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題名 將客戶價值與財務績效連結: 客戶價值指數與商業應用
Connecting Customer Value to Financial Performance: The Customer Value Index and Business Applications作者 李東桓
Lee, Dong-Hwan貢獻者 蔡政憲
Jason Tsai
李東桓
Lee, Dong-Hwan關鍵詞 客戶價值指數
客戶相關財務指標
財務績效
客戶策略
動態互動
分析引導決策
CVI-市場同步
CDSTM
客戶留存
策略協同
Customer Value Index
Customer-Related Financial Metrics
Financial Performance
Customer Strategy
Dynamic Interaction
Analytics-Guided Decision Making
CVI-Market Sync.
CDSTM
Customer Retention
Strategic Alignment日期 2025 上傳時間 1-Sep-2025 15:52:59 (UTC+8) 摘要 在2025年競爭激烈的全球商業環境中，以客戶為中心的策略已成為實現財務成功和維持競爭優勢的基石，因為企業必須應對快速變化的市場動態和日益提高的消費者期望。從目標數位行銷到個性化忠誠計畫等客戶相關活動的激增，受到社交網絡系統（SNS）數據激增的推動，創造了一個複雜的生態系統，使企業難以管理數據過載、協調跨部門努力並在不斷增加的行銷支出中進行創新。 研究必要性與問題陳述 缺乏一個全面、標準化的指標來量化客戶價值創造與財務成果之間的複雜關係，構成了策略決策的關鍵障礙，使企業無法充分發揮以客戶為中心的策略。傳統指標，如客戶終身價值（CLV）或淨推薦值（NPS），往往僅聚焦於特定面向，無法全面捕捉客戶在獲取、參與、保留和盈利能力方面的整體影響。這一差距限制了企業評估以客戶為中心舉措有效性的能力，阻礙了最佳資源分配和長期財務目標的對齊，凸顯了需要一個整合框架來推動可持續成長的需求。 缺乏量化客戶價值的標準化指標，結合本研究獨創提出的客戶相關財務指標（CRFMs）框架，進一步強調了如客戶價值指數（CVI）這樣整合方法的需求。 研究概述與方向 在第一部分，CVI 通過八個關鍵因子—客戶收入效率、行銷影響、參與潛力、忠誠承諾、購買動能、服務滿意度、創新參與和品牌資產—在基於回歸的模型中構建，該模型通過20個客戶相關財務指標（包括收入、客戶獲取成本和流失率）將客戶價值與財務成果聯繫起來。主成分分析（PCA）用於推導這些因子，解釋了80%的數據變異，採用標準化的企業財務記錄和市場數據集，確保客戶動態的穩健表達。該模型的效度通過因子分析、結構方程模型和敏感性檢查等先進統計方法得到驗證，與基礎客戶權益理論保持一致。 背景 在第二部分，研究開發了客戶動態與策略轉型模型（CDSTM），並通過深入的亞馬遜案例研究進行驗證，展示了其在優化CRFM驅動的互動策略方面的實際效用。第二部分引入了CVI-Market Sync框架，整合CVI與CDSTM，以在短期（T1：3-6個月）、中期（T2：6-12個月）和長期（T3：1-3年）階段中，將客戶價值策略與市場動態同步。CVI-Market Sync應用於多個行業，包括電子商務（例如亞馬遜、阿里巴巴）、科技（例如微軟、蘋果）、媒體（例如Netflix、迪士尼）和零售（例如沃爾瑪、Costco），以及台灣中小企業，其中CRFM追蹤率揭示了策略差距。此外，研究開發了一個可擴展性驗證工具，以增強策略決策、改善客戶保留並支持全球企業的分析導向資源優化。 關鍵詞 客戶價值指數、客戶相關財務指標、財務績效、客戶策略、動態互動、分析導向決策、CVI-Market Sync、CDSTM、客戶保留、策略對齊
Background In the fiercely competitive global business landscape of 2025, customer-centric strategies have become the cornerstone for achieving financial success and sustaining a competitive edge, as firms grapple with rapidly evolving market dynamics and heightened consumer expectations. The surge in customer-related activities ranging from targeted digital marketing to personalized loyalty programs has been amplified by the proliferation of data from Social Networking Systems (SNS), creating a complex ecosystem where firms struggle to manage data overload, align cross-departmental efforts, and innovate amidst escalating marketing expenditures. Research Necessity and Problem Statement The absence of a comprehensive, standardized metric to quantify the intricate relationship between customer value creation and financial outcomes poses a critical barrier to strategic decision-making, leaving firms unable to harness customer-focused strategies fully. Traditional metrics like Customer Lifetime Value (CLV) or Net Promoter Score (NPS) often focus narrowly on specific dimensions, failing to capture the holistic impact of customer interactions across acquisition, engagement, retention, and profitability. This gap limits firms’ ability to evaluate the effectiveness of customer-centric initiatives, impeding optimal resource allocation and alignment with long-term financial objectives, and highlighting the need for an integrated framework to drive sustainable growth. The absence of a standardized metric to quantify customer value, coupled with the novel introduction of Customer-Related Financial Metrics (CRFMs)—a framework uniquely proposed in this study—underscores the need for an integrated approach like the Customer Value Index (CVI). Research Overview and Direction In Part 1, the CVI is constructed using 8 Key-Factors—Customer Revenue Efficiency, Marketing Impact, Engagement Potential, Loyalty Commitment, Purchase Momentum, Service Satisfaction, Innovation Engagement, and Brand Equity—within a regression-based model that links customer value to financial outcomes through 