Reza Samizadeh, Sara Parsaie Mehr,
Volume 23, Issue 3 (9-2012)
Abstract
The present research is conducted to show that organizations can use wiki to attract customers' purchase intention inside the e-commerce context. Considering the relation between wiki and ecommerce for CRM, this research tries to explore the characteristics such as perceived risk, customer experience, knowledge sharing culture, trust and knowledge sharing in wiki tool impact on purchase intentions in the web site. By using multidimensional analysis, this study shows that choosing a suitable tool for hosting in the websites in order to gather essential knowledge from customers plays an important role in explaining certain customer online behavior. In particular, the researchers propose a model that explains wikis require a culture of collaboration and sharing in an online environment to achieve a win-win situation between customers and producers.
Aghil Hamidihesarsorkh, Ali Papi, Ali Bonyadi Naeini, Armin Jabarzadeh,
Volume 28, Issue 1 (3-2017)
Abstract
Nowadays, the popularity of social networks as marketing tools has brought a deal of attention to social networks analysis (SNA). One of the well-known Problems in this field is influence maximization problems which related to flow of information within networks. Although, the problem have been considered by many researchers, the concept behind of this problem has been used less in business context. In this paper, by using a cost-benefits analysis, we propose a multi-objective optimization model which helps to identify the key nodes location, which are a symbol of potential influential customers in real social networks. The main novelty of this model is that it determines the best nodes by combining two essential and realistic elements simultaneously: diffusion speed and dispersion cost. Also, the performance of the proposed model is validated by detecting key nodes on a real social network
Shima Khalilinezhad, Hamed Fazlollahtabar, Behrouz Minaei-Bidgoli, Hamid Eslami Nosratabadi,
Volume 32, Issue 3 (9-2021)
Abstract
One of the challenges that banks are faced with is recognition and differentiation of customers and providing customized services to them. Recognizing valuable customers based on their field of business is one of the key objectives and competitive advantages of banks. To determine guild patterns of the valuable customers based on their transactions and value of each guild for the bank, the banking tools on which the customer’s transactions take place need to be surveyed. Using deeper insights into the value of each guild, banks can provide customized services to ensure satisfaction and loyalty of their customers. Study population was comprised of the holders of point of sale (POS) devices in different guilds and the transactions done through the devices in an 18-months period. Datamining methods were employed on the set of data and the results were analyzed. Data preparation and analysis were done though online analytical processing (OLAP) method and to find guild patterns of the bank customers, value of each customer was determined using recency, frequency, monetary (RFM) method and clustered based on K-means algorithm. Finally, specifications of customers in the most valuable cluster were analyzed based on their guilds and the rules were extracted from the model developed using C5 decision tree algorithm.
