Drastic changes and turbulence in macro-economic factors have the greatest impact on banks target market attractiveness in Iran. It is assumed that conventional Segmentation models at the corporate level are not efficient for banking system. This study aims to develop a new Segmentation model at the Industry level for banks of Iran. For this purpose, structures and variables at the Industry level were identified and defined by reviewing the literature and with the help of bank experts in focus group sessions. Then, data of ISIC 3-digit factories with 50 and more employees were extracted from Iran Statistic Center and Tehran Stock Exchange databases during 2005-2013. We used Hierarchical Cluster analysis in each year and identified 4 study groups across 9 years. We found that identified groups are significantly different regarding Industry size, deposit and loan market size, Industry growth, deposit and loan market growth, profitability, investment risk, and transaction with other industries.

When a private banks and financial institutions seriously started working in the banking Industry, the competition between enterprises and banks to customer identification, attraction and retention is most important. Many companies, especially banks which deal with a large number of customers, use the application of data mining techniques in the CRM. Knowing customers and their behavior with some techniques, like Segmentation, is the key to success in today’ s competitive market. The RFM model is used in the most costumer Segmentation research. In this paper, we developed the RFM model by adding continuity variable (C) and entitled RFMC model. In one of the private bank, the costumers clustered by proposed models based on Two-Step algorithm and GRISP-DM methodology. The results demonstrate the accuracy of developed model in costumer Segmentation is 5. 5% higher than the RFM model. Moreover analysis of each cluster customer behavior, the model of feed-forward neural network is predicted the cluster number of customers based on their demographic and behavioral characteristics.

Background and Purpose: Segmentation is one of the most useful tools for a better understanding of customer diversity, and managers can allocate marketing activities efficiently and effectively to homogeneous and prioritized customer groups. Earlier research on the Segmentation of financial services customers show that these studies often rely on socio-demographic information and are less paid to psychological and behavioral variables that indicate why customers are selected and preferred. Therefore, the present study aimed to segment the banking Industry market based on the attitude and financial behavior of customers. Methodology: In order to discover the desired segments, studies were done based on the theme analysis. Then, through a survey of internal and external experts, the indicators were formulated in 56 questions and distributed among the 30 clients as a pre-test. After pre-test confirmation and determining of the sample size, questionnaires were distributed through random cluster sampling and finally 355 full questionnaires were received. Results: Based on factor analysis, 14 attitudinal factors and financial behavior were identified and analyzed using cluster analysis of participants in four sections: Paid Savers, Financial Experts, Independent Concerned Spenders and Indifferent Followers. Conclusion: Bank customers have significant differences in terms of demographic characteristics, attitudes and financial behavior. Therefore, in this research, in order to improve the level of customer interaction with the bank, they are divided into four groups and appropriate marketing solutions and recommendations for each of them were presented.

Purpose: The future of the construction Industry is increasingly influenced by new technologies. In order to adopt appropriate strategies in facing new technologies, it is necessary to know the possible futures of the construction Industry. This research was done with the aim of explaining the technological uncertainties and compiling the future scenarios of the construction Industry.  Method: The research method is applied and was carried out with a combination of quantitative and qualitative methods. First, the library study was used to determine the technological drivers, then the Structural Analysis was used to explain the technological uncertainties, and finally, the Schwartz method was used to compile the scenarios. The statistical population is experts of construction Industry. Findings: Nine technological uncertainties affecting the future of the construction Industry have been identified and for each of them, three states of decline, stagnation and progress have been considered. Data analysis by Scenario Wizard shows eight probable scenarios. The portfolio of scenarios including four groups of progress scenarios, towards progress, towards stagnation, and towards wane has been compiled. Conclusion: In the progress scenario, the 89% of uncertainties have developed. In  towards progress, 56% of the factors are in the development status, which indicates the development of the technological factor application. In the stagnation scenario, no progress has been made in the application of uncertainties and they are in a static state. In towards wane, uncertainties have been placed in a situation of reduced use.

Good decision-making and adoption of effective marketing, sale strategies and tactics are subjected to a proper understanding and identification of consumers and purchasers in organizations. Nowadays, marketing researchers are attempting to identify behavior of consumers in order to make suitable solutions for better and effective sale in order to expand market share. Manufacturer should be aware of perceptual, belonging and behavioral layers of potential purchasers in order to produce distribute and promote products regarding their decision-making structures and mechanisms. Innovators and designers of products can find expectations and satisfaction aspects of individuals by modeling and identifying behavioral pattern of consumers. A data mining-based approach was proposed in this research to determine marketing policies related to each customer within ordering type investigating customers’ behavior. The proposed methodology used clustering of data extracted from database related to customer behavior. Then, this method supported two types of marketing policies considering financial class which customer belongs. In first policy, the customer consolidation or retention in its current financial class in underpinned; this policy introduces those projects or products that may be unknown or unused for customer making them loyal to organization or prolonging their orders for long term. Second policy is named motivational or promotional policy; this policy performs to encourage customers to be in higher financial classes. In this regard, some projects or products will be presented purposefully based on the analysis of behavior of customers at higher financial classes.