20 Customer-Related Financial Metrics, including Revenue, Customer Acquisition Cost, and Churn Rate. Principal Component Analysis is employed to derive these factors, explaining 80% of the data variance, using normalized corporate financial records and market datasets, ensuring robust representation of customer dynamics. The model’s validity is confirmed through advanced statistical methods like factor analysis, structural equation modeling, and sensitivity checks, aligning with foundational customer equity theories. In Part 2, the study develops the Customer Dynamics and Strategic Transition Model, validated through an in-depth Amazon case study, demonstrating its practical utility in optimizing CRFM-activated interactions strategies. Part 2 introduces the CVI-Market Sync. framework, integrating CVI and CDSTM to synchronize customer value strategies with market dynamics across short-term (T1: 3-6 months), medium-term (T2: 6-12 months), and long-term (T3: 1-3 years) phases. CVI-Market Sync. is applied across diverse industries, including e-commerce (e.g., Amazon, Alibaba, technology (e.g., Microsoft, Apple), media (e.g., Netflix, Disney), and retail (e.g., Walmart, Costco), as well as SMEs in Taiwan, where CRFM tracking rates reveal strategic gaps. Additionally, the study develops a scalability validation tool to enhance strategic decision-making, improve customer retention, and support analytics-guided resource optimization across global corporations Keywords Customer Value Index, Customer-Related Financial Metrics, Financial Performance, Customer Strategy, Dynamic Interaction, Analytics-Guided Decision Making, CVI-Market Sync., CDSTM, Customer Retention, Strategic Alignment參考文獻 1. Almquist, E., Senior, J., & Bloch, N. (2016). The elements of value: Measuring—and delivering—what consumers really want. Harvard Business Review, 94(9), 46–53. 2. Damodaran, A. (2012). Investment valuation: Tools and techniques for determining the value of any asset (3rd ed.). Wiley. 3. Deloitte. (2024). 2024 customer experience trends report. Deloitte Insights. Retrieved March 20, 2025, from https://www2.deloitte.com/us/en/insights/topics/customer-experience.html 4. FactSet. (2023). Global financial and market analytics. 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Retrieved March 20, 2025, from https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations-2023 17. Mergent Online. (2023). Mergent corporate financial database. Retrieved March 20, 2025, from https://www.mergentonline.com 18. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40. 19. Porter, M. E. (2008). Competitive advantage: Creating and sustaining superior performance. Free Press. 20. Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46–54. 21. Reichheld, F. F., & Sasser, W. E., Jr. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105–111. 22. Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109–127. https://doi.org/10.1509/jmkg.68.1.109.24030 23. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590 24. Similarweb. (2023). Similarweb digital intelligence platform. Retrieved March 20, 2025, from https://www.similarweb.com 25. Statista. (2023). Statista global industry and market database. Retrieved March 20, 2025, from https://www.statista.com 26. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. McGraw-Hill Education. 27. SurveyMonkey. (2023). SurveyMonkey market research solutions. Retrieved March 20, 2025, from https://www.surveymonkey.com 28. Wooldridge, J. M. (2013). Introductory econometrics: A modern approach (5th ed.). South-Western Cengage Learning. 29. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1993). The nature and determinants of customer expectations of service. Journal of the Academy of Marketing Science, 21(1), 1–12. https://doi.org/10.1007/BF02894440 30. Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2009). Services marketing: Integrating customer focus across the firm (5th ed.). McGraw-Hill Education. 描述 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
