جستجوی مقالات مرتبط با کلیدواژه « segmentation » در نشریات گروه « مدیریت »

Supplier Segmentation (SS) makes Supplier Relationships Management (SRM) easier, especially when the number of suppliers is large. SS creates value and synergy in relation with the supplier. SS as a step between the two strategic activities of supplier selection and SRM obtains distinct groups of suppliers based on their similarities. In the field of marketing, segmentation usually refers to the aspect of market demand, in which the goal of companies is to segment groups of customers with similar needs and demands. Past researches have usually considered one of the aspects of supply or demand for segmentation and have not investigated the issue of segmentation from both aspects (supply and demand). When a company (manufacturing or service) has segmentation information about the status of its customers and shopping centers at the same time, it can be successful in policies. In this paper, shopping centers (supply side) and customers (demand side) of Food Center Company (FCC) are segmented using the TOPSIS technique.

First, the criteria related to the segmentation of shopping centers and customers are identified separately based on the literature and after localization by the managers of the FCC, they are classified in two objective and subjective dimensions. After localization of criteria, four decision matrixxs are formed for 109 shopping centers and 50 customers. By implementing TOPSIS steps on the decision matrixes of shopping centers and customers, the final score of shopping centers and customers is obtained in objective and subjective dimensions. Finally, shopping centers and customers are selected in four sections, namely shopping centers/customers with weak objective and subjective dimensions, shopping centers/customers with strong objective dimension and weak subjective dimension, shopping centers/customers with weak objective dimension and Strong subjective dimension, and shopping centers/customers with objective and strong subjective dimensions are placed. By sharing the segmentation results with the managers of the FCC and consulting with them, solutions for improving the performance of shopping centers and improving the relations of the FCC with customers in the areas of supplier relationship management and customer relationship management are presented.

After studying the literature, 19 criteria related to customer segmentation and 19 criteria related to shopping centers were identified. After receiving experts' opinions, 11 criteria for segmenting customers and 16 criteria for segmenting shopping centers were recognized as final criteria. Segmentation results of shopping centers show that 32 shopping centers were placed in Section 1, 4 shopping centers in Section 2, 31 shopping centers in Section 3 and 42 shopping centers in Section 4. Segmentation results of customers show that 19 customers were placed in Section 1, 4 customers in Section 2, 16 customers in Section 3 and 11 customers in Section 4.The customers of Section 1 are the weakest customers of the FCC. FCC can remove these customers from its customer list or help improve their situation through incentive programs. For the customers of Section 2, solutions such as interaction in social networks and recording the voice of customers will be useful. For the customers of Section 3, solutions such as designing a communication calendar will be useful. The customers of Section 4 are the strongest customers of the FCC. For the customers of this sector, solutions such as providing free services and special appreciation for them can help to retain them for the company.For shopping centers of section1, the FCC can remove them from its shopping list or help to improve their situation through methods such as holding training courses in order to promote these shopping centers and evaluate their performance after conducting training courses. For shopping centers of section 2, FCC can improve their mental dimension by improving the indicators of their position in the industry, after-sales service, customer relationship management, compliance with ethical standards, desire to share information and desire to learn and grow in these shopping centers. For shopping centers of section 3, FCC should help to improve the objective dimension of these shopping centers in improving the geographical distance of the shopping center to the customer, the frequency of sales of the shopping center, the quality of the shopping center's products or services, the variety of advertising by the shopping center, and the use of incentive policies by the shopping center. shopping centers of section4 are the best and most profitable shopping centers of the FCC.

Since the past researches have examined the issue of segmentation mainly from the aspect of supply, considering aspects of supply and demand, and application in the real world is useful for researchers in this field. The company should maintain these profitable shopping centers that are effective in the survival of the company and invest in creating long-term relationships with these shopping centers.

The purpose of this research is to segment and determine the profile of customers using a self-organizing neural network algorithm (case study: customers of branches of Agricultural Bank of Ahvaz). The present research is included in the group of quantitative methods. The research method is based on the purpose of the applied type and the method of collecting descriptive (non-experimental) data - survey, and in terms of location, it is field research. The statistical population of this research is the customers of Ahvaz Agricultural Bank. Since the statistical population of this research is unlimited, the sample size is determined to be 384 people based on Cochran's unlimited population formula. The research tool is a questionnaire. In this research, content validity was done by asking the supervisor and other professors who have opinions on the subject area of this research. After applying these opinions on the questionnaire's content and solving the existing problems, Moore's questionnaire was approved. Data analysis was done by SOM neural network analysis method in MATLAB software. After the implementation of data analysis, the findings of the research identified five different categories of customers with different behavioral and demographic characteristics. These 5 segments are largely traditional and valuable customers, golden customers, low-value customers, loyal diamond customers, and special customers.

