دانلود رایگان ترجمه مقاله مدیریت آگاهی و داده کاوی برای بازاریابی (نشریه الزویر ۲۰۰۱)
این مقاله انگلیسی ISI در نشریه الزویر در ۱۱ صفحه در سال ۲۰۰۱ منتشر شده و ترجمه آن ۱۹ صفحه میباشد. کیفیت ترجمه این مقاله ارزان – نقره ای ⭐️⭐️ بوده و به صورت کامل ترجمه شده است.
دانلود رایگان مقاله انگلیسی + خرید ترجمه فارسی | |
عنوان فارسی مقاله: |
مدیریت آگاهی و داده کاوی برای بازاریابی |
عنوان انگلیسی مقاله: |
Knowledge management and data mining for marketing |
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مشخصات مقاله انگلیسی | |
فرمت مقاله انگلیسی | pdf و ورد تایپ شده با قابلیت ویرایش |
سال انتشار | ۲۰۰۱ |
تعداد صفحات مقاله انگلیسی | ۱۱ صفحه با فرمت pdf |
نوع مقاله | ISI |
نوع نگارش | مقاله پژوهشی (Research article) |
نوع ارائه مقاله | ژورنال |
رشته های مرتبط با این مقاله | مدیریت، مهندسی صنایع |
گرایش های مرتبط با این مقاله | مدیریت فناوری اطلاعات، بازاریابی، مدیریت کسب و کار، سیستم های اطلاعاتی پیشرفته، داده کاوی |
چاپ شده در مجله (ژورنال) | سیستم های پشتیبانی تصمیم گیری – Decision Support Systems |
کلمات کلیدی | داده کاوی، مدیریت آگاهی؛ پشتیبانی تصمیم گیری؛ مدیریت رابطه مشتری |
کلمات کلیدی انگلیسی | Data mining – Knowledge management – Marketing decision support – Customer relationship management |
ارائه شده از دانشگاه | گروه مدیریت بازرگانی، ایالات متحده آمریکا |
نمایه (index) | Scopus – Master Journals – JCR |
شناسه شاپا یا ISSN | ۰۱۶۷-۹۲۳۶ |
شناسه دیجیتال – doi | https://doi.org/10.1016/S0167-9236(00)00123-8 |
ایمپکت فاکتور(IF) مجله | ۵٫۴۲۱ در سال ۲۰۱۹ |
شاخص H_index مجله | ۱۲۷ در سال ۲۰۲۰ |
شاخص SJR مجله | ۱٫۵۳۶ در سال ۲۰۱۹ |
شاخص Q یا Quartile (چارک) | Q1 در سال ۲۰۱۹ |
بیس | نیست ☓ |
مدل مفهومی | ندارد ☓ |
پرسشنامه | ندارد ☓ |
متغیر | ندارد ☓ |
رفرنس | دارای رفرنس در داخل متن و انتهای مقاله ✓ |
کد محصول | F1730 |
نشریه | الزویر – Elsevier |
مشخصات و وضعیت ترجمه فارسی این مقاله | |
فرمت ترجمه مقاله | pdf و ورد تایپ شده با قابلیت ویرایش |
وضعیت ترجمه | انجام شده و آماده دانلود |
کیفیت ترجمه | ترجمه ارزان – نقره ای ⭐️⭐️ |
تعداد صفحات ترجمه تایپ شده با فرمت ورد با قابلیت ویرایش | ۱۹ صفحه (۱ صفحه رفرنس انگلیسی) با فونت ۱۴ B Nazanin |
ترجمه عناوین تصاویر | ترجمه شده است ✓ |
ترجمه متون داخل تصاویر | ترجمه نشده است ☓ |
ترجمه ضمیمه | ترجمه نشده است ☓ |
ترجمه پاورقی | ترجمه نشده است ☓ |
درج تصاویر در فایل ترجمه | درج شده است ✓ |
منابع داخل متن | به صورت عدد درج شده است ✓ |
منابع انتهای متن | به صورت انگلیسی درج شده است ✓ |
کیفیت ترجمه | کیفیت ترجمه این مقاله پایین میباشد. |
فهرست مطالب |
چکیده |
بخشی از ترجمه |
چکیده |
بخشی از مقاله انگلیسی |
Abstract Due to the proliferation of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of customer data in large databases. However, much of the useful marketing insights into customer characteristics and their purchase patterns are largely hidden and untapped. Current emphasis on customer relationship management makes the marketing function an ideal application area to greatly benefit from the use of data mining tools for decision support. A systematic methodology that uses data mining and knowledge management techniques is proposed to manage the marketing knowledge and support marketing decisions. This methodology can be the basis for enhancing customer relationship management. ۱- Introduction In recent years, the advent of information technology has transformed the way marketing is done and how companies manage information about their customers. The availability of large volume of data on customers, made possible by new information technology tools, has created opportunities as well as challenges for businesses to leverage the data and gain competitive advantage. Wal-Mart, the largest retailer in the U.S., for example, has a customer database that contains around 43 tera-bytes of data, which is larger than the database used by the Internal Revenue Services for collecting income taxes 10 . w x The Internet and the World Wide Web