112933039資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112933039 資料類型 thesis dc.contributor.advisor 蔡政憲 zh_TW dc.contributor.advisor Jason Tsai en_US dc.contributor.author (Authors) 李東桓 zh_TW dc.contributor.author (Authors) Lee, Dong-Hwan en_US dc.creator (作者) 李東桓 zh_TW dc.creator (作者) Lee, Dong-Hwan en_US dc.date (日期) 2025 en_US dc.date.accessioned 1-Sep-2025 15:52:59 (UTC+8) - dc.date.available 1-Sep-2025 15:52:59 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2025 15:52:59 (UTC+8) - dc.identifier (Other Identifiers) G0112933039 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159216 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營管理英語碩士學位學程(IMBA) zh_TW dc.description (描述) 112933039 zh_TW dc.description.abstract (摘要) 在2025年競爭激烈的全球商業環境中，以客戶為中心的策略已成為實現財務成功和維持競爭優勢的基石，因為企業必須應對快速變化的市場動態和日益提高的消費者期望。從目標數位行銷到個性化忠誠計畫等客戶相關活動的激增，受到社交網絡系統（SNS）數據激增的推動，創造了一個複雜的生態系統，使企業難以管理數據過載、協調跨部門努力並在不斷增加的行銷支出中進行創新。 研究必要性與問題陳述 缺乏一個全面、標準化的指標來量化客戶價值創造與財務成果之間的複雜關係，構成了策略決策的關鍵障礙，使企業無法充分發揮以客戶為中心的策略。傳統指標，如客戶終身價值（CLV）或淨推薦值（NPS），往往僅聚焦於特定面向，無法全面捕捉客戶在獲取、參與、保留和盈利能力方面的整體影響。這一差距限制了企業評估以客戶為中心舉措有效性的能力，阻礙了最佳資源分配和長期財務目標的對齊，凸顯了需要一個整合框架來推動可持續成長的需求。 缺乏量化客戶價值的標準化指標，結合本研究獨創提出的客戶相關財務指標（CRFMs）框架，進一步強調了如客戶價值指數（CVI）這樣整合方法的需求。 研究概述與方向 在第一部分，CVI 通過八個關鍵因子—客戶收入效率、行銷影響、參與潛力、忠誠承諾、購買動能、服務滿意度、創新參與和品牌資產—在基於回歸的模型中構建，該模型通過20個客戶相關財務指標（包括收入、客戶獲取成本和流失率）將客戶價值與財務成果聯繫起來。主成分分析（PCA）用於推導這些因子，解釋了80%的數據變異，採用標準化的企業財務記錄和市場數據集，確保客戶動態的穩健表達。該模型的效度通過因子分析、結構方程模型和敏感性檢查等先進統計方法得到驗證，與基礎客戶權益理論保持一致。 背景 在第二部分，研究開發了客戶動態與策略轉型模型（CDSTM），並通過深入的亞馬遜案例研究進行驗證，展示了其在優化CRFM驅動的互動策略方面的實際效用。第二部分引入了CVI-Market Sync框架，整合CVI與CDSTM，以在短期（T1：3-6個月）、中期（T2：6-12個月）和長期（T3：1-3年）階段中，將客戶價值策略與市場動態同步。CVI-Market Sync應用於多個行業，包括電子商務（例如亞馬遜、阿里巴巴）、科技（例如微軟、蘋果）、媒體（例如Netflix、迪士尼）和零售（例如沃爾瑪、Costco），以及台灣中小企業，其中CRFM追蹤率揭示了策略差距。此外，研究開發了一個可擴展性驗證工具，以增強策略決策、改善客戶保留並支持全球企業的分析導向資源優化。 關鍵詞 客戶價值指數、客戶相關財務指標、財務績效、客戶策略、動態互動、分析導向決策、CVI-Market Sync、CDSTM、客戶保留、策略對齊 zh_TW dc.description.abstract (摘要) Background In the fiercely competitive global business landscape of 2025, customer-centric strategies have become the cornerstone for achieving financial success and sustaining a competitive edge, as firms grapple with rapidly evolving market dynamics and heightened consumer expectations. The surge in customer-related activities ranging from targeted digital marketing to personalized loyalty programs has been amplified by the proliferation of data from Social Networking Systems (SNS), creating a complex ecosystem where firms struggle to manage data overload, align cross-departmental efforts, and innovate amidst escalating marketing expenditures. Research Necessity and Problem Statement The absence of a comprehensive, standardized metric to quantify the intricate relationship between customer value creation and financial outcomes poses a critical barrier to strategic decision-making, leaving firms unable to harness customer-focused strategies fully. Traditional metrics like Customer Lifetime Value (CLV) or Net Promoter Score (NPS) often focus narrowly on specific dimensions, failing to capture the holistic impact of customer interactions across acquisition, engagement, retention, and profitability. This gap limits firms’ ability to evaluate the effectiveness of customer-centric initiatives, impeding optimal resource allocation and alignment with long-term financial objectives, and highlighting the need for an integrated framework to drive sustainable growth. The absence of a standardized metric to quantify customer value, coupled with the novel introduction of Customer-Related Financial Metrics (CRFMs)—a framework uniquely proposed in this study—underscores the need for an integrated approach like the Customer Value Index (CVI). Research Overview and Direction In Part 1, the CVI is constructed using 8 Key-Factors—Customer Revenue Efficiency, Marketing Impact, Engagement Potential, Loyalty Commitment, Purchase Momentum, Service Satisfaction, Innovation Engagement, and Brand Equity—within a regression-based model that links customer value to financial outcomes through 20 Customer-Related Financial Metrics, including Revenue, Customer Acquisition Cost, and Churn Rate. Principal Component Analysis is employed to derive these factors, explaining 80% of the data variance, using normalized corporate financial records and market datasets, ensuring robust representation of customer dynamics. The model’s validity is confirmed through advanced statistical methods like factor analysis, structural equation modeling, and sensitivity checks, aligning with foundational customer equity theories. In Part 2, the study develops the Customer Dynamics and Strategic Transition Model, validated through an in-depth Amazon case study, demonstrating its practical utility in optimizing CRFM-activated interactions strategies. Part 2 introduces the CVI-Market Sync. framework, integrating CVI and CDSTM to synchronize customer value strategies with market dynamics across short-term (T1: 3-6 months), medium-term (T2: 6-12 months), and long-term (T3: 1-3 years) phases. CVI-Market Sync. is applied across diverse industries, including e-commerce (e.g., Amazon, Alibaba, technology (e.g., Microsoft, Apple), media (e.g., Netflix, Disney), and retail (e.g., Walmart, Costco), as well as SMEs in Taiwan, where CRFM tracking rates reveal strategic gaps. Additionally, the study develops a scalability validation tool to enhance strategic decision-making, improve customer retention, and support analytics-guided resource optimization across global corporations Keywords Customer Value Index, Customer-Related Financial Metrics, Financial Performance, Customer Strategy, Dynamic Interaction, Analytics-Guided Decision Making, CVI-Market Sync., CDSTM, Customer Retention, Strategic Alignment en_US dc.description.tableofcontents I. Introduction………17 1. Customer-Centric Strategies and Financial Performance……17 1.1. The Role of Customer-Related Activities in Global Markets……17 1.2. Operational Framework: Direct and Indirect Departments………17 1.3. Financial Metrics and Implications…………18 1.4. Financial Contributions and Strategic Outcomes………19 1.5. Introduction of Customer Value Index (CVI) ………19 1.6. Glossary of Abbreviations…………20 II. Literature Review………21 1. Customer Value and Financial Performance………21 2. Customer-Centric Strategies and Organizational Dynamics………22 3. Quantitative Approaches to Customer Value Measurement……23 4. Dynamic Interaction Models and Customer Value………23 5. Gaps in Existing Research…………24 5.1. Limitations of Existing Customer Value Metrics…………25 III. Research Model Overview…………27 1. Introduction………27 2. Part 1: Quantifying Customer Value’s Financial Impact……27 3. Part 2: Dynamic Application of Customer Value…………28 4. Overall Research Model………28 IV. Methodology_Part 1………29 1. Customer Value Index: Framework for Measuring Customer-Centered Value………29 1.1. Quantifying the Financial Impact of Customer-Centric Strategies………29 1.2. Conceptual Design………29 1.3. Methodological Construction: Regression Model………30 1.4. Validation and Robustness………30 2. Selection Criteria and Rationale for Target Companies……30 2.1. Objectives of Company Selection…………30 2.2. Selection Criteria…………31 2.3. Rationale for Diversity and Representation…………31 2.4. Contribution to CVI Validation………32 3. Research Customer-Related Financial Metrics (CRFMs)…………33 3.1. Importance………………33 