k-means algorithm

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. 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 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.   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.

In today's competitive world, applying new techniques to business development has a great impact. The restaurant industry is no exception. Therefore, in this research, using new methods of knowledge discovery and data mining, customer data of chain restaurant is investigated. The purpose of this study was to explore customer behavior patterns using data mining methods.In this study, one million and five hundred thousand customer records were reviewed in five branches of a chain restaurant and two stages of clustering modeling using RFM method and then classification modeling were performed on the data and the behavior rules chain restaurant customers were extracted. The results of this study have helped to identify the loyal and profitable customers of the chain restaurant which has led to the improvement of the profitability of the chain restaurant. One of the innovations of this research has been the communication between clustering and classification results.

In recent years, a new issue called "multi-criteria clustering" has emerged that aims at grouping alternatives into homogeneous classes called clusters according to different evaluation criteria. Following the related studies in literature, by combining K-means algorithm and PROMETHEE technique, this paper aims to present a new multi-criteria clustering method. The parameters of the problem are the cluster separator profiles which genetic algorithm (GA) is used to optimize them. In the modeling process in each stage of updating responses, alternatives allocate to the nearest cluster according to the distance of their pure flow of privileges from the profiles. The mutation operator is only applied when the chromosomes’ similarity level in each population reaches to a certain level which this intelligence reduces the computation time. Finally, by simulating the proposed algorithm and some well-known clustering algorithms based on the several financial databases the efficiency of the algorithm compared to other algorithms. The results show the algorithm, in addition to determine the optimal number of clusters in comparison to other algorithms, also provides better results.