have made the process of collecting data easier, adding to the volume of data available to businesses. On the one hand, many organizations have realized that the knowledge in these huge databases are key to supporting the various organizational decisions. Particularly, the knowledge about customers from these databases is critical for the marketing function. But, much of this useful knowledge is hidden and untapped. On the other hand, the intense competition and increased choices available for customers have created new pressures on marketing decision-makers and there has emerged a need to manage customers in a long-term relationship. This new phenomenon, called customer relationship management, requires that the organizations tailor their products and services and interact with their customers based on actual customer preferences, rather than some assumed general characteristics 21,22 . As organiza- w x tions move towards customer relationship management, the marketing function, as the front-line to interact with customers, is the most impacted due to these changes. There is an increasing realization that effective customer relationship management can be done only based on a true understanding of the needs and preferences of the customers. Under these conditions, data mining tools can help uncover the hidden knowledge and understand customer better, while a systematic knowledge management effort can channel the knowledge into effective marketing strategies. This makes the study of the knowledge extraction and management particularly valuable for marketing. Developments in database processing 6,13,15,28 , w x data warehousing 16,18 , machine learning 4,12,25 wx w x and knowledge management 2,14,24 have con- w x tributed greatly to our understanding of the data mining process. More recent research on data mining and knowledge discovery 20,26,27 has further en- w x hanced our understanding of the application of data mining and the knowledge discovery process. But, most research has focused on the theoretical and computational process of pattern discovery and a narrow set of applications such as fraud detection or risk prediction. Given the important role played by marketing decisions in the current customer-centric environment, there is a need for a simple and integrated framework for a systematic management of customer knowledge. But, there is a surprising lack of a simple and overall framework to link the extraction of customer knowledge with the management and application of the knowledge, particularly in the context of marketing decisions. While data mining studies have focused on the techniques, customer relationship studies have focused on the interface to the customer and the strategies to manage customer interactions. True customer relationship management is possible only by integrating the knowledge discovery process with the management and use of the knowledge for marketing strategies. This will help marketers address customer needs based on what the marketers know about their customers, rather than a mass generalization of the characteristics of customers. We address this issue in this paper by presenting an integrated framework for knowledge discovery and management, in the context of marketing decisions. Our paper is further organized as follows. First, we present a taxonomy of data mining tasks and discuss knowledge management as an iterative process Section 2 . We then survey different types Ž . of potentially useful marketing and customer knowledge discovered by data mining Section 3 . Market- Ž . ing decisions based on discovered customer knowledge leads to knowledge-based marketing Section Ž ۴ . We close our discussion by identifying the . emerging issues to be addressed in the process of managing the discovered marketing knowledge Sec- Ž tion 5 . |