3.2. Selection for Analysis…………33 3.3. Integration into the CVI Model………………33 3.4. Factor Equation for CVI Regression Model…………36 3.5. Data Collection Process…………37 4. PCA for Factor Equation Development…………37 4.1. Background and Rationale for PCA in Factor Selection……………37 4.2. Purpose and Role of PCA……………38 4.3. Theoretical Background and Alignment…………39 4.4. Application of PCA for Key-Factors Derivation…………39 4.5. Principal Component Generation…………45 4.6. Explained Variance Ratio Assessment…………49 4.7. Identification of High-Loading Metrics……………51 4.8. Review of the PCA Process……………54 4.9. Factor Classification and Construction of the 8 Key-Factors…………55 4.10. Result: Network among 8 Key-Factors and CRFMs……………57 4.11. Validation and Application of 8 Key-Factors……………59 4.12. Process Flow for CVI Model Development and Validation…………60 4.13. Conclusion and Implications……………62 4.14. Application in the Study……………64 4.15. PCA-Derived Core Metrics and Expanded Factor Equations………65 5. Explanation of the 8 Factor Equations for the CVI Model…………67 5.1. Basic Structure of the 8 Factor Equations and Theoretical Rationale…………67 5.2. Theoretical Foundation……………69 5.3. Individual Explanation………………69 5.4. Introduction to Factor Equations……………71 6. Introduction: Weight Computation Methodology…………76 6.1. Introduction of Weight Computation for Model 1 and Model 2………76 6.2. Explanation of Finance-Centric and Customer-Centric……77 7. The Weight Computation Methodology………78 7.1. Weight Computation for Model 1 (ROE-Based)………78 7.2. Weight Computation Formula ……………80 7.3. Determining the Weighting Ratio: AHP & Sensitivity Analysis……81 7.4. Computation of Final Weight for Model 1………82 7.5. Weight Computation for Model 2 (CLV-Based)……………82 7.6. OLS (Ordinary Least Squares) Regression for Intermediate Weights……84 7.7. Weight Computation Formula…………84 7.8. Computation of Final Weights for Model 2…………85 7.9. Overview of the CVI Computation Process…………87 7.10. Computation of CVI……………87 8. Scaling CVI Values for Enhancing Interpretability………90 V. Results_Part 1……………………95 1. Statistical Validation and Interpretation of CVI Framework………95 1.1. Introduction: Pearson Correlation Analysis……………95 1.2. Statistical Analysis Result for Model 1………………96 1.3. Statistical Analysis Result for Model 2…………98 2. Integration of Model 1 & 2 and the Implications for the CVI…………100 2.1. Consolidated Conclusion………100 2.2. Theoretical and Practical Validity of the CVI Framework…………102 2.3. Model Explanatory Power and Implications for Research…………102 2.4. Implications for Business Strategy and Future Research…………103 3. Analysis of Industry Positioning by CVI Values and Mapping Graphs………104 3.1 Introduction……………104 3.2. Position Mapping Graphs and Cross……………106 3.3. E-Commerce Industry…………106 3.4. Tech & Software Industry………………107 3.5. Media & Streaming Industry…………109 3.6. Retail Industry ………………110 3.7. Conclusion of Positioning Analysis……………111 4. Consistency between Mapping Interpretations and Real-World Evaluations…………112 4.1. Summary of Group A-B-C Analysis……112 4.2. Introduction: 5 Perspectives for Evaluating Target Companies………112 4.3. Matching with External Evaluations…………113 5. Comprehensive Approach for CVI Model…………116 VI. Summary of Part 1 Achievements and Transition to Business Applications…………117 VII. Methodology_Part 2………………119 1. Dynamic Interactions and Cyclical Model…………119 1.1. Introduction: Purpose and Background……………119 1.2. Theoretical Concept: Dynamic Interactions and Cycling (Loop)……………120 1.3. Three Frameworks of Dynamic Interactions and Cycling (Loop)…………121 2. Common Cyclical Model…………………122 2.1. Explanation of Common Loop Dynamics………………122 2.2. Dynamics of Loop 1 in Common Cyclical Model……………125 2.3. Dynamics of Loop 2 in Common