Introduction: In the insurance Industry, customers’ systematic identification and clustering is a major concern not only for marketers but for the entire organization, for this reason, Customer Segmentation helps target organizations to customize their services and prioritize products based on their profitability. Methodology: This research is an applied, descriptive and quantitative study aiming to cluster customers by using k-means clustering. The data were collected from 800 customers of Pasargad insurance company in the city of Shiraz using the random sampling technique. The data on length, recency, frequency and monetary issues were collected by considering research ethics principles. Customers were clustered into four groups including key, prodigal, intermittent and uncertain by using the K-means method. Eventually, the customers’ lifetime value was determined Results and Discussion: Clustering has been carried out in four categories, including key clients whose contribution to a sample of 800 insurance customers is 24. 2%. This group of customers has high financial value characteristics and high purchase frequencies. They are ranked first in terms of lifetime value. Based on the findings, the indicator of the volume of financial exchange is an index that graduates the other indices placing a client in the position of key a customer. Prodigal customers featured with high financial characteristics, low shopping frequencies and a 25. 8% share of insurance customers are in the second category and ranked second in terms of lifetime value. The third group of customers, having a share of 33. 4% of the insurance customers, low purchasing value characteristics and high purchase frequency, are frequent customers who are in the third rank of life value. The last group of customers is uncertain ones who account for a significant 16. 6% share of customers. They have monetary value characteristics and low purchasing frequency and are ranked last in terms of lifetime value. They are among the customers who have no significant trade volumes and the lowest value of the purchasing iteration index, regardless of the time indicators associated with these customers. This puts them in the cluster of uncertain customers with a 16. 6% share in the selected statistical sample. This is because they have different and irregular financial behaviors during a certain period. So, it may not be profitable to give them services. Conclusion: Determining the share and importance of customer groups based on customer lifetime value is one of the results of this study. While keeping prodigal customers, it is recommended to managers and marketing planners of the insurance Industry to pay special attention to key and intermittent customers. From a managerial perspective, customer Segmentation is a very important issue in the insurance Industry. It can be a subject for studies and applied planning in every sector. Also, the specialization of insurance Industry services in proportion to the customers' lifetime value, expectations and preferences based on scientific Segmentation and customer data is one of the managerial recommendations. Another aspect that can be suggested to the managers of the insurance Industry based on the results of this study is paying attention to the characteristics of customers in each cluster. Among these four groups, the cluster of key customers has a significant volume of transactions and length of the period of communication and repetition of insurance transactions. It also requires insurance companies to pay special attention to these customers. Next to this group are prodigal customers who have mostly low repetition of their insurance transactions, while the volume of turnover of this group is significant for the insurance Industry. The importance of this group increases when these people have the lowest share in the overhead costs of insurance services for insurance companies, and, at the same time, their premiums are relatively higher than other groups. This makes managers pay more attention to this group. However, due to the low contact of these people with the employees of insurance companies, it is possible that they will receive less attention in relational marketing issues and promotional measures of this group. Accordingly, it is necessary for the managers of the insurance company to recognize generous customers and make special plans for them, especially in relationship marketing. In addition, given that a good number of the insurance company customers are uncertain clients, special planning is necessary to maintain and increase their loyalty. Another group identified in this study is that of the intermittent customers. This group of customers receive a relatively large amount of insurance services, while the premiums received from this group are not significant compared to the other groups. Identifying this type of customers and defining ways to retain them while reducing referrals to this group of customers is essential.

Nowadays, the analysis of customer behavior is necessary for active organizations in the field of banking which deal with many customers with different characteristics. In recent years, Shahr Bank of Iran has faced many problems because of poor customer-orientedness and customer services. Therefore, to solve the existing problem, the current study concentrated on segmenting the customers of Shahr Bank based on their expected benefits. This study is applied in terms of purpose and descriptive-survey research in terms of data collection and analysis. To achieve the research objectives, through field studies and exploratory interviews with customers and banking experts, 165 benefits were extracted. Then, using expert questionnaires, the number of these benefits was reduced, and through factor analysis, nine factors were identified as the most important expected benefits. Moreover, using cluster analysis, four customer segments were extracted, namely benefit-oriented, peace-oriented, interest-oriented, and moderate ones. Finally, a suitable marketing solution was provided to the bank consistent with the most important features of the segments. For bank managers, this paper provides an appropriate view to identify customers' preferencesas an important factor in customer-orientedness and bank profitability.

Objective: Today in the Insurance Industry, playing the role customer of follow the service providers has become the guiding providers. Therefore, due to the difference in profitability, volume and type of purchase, loyalty, risks, behavioral and demographic dimensions we seek to create significant demarcations between them with using the customer Segmentation approach that by recognizing the characteristics of each of these different groups increase the competitive power and success of activists in this field be provided. Method: Customer Segmentation Using a two-step cluster analysis with scalable cluster analysis algorithm with respect to the feasibility of this technique in the analysis of continuous and categorical variables was performed. Dominant patterns in customer grouping were identified. Then using discriminate analysis, clustering validity was examined. Findings: According to the defined indicators, customers were divided into six clusters. Variables discounts offered, profit, claims ratio, volume and number of insurances purchased the highest role in the separation of the clusters. Also, in terms of profitability, all clusters are different from each other. In terms of absorption method debtor cluster with Passers and favourite cluster with credit worthy are different. Conclusion: Insurance companies can use the customer Segmentation technique based on the criteria proposed in this article and identify their characteristics, identify the position of each group in the company's profit or loss, predict and draw the behavior pattern of potential and future customers with similar characteristics. Determine the target market and appropriate marketing strategy to increase their competitiveness compared to other competitors.