臺灣博碩士論文加值系統

本研究之目的為利用台灣售票公司提供之歷史演唱會售票資料，來建立演唱會銷售預測以及分析模型。本研究先將演唱會資料依據場地大座位數量分成大、中、小型演唱會，並透過建立兩種目標來達成不同的預測分析。第一個目標為利用不同區段的售票銷售模式來建立預測模型。主要以不同的演唱會售票天數進行分割成五個售票區間，再利用五個售票區間的售票狀況進行演唱會分群，最後再進行演唱會售票模式預測，預測出可能的演唱會售票模式趨勢走向。第二個目標為分析規劃演唱會中的前置因子影響。在規劃演唱會中，很多因子需要在售票前事先規劃以及決定。本研究提出四種前置因子，分別為演唱會的售票天數、票價分佈、預設公關票比例以及總座位數，並透過本研究提出的兩階段預測模型來確認前置因子規劃的重要性。根據研究結果顯示，本研究在第一個目標的模型中，大型演唱會可以達到九成以上的售票模式準確率、中型演唱會達到八成以上準確率以及小型演唱會達到七成以上之準確率結果，並可準確地預測出演唱會的銷售模式走向。第二個目標的實驗結果證實前置因子對最終銷售狀況具顯著的影響，其 R-squared 指標皆高於0.75。

This research utilized the transaction data regarding concert ticket sales provided by one ticketing company in Taiwan to predict the sales patterns. The concert data was divided into three categories based on the stadium size (Big, Medium, Small). In this research, two research objectives were defined. First, a prediction model was developed based on five different sales performance data to investigate sales patterns of concert ticket sales. The goal is to forecast the ticket sales pattern of concerts before selling tickets. Second objective is to develop a two-stage forecasting model to discover the influence of pre-determined variables which are usually determined before selling the tickets. In a concert ticket sale, four decision variables including the number of selling days, pricing distribution, the reserved seats for public relation, and the number of total seats are usually pre-determined. Based on the experimental result, the sales pattern prediction accuracy is more than 90% for big stadium concerts; more than 80% for medium stadium concerts; more than 70% for small stadium concerts. Regarding the study of the pre-determined variables, the experiment result show that the pre-determined variables have significant impact on the concert sales pattern with more than 0.75 R-squared for three size of concert stadium.

摘要 I
ABSTRACT II
致謝 III
CONTENTS IV
FIGURE LIST VI
TABLE LIST VIII
CHAPTER 1. INTRODUCTION 1
1.1. Research Background 1
1.2. Research Problem 2
1.3. Structure of this Research 3
CHAPTER 2. LITERATURE REVIEW 4
2.1. E-Ticketing Service 4
2.2. Ticket Sales Forecasting 5
2.3. Classification - Probability Estimation 6
CHAPTER 3. METHODOLOGY 8
3.1. Research Objectives 8
3.1.1. Objective 1 - Investigation of Customers Purchasing Behavior 8
3.1.2. Objective 2 - Study of Pre-Determined factors for Event Organizer 9
3.2. Research Conceptual Model 10
3.2.1. Classification Model 10
3.2.2. Regression Model 10
3.2.3. Research Structure 12
3.3. Random Forest Method 14
3.3.1. Random Forest Classification. 15
3.3.2. Random Forest Regression 17
3.4. Model Evaluation 19
3.5. Attributes Creation 20
3.5.1. Public Relations Tickets (PRT) 20
3.5.2. Selling Patterns 22
3.5.3. Expected Ticket Price (ETP) 26
3.6. Assumptions 27
CHAPTER 4. DATA COLLECTION AND PREPROCESSING 29
4.1. Data Collection 29
4.2. Data Pre-processing 30
4.2.1. Data Transformation 30
4.2.2. Additional Attributes Collection 31
4.2.3. Categorical Data Handling 34
4.2.4. Data Normalization 35
CHAPTER 5. EXPERIMENT 37
5.1. Data Processing 37
5.1.1. Data Preparation 38
5.2. Objective 1 – Investigation of Customers Purchasing Behavior 40
5.2.1. Clustering Results 40
5.2.2. Clustering Analysis 43
5.2.3. Selling Pattern Analysis 46
5.2.4. Classification Results 51
5.3. Objective 2 – Study of Planning Strategies for Event Organizers 58
CHAPTER 6. CONCLUSION 60
APPENDIX 63
A1 Clustering Result 63
A2 Selling Pattern 65
A3 Estimating Probabilities 72
A4 Accumulation Estimating Probabilities 76
A5 List of categorical variables transformed into dummy coded variables 80
REFERENCE 81

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