Cyclical Model…………129 3. Core Cyclical Model………………132 3.1. Explanation of Core Loop Dynamics…………132 3.2. Dynamic Relationships with Financial, Relational, and Innovation…………134 3.3. Role of Additional Concepts……………135 3.4. Business Perspective of Core Cyclical Model…………135 3.5. Dynamics of Loop1 in Core Cyclical Model…………………136 3.6. Dynamics of Loop 2 in Core Cyclical Model……………140 3.7. Summary: Common Vs. Core Cyclical Model…………143 4. Generalized Cyclical Model……………143 4.1. Introduction………………143 4.2. Temporal Dynamics……………144 4.3. Structural and Theoretical Validation………144 4.4. Practical Applications and Future Research Directions…………144 4.5. Derivation of the Generalized Cyclical Model……………145 4.6. Generalized Model……………145 4.7. Adopting System Dynamics for a Generalized Cyclical Model……………147 4.8. System Dynamics in the Generalized Cyclical Model………148 4.9. Summary……………149 5. CDSTM: CVI-based Dynamic Sequential Triggering Model……150 5.1. Introduction…………………………150 5.2. Key Concept of CDSTM……………150 5.3. CDSTM Framework…………152 5.4. Overview…………153 5.5. Significance for Enterprise Applications……154 5.6. Case Study Selection (Amazon) ……………155 5.7. Interpretation of the Trigger Mechanism…………157 5.8. Implications: Time-Term of CRFMs for Financial Performance (Amazon)…………158 5.9. The Dynamic Mechanism…………160 5.10. Implications…………161 6. CVI and CDSTM Integration…………162 6.1. Introduction…………162 6.2. Theoretical Foundation…………162 6.3. Define CVI as T1 Short-Term Performance…………163 6.4. Equation Models (T1, T2, T3) …………164 6.5. Industry and Company-Specific Differentiation…………167 6.6. Summary…………168 6.7. The Framework of CVI and CDSTM Integration…………170 6.8. Implication for Business Operations…………171 VIII. Results_Part 2 : Business Application…………173 1. Introduction…………173 2. Business Application Methodology…………173 3. Implication…………173 4. Corporate Operation Frameworks…………174 4.1. Introduction…………174 4.2. Data Processing Flow…………174 4.3. Suggestion Application Tools for Practical Implementation…………176 4.4. Significance of Implementing Prediction System…………176 4.5. Prediction Model Design…………177 4.6. Model Set-Up…………179 4.7. Loop Simulation (Using Vensim Simulation Concept)………180 4.8. Matching Loops by Time Sequence and Financial Performances…………182 4.9. The industry-specific cyclical loops…………183 4.10. Implication…………186 5. Corporate Strategy and Execution…………187 5.1. Leveraging CVI and CDSTM for Strategic Decision-Making…………187 5.2. Strategic Alignment with CVI Factors…………187 5.3. Phased Strategic Implementation Using CDSTM…………187 5.4. Decision Support through Data Insights…………189 5.5. Executive Engagement and Cross-Functional Collaboration…………189 5.6. Implications for Corporate Strategy…………189 5.7. Formulation of Comprehensive Execution Plan…………191 5.8. Monitoring, Reporting, and Regular Executive Review…192 5.9. Submission of Strategic Reports (Dashboard) …………196 6. Corporate Competitiveness…………197 6.1. Introduction…………197 6.2. Case Study: Amazon (E-Commerce) …………198 6.3 Analysis Result for Amazon Teams and Implication…………201 6.4. Preparations for Execution…………203 6.5. Computation Result utilizing the Equations…………207 6.6. Adjustment of Final Ratios: Interdepartmental Consultation…………208 6.7. Suggestion to Enterprise Application…………209 6.8. Integration of Strategies…………211 6.9. Corporation Culture…………211 7. Marketing Strategy: CVI-Market Sync…………212 7.1. Overview of the CVI-Market Sync. Framework…………212 7.2. Phased Marketing Strategies…………212 7.3. Data-Guided Optimization and Personalization……213 7.4. Monitoring and Continuous Improvement………214 7.5. Contribution to Marketing Strategy……………214 7.6. Framework…………214 8. Proposal for CVI as a Non-Financial Metric in Annual Reports…………217 8.1. Rationale for Inclusion……………217 