The emergence of car import market in Arvand Free Zone in Khuzestan province, has covered a significant share of car purchases in this vast province. The present study was conducted using artificial network algorithm of self-organizing maps model aiming at segmentation and profiling of customers of Arvand license plate registered vehicles. In order to identify the profile of customers and adopt an appropriate marketing strategy, the statistical population of this study was formed over 70,000 owners of Arvand license plate registered vehicles across the Khuzestan province. Using Morgan table, 384 people were selected as a sample by random sampling method, and a questionnaire was utilized to collect information. Research in the group was carried out with a quantitative method that was applied in terms of purpose, exploratory-survey in terms of nature, and also cross-sectional time wise. Data analysis was performed by self-organizing neural network analysis (SOM). Simultaneously, the demographic, psychological, and behavioral characteristics have also been considered to categorize customers. Based on the results, 3 sections including indifferent, conservative and loyal customers were identified and named. The validity and reliability of the study were also statistically confirmed. The results of this study show that demographic, psychological and behavioral variables have a decisive and special role in customer segmentation of Arvand license plate registered vehicles.

Religious tourism is one of the five main branches of tourism according to the World Trade Organization. The popularity of pilgrimage has increased in recent decades, and religious tourism has been an important part of the dynamics of the world tourism economy. Therefore, this applied descriptive-survey study sought to segment and determine the psychological-demographic and behavioral profile of Karbala pilgrims attending the annual Arbaeen rituals. The statistical population of the study comprised of the pilgrims of Karbala who have traveled through the borders of Khuzestan province to attend the Arbaeen rituals. The neural network algorithm was used to analyze the data via self-organizing maps. The study's findings indicated that there were three different groups of pilgrims with different travel motives, who were categorized as a) being more tourist than a pilgrim (tourist pilgrim), b) being more pilgrim than a tourist (pilgrim tourist), and c) pilgrims. These three groups of tourists were named after leisureists, enthusiasts of various activities, and religious people, suggesting that each type of pilgrim had its own psychological, demographic, and behavioral characteristics. Therefore, marketing strategies for developing tourism activities should be set based on the characteristics of each type of tourist.

Knowing customers and identifying profitable services is of great importance due to the diversity of bank customers and the variety of services in the country's banking system. Customer relationship management is now the core of the business world, today the electronic portals of banks using various information exchange networks such as Satna, Paya, Chakavak, Sayad and. . . They are connected to each other. The most important interbank network used in Iran is Shetab network.In this study, data mining techniques were used to classify and rank customers in the Shetab network using an improved RFM-based data mining model so that banks could analyze and evaluate their customers' behavior in this network and formulate Deal with effective policies in dealing with customers.For this purpose, the required data were extracted from the bank card switch database and the required analyzes and data mining operations were performed on it by CRISP method. This data is available in the database of the Welfare Bank and was extracted from the database of the institution using DML commands. Also, in order to review similar studies and increase information through library and internet studies, information related to the model was collected. Finally, R+FMW presented a model for clustering bank customers and their transactions. The results showed that the developed R+FMW model has a higher accuracy than the basic RFM model and by using this model, banks can identify customers active in the interbank exchange network (acceleration) and expensive customers and communication channels. Recognize in terms of fees and demographic information

Due to the accumulated volume of customer purchasing information and the complexity of competition in the present era, the importance of creating a platform for analyzing up-to-date and accurate customer data, with the aim of creating effective relationships with current and loyal customers, more than ever for organizations as It has become a competitive advantage. The purpose of this study was to investigate the behavioral patterns of customers buying Hygienic Products in order to classify them based on the WRFM using data mining methods. 65534 samples were collected from the company databases in the period of 1396-1397 among the customers of Tehran province by the available purposeful sampling method, also with the help of SPSS, the amount of WRFM determined according to the opinion of industry experts and then this field had been to other fields in the research and using Clementine software, customers clustering has been done according to 70% of the data; also, in order to evaluate the quality of clustering, the criteria of Gini Score, error percentage, and normalized mutual information were used. The results indicate the high efficiency of the K-Means clustering method with the number of four clusters with purity percentage (0.761) for customer segmentation.

