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研究生:馬克斯
研究生(外文):zur Muehlen, Maximilian
論文名稱:用消費者行為改進銷售預測
論文名稱(外文):Improved sales forecasting with consumer behavior
指導教授:林左裕林左裕引用關係
指導教授(外文):Lin, Calvin
口試委員:徐士勛黃仁德
學位類別:碩士
校院名稱:國立政治大學
系所名稱:應用經濟與社會發展英語碩士學位學程(IMES)
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
畢業學年度:105
語文別:英文
論文頁數:39
中文關鍵詞:ADL銷售預測Google趨勢消費者行為
外文關鍵詞:ADLSales forecastingGoogle trendsConsumer behavior
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本篇目的---對於精實企業來說資訊預測的能力扮演舉足輕重的角色,如汽車製造商須要有可靠的資訊來完成各項重要的決策以保持企業競爭力,市場以及消費者的活動提供了新型態的資料可以透過現代科技來處理分,本篇論文希望從2008年至2016年整合的Google 搜尋趨勢資料來建構預測模型。
設計/方法論/方法---基於五階段消費者購買行為,此研究檢視整個過程中合適的Google關鍵字,並利用滯後變數模型和Google搜尋趨勢來驗證銷售和各種經濟變數之間的關係,預測的銷售會更進一步檢視其正確性。
結論與發現---用來檢視預測正確性的兩種最常見的方法指出Google搜尋趨勢可以作為有效的銷售預測依據,研究發現總體經濟變數和時間序列在預測上相較於Google搜尋趨勢在短期相對有效性小。
研究貢獻---僅有少許在汽車銷量預測上的研究將Google搜尋趨勢和合適的時間滯留列入考量,本篇研究提供消費者行為和銷售資料關係的新視角。
Purpose – The role of forecasting in a lean enterprise is immense. It is crucial for car manufacturers to have reliable information about the future to make important decisions and stay competitive. Developing markets and consumers provide new types of data that demand modern approaches to be handled. This paper aims to create reliable forecasting models through integration of Google Trends data from 2008 to 2016.
Design/methodology/approach – Building on the 5-stage-model of consumer buying behavior, the study identifies suitable Google keywords for this process. Autoregressive distributed lag models are used to examine the relationship between sales and macro-economic variables as well as Google Trends. Predicted sales are used to test for accuracy.
Findings – Two most common evaluation measurements for forecasting accuracy suggest the use of Google Trends, as predictors for future sales, is outstanding. The finding concludes that macro-economic variables and seasonality are not as valuable as Google Trends in short-term, up to one year, forecasting.
Value – Only little research on car sales forecasting takes Google Trends and their appropriate time lags into account. This analysis provides new insights into the linkage of consumer behavior and sales data.
1 Introduction 1
1.1 Background 1
1.2 Statement of the Problem 2
1.3 Research Goal 3
2 Literature Review 4
2.1 Decision Model for Purchases – Five-Stage Model 4
2.2 Google Trends as a Source of Consumer Behavior 8
2.3 Empirical Findings 11
3 Methodology 13
3.1 Conceptual Model 13
3.1.1 Time Lag 13
3.1.2 Autoregressive Distributed Lag Modeling 15
3.2 Data Collection and Scope 15
3.2.1 Dependent Variable 15
3.2.2 Macro Data Variables 16
3.2.3 Google Trends Keyword Selection 17
3.2.4 Variable Correlation 18
4 Empirical Model 20
4.1 Adjusted R² and Akaike Information Criterion 20
4.1.1 Testing Residuals 24
4.2 Forecasting 25
4.2.1 Evaluation Measurements 27
5 Results 28
5.1 Data Analysis and Results of Empirical Analysis 28
5.2 Key Findings 31
6 Conclusion 33
6.1 Discussion 33
6.2 Practical Application and Limitations 34
6.2.1 Implementation in the Automotive Industry 34
6.2.2 Major Implications for Sales 34
6.3 Limitation of this Research 36
References 37
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