臺灣博碩士論文加值系統

現今企業的競爭重心已由過去的產品導向轉為客戶導向，而行銷管理可以說是企業直接影響客戶和企業關係的重要因素之一。因此本研究將針對行銷管理議題之一--行銷最佳化，提出一解決方法。行銷最佳化是指在多產品或服務、多行銷管道、多顧客的情況下，應該要如何安排何種產品或服務透過何種行銷管道推銷給某位顧客，使得企業的利益能夠達到最大。首先我們會建立一個行銷最佳化的模型，並以某電信公司的資料為基礎，代入模型之中。值得一提的是我們將模型中的不確定因素以某種分配來表示。為了解決這樣的最佳化問題，本研究將使用模擬最佳化的技術來求解行銷最佳化的模型。其中模擬最佳化中所用的的最佳化技術為基因演算法，而模擬技術則是使用蒙地卡羅模擬。

The competition focus of enterprises migrates from product-oriented to customer-oriented. Marketing management is one of the important factors that enterprises affect the relationship between customer and enterprise. So our research wants to solve one problem of marketing management--campaign optimization. Campaign optimization means finding a way to offer the products or services across all customers through an appropriate channel so that the enterprise will gain the greatest benefit. First, we establish a campaign optimization model and feed a specific telecom’s data into the model. In addition, we use some kind probability distribution function to represent the uncertain factor. In order to solve such optimization problem, we apply simulation-optimization to solve the campaign optimization model. The optimization technique of simulation-optimization we apply is genetic algorithms and the simulation technique is Monte Carlo simulation.

目錄
書名頁…………………………………………………………………i
論文口試委員審定書…………………………………………………ii
授權書…………………………………………………………………iii
博碩士論文電子檔案上網授權書……………………………………iv
授權書(博碩士論文)…………………………………………………v
中文摘要………………………………………………………………vi
英文摘要………………………………………………………………vii
誌謝……………………………………………………………………viii
目錄……………………………………………………………………ix
表目錄…………………………………………………………………x
圖目錄…………………………………………………………………xi
方程式目錄……………………………………………………………xii
Contents
1. Introduction……………………………………………………………1
2. Literatures Review……………………………………………………4
2.1 Market Campaign Management…………………………………………4
2.2 Campaign Optimization………………………………………………6
2.3 Customer Lifetime Value……………………………………………8
2.4 Optimization and Uncertainty………………………………………9
2.5 Genetic Algorithms…………………………………………………10
2.6 Monte Carlo Simulation……………………………………………12
2.7 Simulation-optimization……………………………………………12
3. Methodology……………………………………………………………15
3.1 Campaign Optimization Model……………………………………16
3.2 The Simulation-optimization Approach…………………………18
3.3 Parameters Setting of Simulation-optimization Mechanism…19
4. Experiments……………………………………………………………20
4.1 The Dataset……………………………………………………………20
4.2 Simulation-optimization Techniques……………………………23
4.3 The Results……………………………………………………………23
5. Discussion and Conclusions………………………………………27
6. Reference………………………………………………………………30
Appendix A…………………………………………………………………36
Appendix B…………………………………………………………………37

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