A new matter that arises in term of the dynamic behavior of customers is considering the customer segmentation. Based on the banks competitions to increase market share as well as the psychological and environmental factors the dynamics of customers’ behavior should be considered over time. Transferring customers to different sectors over time and discovering the dominant models in their displacements between sectors are of important topics in this context.

this article aims to identify the behavioral clusters, the dominant patterns of displacement, and the leading characteristics and patterns of customer displacements with a focus on the customer dynamics behavior of Ansar banks. Design/

A Hybrid method based on clustering and association rules has been proposed.

four different behavioral group of customer are identified:" low-value customers with sustainable model", "low-value customer with unsustainable profitability model", "turned away customers with average profitability", "loyal customers with low profitability". Relations between of these groups are analyzed by association rules

The success of tourism destinations depends on a proper understanding of the brand image of individuals, but individuals have different mental images which are potentially the basis for segmentation in the tourism market. The present study is primarily an attempt to develop a brand concept map method that is done by refining it with an inventive algorithm, so the research is developmental in terms of purpose. Secondly, to apply the refined Brand Concept Map method in practice this method is used for the tourism brand of Kish Free Zone. The statistical population of the study is people in Tehran city in two groups of people who have visited and those who haven’t visited the Kish Free Zone. The available sampling method is used. The four-step of extraction, segmentation, mapping, and interpretation of mental maps will ultimately lead to the identification of seven segments for the Kish Free Zone Tourism Market. According to the results, there are four categories of luxury tourists, price-sensitive tourists, non-loyal tourists, and Eco-tourists among people who have visited Kish and there are three categories of shoppers, entertainers, and immigrant among people who haven’t visited Kish

  All kinds of word-of-mouth (WOM) communication are not created in the same way, and their effects vary depending on several factors, such as resource, recipient, message, and status features. Accordingly, for the purpose of effective use of social networks, it is not enough to create a positive WOM relationship but it is important to take into account the conditions and factors through which the users of such communications accept and communicate it with others. Although researchers have been studying WPM communication in various online platforms, including online consumer surveys as well as from a variety of perspectives including marketing and psychology, there is little research on word of mouth communication in the field of social networking. The investigation in the present study shows that there is no research (local and international) investigating social network users based on social incentives affecting participation in WOM communication so far. This research can be considered as the first to deal with the partitioning of social network users based on the motivations of participation in oral communication. The main objective of this research is to identify the social motivations affecting the participation of consumers in advertising campaigns on social networks, the segmentation of users from this perspective, and ultimately providing marketing strategies tailored to each sector.

This research is applied in terms of purpose and descriptive-exploratory in terms of implementation. The data were collected distributing the on-line link to the questionnaire to more than 385 social network users selected based on available sampling method. In order to analyze the data and to partition social network users, self-organizing maps based on artificial neural networks have been used.

Based on the findings, social network users were divided into three sections with different demographic, behavioral and social motivations affecting WOM behaviors. These three sections were titled "Low Active Social Stimulus", "Active Social Stimulus" and "Social Inactivity Stimulus". The first section is applied to users who spend a fair amount of time on social networks, but have less social motivation to engage in word of mouth behaviors. The second section is applied to young people who devote most of their time to social networking activities and are highly motivated. And the third category is applied to those who are motivated enough but devote very little time to working on social networks.

Managers and advertising activists in social networks should consider word of mouth communication as an important part of social interactions. Although the content of WOM messages is often related to brands, the fact is that these types of communications are more likely to be influenced and published by the various incentives of consumer. This kind of behavior suggests that communication strategies should develop to the point where consumers' incentives to engage in oral communication in social networks can be identified and thereby their likelihood of purchasing can be increased. In the first group, it is recommended that marketers take into consideration the incentives that affect this sector and activate them based on the relative great magnitude of this sector compared to other sectors. In the second group, marketers are advised to consider this sector as a motivated sector for communication and encouraging them to have WOM communication. Hence, those who want to advertise their products on social networks should know that this section of social networking users are willing to receive oral messages and that they should provide the product information in a comprehensive and detailed manner so that they can further explore them by sharing it with experts. In the third group, marketers should invite them to engage in oral communication through communicating with individuals with similar beliefs, behaviors and intellectual flow. The members of this section are buying the products that others have verified. Hence, using well-known individuals to advertise their products can be used as one of the appropriate ways to promote products and brands for this section.

Due to increasing competition among banks, in order to attract new customers, understanding and predicting their behaviors are very critical. In order to analyze customer behavior, it is necessary to identify customers, distinguish between them, and recognize more valuable customers. To distinguish among customers, segmentation concepts are used. A new issue that has recently been raised in customer segmentation is considering dynamic behavior of customers. Based on the developments in IT, offering new banking services and the banks competitions to increase market share as well as the psychological and environmental factors the dynamics of customers’ behavior should be considered over time. Transferring customers to different sectors over time and discovering the dominant models in their displacements between sectors are one of the important topics in this context.  Therefore, this article aims to identify the behavioral clusters, the dominant patterns of displacement, and the leading characteristics and patterns of customer displacements with a focus on the customer dynamics behavior of Ansar Bank. To this end, customers have been clustered and tagged in each of the time periods using the RFM variable. By sticking labels at each interval, the transmission patterns are obtained for the customer's behavior. Then, with the help of combination method clustering and association rules, patterns of behavior have been analyzed. Based on the results, four clusters of behaviors were identified: low-value customers with sustainable model, low-value customer with unsustainable profitability model, turned away customers with average profitability, loyal customers with low profitability and the relationships between them have been analyzed. The outcomes of this study can play a remarkable role for top managers to take appropriate marketing strategies.

