strategy on the implementation of digital marketing can lead to agility, organizational consequences, identifying the basic patterns of customer purchase behavior, consumer cooperation in new product production, and building trust in digital operating systems. Conclusions AI-based customer relationship management systems can add business value and also turn B2B digital marketing into a sustainable strategy that can predict the steps a company should take to succeed in its marketing strategies. Therefore, the application and new uses of artificial intelligence-based customer relationship management are essential for companies in the business-to-business environment. Artificial intelligence-based customer relationship management increases the profitability of marketing accounts and improves business performance even globally by collecting data and predicting sales, business, and scalability. Artificial intelligence-based customer relationship management that works in digital marketing environments pays attention to techniques focused on machine learning and big data and uses data-based marketing strategies to guide and collect customer knowledge data and evaluate the performance of activities. Usually, these processes are related to analytical customer relationship management types. Therefore, using artificial intelligence-based customer relationship management facilitates decision-making processes, understanding of user behavior and responses, innovation strategies, sales forecasting, understanding of social network strategies, as well as customer orientation in digital environments. استفاده از بازاریابی دیجیتال به‌عنوان کانال ارتباطی و فروش منجر به نیاز و استفاده از سیستم‌های مدیریت ارتباط با مشتری (CRM) مبتنی بر هوش مصنوعی برای مدیریت صحیح اطلاعات شرکت شده است. هدف از پژوهش حاضر طراحی مدل پیاده‌سازی بازاریابی بنگاه به بنگاه با تأکید بر مدیریت ارتباط با مشتری مبتنی بر هوش مصنوعی است. این پژوهش از نوع پژوهش‌های آمیخته با رویکرد کیفی و کمّی است که ازنظر هدف، کاربردی و ازلحاظ نحـوۀ گـردآوری داده از نوع مطالعات پیمایشی است. ابزار گردآوری اطلاعات در بخش کیفی مصاحبۀ نیمه‌ساختاریافته با 18 نفر از متخصصان و خبرگان در‌زمینۀ بازاریابی دیجیتال بنگاه به بنگاه و استادان دانشگاه است که به روش گلوله‌برفی انتخاب‌ شده است. نمونه‌گیری در بخش کمـّی به‌صورت هدفمند با 35 نفر از خبرگان و متخصصان بازاریابی دیجیتال انجام شده و گردآوری اطلاعات نیز با پرسشنامه است. روش تحلیل داده‌ها در بخش کیفی رویکرد نظریۀ داده‌بنیاد با روش استراوس و کوربین است که با استفاده از نرم‌افزار مکس کیو دا (MAXQDA) و بـا استفاده از روش کدگذاری تدوین ‌شده است. روش اعتبار سنجی در بخش کمّی نیز بر‌مبنای آزمون همبستگی کندال است. یافته‌های پژوهش (6 مقولۀ کلی، 25 مقولۀ فرعی و 173 مفهوم اصلی) شامل ارائۀ مدلی مشتمل بر شرایط علّی، زمینه‌ای و مداخله‌گر به همراه معرفـی پدیـده‌محـوری و ارائـۀ راهبردهای پیاده‌سازی بازاریابی دیجیتال بنگاه به بنگاه و شناسایی پیامدهای آن است. نتایج پژوهش نشان داد که مدیران شرکت‌ برای پیاده‌سازی بازاریابی دیجیتال بنگاه به بنگاه به عواملی مانند مدیریت ارتباط با مشتری مبتنی بر هوش مصنوعی (تحلیلی، مشارکتی و عملیاتی) توجه کرده‌اند و به‌دنبال آن بر تضعیف عوامل بازدارنده و تقویت عوامل مثبت و تأثیرگذار همت ورزیده‌اند.

https://nmrj.ui.ac.ir/article_28054_58c638376fee87d5aba8ebe82848e821.pdf