8.2. Addressing Practical Constraints…………219 8.3. Proposal for the Internal Adoption and Operation of CVI in Enterprises…………221 8.4. Specific Reference…………223 8.5. Conclusion…………………224 9. Practical Application: Strategy for SMEs in Taiwan……226 9.1. Targeting Taiwan’s Small but Resilient SMEs: The Machinery Sector…………226 9.2. Challenges Faced by Taiwanese Machinery SMEs…………226 9.3. Analysis and Proof of Challenges……………227 9.4. Proposed CVI Model Application for Taiwanese SMEs……229 9.5. Departmental Mapping and Impact Quantification……230 9.6. CDSTM-Guided Phased Strategies…………231 9.7. Cross-Functional Initiatives for Customer-Centric Culture………232 9.8. Competitive Positioning and Monitoring……233 9.9. Solution and Consulting Approach……233 9.10. Conclusion………235 IX. Future Research…………237 Reference………………238 Appendix……………242 Appendix A: Glossary of Abbreviations (Alphabetically Ordered)…………242 Appendix B: Definitions of Customer-Related Financial Metrics (CRFMs)………246 Appendix C: Supplementary Data for CRFMs Analysis…………248 Appendix D: Computation Result of 8 Key-Factor Equations………………253 Appendix E: ROE Values used for CVI Computation………254 Appendix F: Weight Computation Result for Model 1………255 Appendix G: Computation Result of CLV and its Components…256 Appendix H: Weight Computation Result for Model 2…………257 Appendix I: Pearson Correlation Analysis Results for Model 1………258 Appendix J: Statistical Analysis Result for 8 Key-Factors of Model 1…………259 Appendix K: Pearson Correlation Analysis Results for Model 2…………261 Appendix L: Statistical Analysis Result for 8 Key-Factors of Model 2………262 Appendix M: Interpretation of A-B-C Position Mapping Graph…………264 Appendix N: Position Mapping Graph for E-Commerce Industry………265 Appendix O: Position Mapping Graph for Tech & Software Industry………266 Appendix P: Position Mapping Graph for Media & Streaming Industry…………267 Appendix Q: Position Mapping Graph for Retail Industry………268 Appendix R: Quantitative Evaluation from Position Mapping (Model 1)…………269 Appendix S: Quantitative Evaluation from Position Mapping (Model 2)…………270 Appendix T: Validation of Mapping Graph Interpretations by 5-Perspectives…………271 Appendix U: Theoretical and Methodological Foundations of the CVI Model……273 Appendix V: Detailed Overview of Application and Practical Data Processing Workflow…………274 Appendix W: Dashboard suggestion for SMEs: Periodical Executive Review (1)…………275 Appendix X: Dashboard suggestion for SMEs: Periodical Executive Review (2)…………276 Appendix Y: Amazon Case (Customer Related Teams)………277 Appendix Z: Consulting Report (Sample)………………278 Appendix AA: Notes on Data Collection Constraints…………279 zh_TW dc.format.extent 12583431 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112933039 en_US dc.subject (關鍵詞) 客戶價值指數 zh_TW dc.subject (關鍵詞) 客戶相關財務指標 zh_TW dc.subject (關鍵詞) 財務績效 zh_TW dc.subject (關鍵詞) 客戶策略 zh_TW dc.subject (關鍵詞) 動態互動 zh_TW dc.subject (關鍵詞) 分析引導決策 zh_TW dc.subject (關鍵詞) CVI-市場同步 zh_TW dc.subject (關鍵詞) CDSTM zh_TW dc.subject (關鍵詞) 客戶留存 zh_TW dc.subject (關鍵詞) 策略協同 zh_TW dc.subject (關鍵詞) Customer Value Index en_US dc.subject (關鍵詞) Customer-Related Financial Metrics en_US dc.subject (關鍵詞) Financial Performance en_US dc.subject (關鍵詞) Customer Strategy en_US dc.subject (關鍵詞) Dynamic Interaction en_US dc.subject (關鍵詞) Analytics-Guided Decision Making en_US dc.subject (關鍵詞) CVI-Market Sync. en_US dc.subject (關鍵詞) CDSTM en_US dc.subject (關鍵詞) Customer Retention en_US dc.subject (關鍵詞) Strategic Alignment en_US dc.title (題名) 將客戶價值與財務績效連結: 客戶價值指數與商業應用 zh_TW dc.title (題名) Connecting Customer Value to Financial Performance: The Customer Value Index and Business Applications en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. 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