This research aims at associating two groups of demographic and transaction related factors and furthermore, proposes customer churn factor as another influential factor in customer value analysis. To this end, at first, customers' transaction data in a real local ISP in a four year period are utilized for segmentation purpose using k-means method. Regarding service nature of the case, customers' behavior has been considered in terms of customer satisfaction factors. The results of Exploratory Factor Analysis (EFA) indicate that three factors of service quality, expectation confirmation and post-purchasing satisfaction are influential factors. Then, association rules using GRI algorithm are exploited in order to investigate among customers' behaviors and propose appropriate strategies and action plans for each customer segment. Finally, segmentation results are associated with physiographic variables and existence of a significant difference among identified segments is more investigated using one way ANOVA test. The results clarified that expectation confirmation and post- purchasing satisfaction as hygienic factors and service quality as an incentive factor influence customer loyalty.

The increasing trend of mobile phone owners reveals the importance of mobile banking on these days. Understanding the user's expectations helps banks to increase the market share. The aim of this study is to segment mobile banking users based on expectations. In this study, Agriculture bank of Iran is selected as the case study. Based on a mixed approach of type qualitative → quantitative, mobile banking user's expectations were identified within 4 main expectation as: expectations of the main services, added service, facilitating conditions for using and perceived usefulness for using mobile banking. Data has been collected from 308 users of mobile banking through the closed questionnaire. In the next step, after determining the optimal number of clusters by Ward method, data mining technique of K-Means has been used for clustering. Based on Anova test applied on identified expectations, the results showed that the average of expectations between clusters is different because the significance level of this expectations was lower than 0.001.

Effective Competition in today's markets requires correct identification of market segments. The food market is one of markets that need to be segmented on the basis of appropriate criteria. Food-Related Life Styles (FRL) is a comprehensive tool for segmentation of the food market in Europe that has rarely been used in Iran. The aim of this study is segmentation of the food Market based on this model. The study sample is 383 residents of Shiraz selected by cluster sampling and completed the study's questionnaire. In order to identify clusters of customers, the reliability and validity of the standard questionnaire was evaluated based on the data collected and eventually modified questionnaire with 19 dimensions and 55 items was obtained. Then, based on the modified questionnaire clustering process in two steps was done and leads to the identification of three clusters of traditional, Unaware and rational in the city of Shiraz.

With emergence of electronic trading through internet, multichannel retailing became the dominant paradigm of business world. New media emerged and a developed set of different channels were made for companies and consumers. The customers developed their experience channels from traditional purchase shops and now it is very usual for them buy from online shops, catalogues, ATMs and call centers. The main purpose of the present study is segmenting the customers regarding their tendency to different channels in information searching phases and purchase and also explaining the determining factors of their membership in this segment. Demographic and psychological variables are considered simultaneously in the present research. To this end, the data was collected through distribution of 800 questionnaires among consumers in Tehran city. Combining self-organizing maps and logistic regression was used to analyze the data. The results showed that regarding consumers’ attitude, they can be divided to four different channels including multi-channel consumers, traditional, neutral and search-oriented ones. The results also showed the existence of a multi-channel consumer section with 23.88% of the participants. Other findings showed that socio-demographic variables including gender, age, job and income level and psychological variables including innovativeness, quality consciousness, time pressure and motivation to confirm had determining roles in probable membership of consumers in different sections.

In recent years،Internet-based banking services have become the focus of competition in Iran’s banking system. In this respect، Internet banking users’ identification and segmentation leads to better understanding of users’ needs and expectations and planning to meet them. This in turn will result in improving the image of the bank and obtaining competitive advantage. In this research، seven banks of Pasargad، Mellat، Parsian، Saman، Eghtesad-e-novin، Tejarat and Melli are selected as rival brands. According to scrutiny، the expectations of internet banking users were identified in the form of 17 indicators. Using closed questionnaires، necessary data was collected from 274 users of Internet banking services in selected banks. At the first stage، based on exploratory factor analysis، five factors were identified which include: ease of use، variety of e-banking services، security، speed of providing services and reliability. In the second stage، by applying k-means procedure، optimum number of clusters was detected equal to 6. Then the expectations of each cluster were evaluated. The result showed that the average of expectations and frequency of demographic variables between clusters are different. So the extracted clusters have good quality.