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研究生(外文):Ching-Wen Tu
論文名稱(外文):On the Relationship of Insurance Agents Performance and Sales Target
指導教授(外文):Seng-Cho ChouLing-Ling Wu
口試委員(外文):Tzy-Yuan Chou
中文關鍵詞:資料探勘分群演算法獎勵制度人壽保險公司資料視覺化80-20 法則
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在本篇論文中,我們先透過資料視覺化 (Data Visualization) 的方式來了解公司的整體營運狀況,接下來我們利用分群演算法 (Clustering Algorithm) 來將保單以及產品的貢獻度進行分類,在特定的保險類型之產品對公司的整體收益有比較高的貢獻度。除了分析產品對公司的貢獻之外,我們也使用分群演算法來將每個業務員每年的貢獻進行分類,不同組的業務員的貢獻有不小的差距,因此,我們觀察到業務員在每年的表現中是有存在不同的差異。
Implementing the incentive program is very common in the life insurance company. However, the real influence of these incentive systems cannot be easily calculated, and the organization suffers that it is unable to predict the actual income annually. The company usually faces great gaps between its target and the real operation with the incentive programs. The company wants to know the contribution of each incentive program when they implemented.
In our thesis, we use the visualization method to understand the overall trend of the company. After that, we use clustering methods to separate the performance of insurance policies and products. We discover that some of the insurance types of products have a higher contribution. We also use clustering methods to separate the performance of each agent and we discover that each group of performance have gaps between them. We realize that the contribution between agents has great diversity.
Afterward, we analysis the agents who have worked in the company for a long time to discover the relationship between agents and the company. We observe that the agents can generally be divided into two groups and those who in the top group is around twenty percent of agents in the company and their contribution to the company is near eighty percent. When we fit the same model back to all the agents in every year, we have the same conclusion.
In addition to discovering the rule of 80-20 exists in the company, we observe that the company can improve their income if they launch the products meet the market requirement and provide the incentive programs that attract agents. In the future, the company can spur its performance growth rapidly when they meet the two factors that we mentioned above.
誌謝 i
中文摘要 ii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Objectives 2
1.3 Thesis object and scope 3
1.4 Thesis Outline 3
Chapter 2 Background and Related Work 5
2.1 Behavior under the Incentive Program 5
2.1.1 Dysfunctional Outcome 6
2.1.2 Important of Incentive Factors 7
2.2 Performance Prediction 7
2.2.1 Regression Model 8
2.2.2 RFM Model 9
Chapter 3 Method 11
3.1 Dataset 11
3.2 Process 15
3.3 Data Preparation 16
3.4 Data Visualization 20
3.5 Data Grouping 21
3.5.1 Product Clustering 21
3.5.2 Agent Clustering 22
3.6 Data Analysis 22
3.6.1 5-Year Working Agent Clustering 23
3.6.2 Each Year Agent Clustering 24
Chapter 4 Experiment and Result 25
4.1 General Data Visualization 25
4.2 Clustering by Product 28
4.3 Clustering by Agent 32
4.4 5-Year Working Agent Clustering 37
4.5 Each Year Agent Clustering 44
4.6 Top 20 Percent Agents’ Performance 48
Chapter 5 Conclusion and Future Work 51
5.1 Conclusion 51
5.2 Recommendation 52
5.3 Future Work 53
Appendix A. PCA Result of Five-Year Agents 59
Appendix B. PCA Result of Each Year Agent 62
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