بخش‌بندی خوشه‌ای و مدل ارتباطی ال آر اف ام مشتریان صنعت بیمه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اجتماعی دانشگاه شیراز

2 استادیار گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اجتماعی دانشگاه شیراز

3 کارشناس ارشد مدیریت بازرگانی، دانشکده اقتصاد، مدیریت و علوم اجتماعی، دانشگاه شیراز

چکیده

در صنعت بیمه شناخت و دسته‌بندی نظام‌مند مشتریان نه‌تنها برای بازاریابان، بلکه برای کل سازمان یک دغدغه اساسی است. هدف این پژوهش به عنوان یک تحقیق کاربردی، توصیفی و کمّی، خوشه‌بندی مشتریان با رویکرد داده‌کاوی (کی میانگین) می‌باشد. جامعه آماری پژوهش مشتریان بیمه پاسارگاد در شهر شیراز بوده که 800 نفر به صورت تصادفی انتخاب و با رعایت اصول اخلاق پژوهشی، داده‌های چهار عامل مدت ارتباط، تازگی، تعداد تراکنش‌ها  و حجم خرید نمونه آماری، از سامانه استخراج  و با روش کی-میانگین به چهار خوشه کلیدی، دست و دل­باز، متناوب و نامطمئن تقسیم شدند. در نهایت ارزش وزنی طول عمر آنها تعیین شد. یافته‌ها نشان می‌دهد 2/24 درصد از مشتریان در گروه مشتریان کلیدی با بالاترین ارزش طول عمر قرار دارند  که حفظ و نگهداری از آن‌ها برای شرکت بیمه بسیار ضروری به نظر می‌رسد. مشتریان متناوب با بیشترین سهم از کل مشتریان بیمه با 4/33 درصد، در رتبه دوم ارزش طول عمر قرار گرفتند که شرکت‌های بیمه برای بالابردن ارزش مالی مبادلات این گروه نیازمند برنامه‌ریزی است. مشتریان دست ‌و دل‌باز با 8/25درصد و مشتریان نامطمئن با 6/16 درصد به ترتیب در رده­های بعدی گروه فراوانی مشتریان در خوشه­ها قرار دارند. تعیین سهم و اهمیت خوشه‌های مشتریان بر اساس ارزش وزنی طول عمر مشتری از نتایج این پژوهش است که ضمن نگهداشت مشتریان دست و دل­باز، توجه خاص به دو گروه مشتریان کلیدی و متناوب را به مدیران و برنامه‌ریزان بازاریابی صنعت بیمه توصیه می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

The segmentation of insurance industry customers using clustering techniques and the LRFM communication model

نویسندگان [English]

  • Abolqasem Ebrahimi 1
  • Kazem Askarifar 2
  • Amin Nikbakht 3
1 Associate Professor, in Business Management, Shiraz University, Shiraz, Iran
2 Assistant Professor, in Business Management, Shiraz University, Shiraz, Iran
3 Master of Business Management, Shiraz University, Shiraz, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Insurance
  • Customer lifetime value
  • LRFM model
  • K-means algorithm
  • Data mining

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کاوش‌های مدیریت بازرگانی
دوره 13، شماره 25
شهریور 1400
صفحه 557-585

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