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    题名: 用消費者行為改進銷售預測
    Improved sales forecasting with consumer behavior
    作者: 馬克斯
    zur Muehlen, Maximilian
    贡献者: 林左裕
    Lin, Calvin
    馬克斯
    zur Muehlen, Maximilian
    关键词: ADL
    銷售預測
    Google趨勢
    消費者行為
    ADL
    Sales forecasting
    Google trends
    Consumer behavior
    日期: 2017
    上传时间: 2017-07-31 11:36:20 (UTC+8)
    摘要: 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.
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    Fantazzini, D., & Toktamysova, Z. (2015). Forecasting German Car Sales Using Google Data and Multivariate Models. Internal Journal of Production Economics, Vol. 170, Part A, 97-135.
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    Schmidt, T., & Simeon, V. (2009). Forecasting Private Consumption: Survey-based Indicators vs. Google Trends. Journal of Forecasting, Vol. 30, No. 6, Ruhr economic papers, No. 155, 565-578.
    Shende, V. (2014). Analysis of Research in Consumer Behavior of Automobile Passenger Car Customer. International Journal of Scientific and Research Publications, Vol. 4, No. 2, 1-8.
    Swanson, D. A., Jeff, T., & T.M., B. (2011). MAPE-R: A rescaled measure of accuracy for cross-sectional forecasts. Springer.
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    Wikipedia. (2017). Augmented Dickey Fuller Test. Retrieved April 10, 2017, from Wikipedia: https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test
    zur Muehlen, M. (2016). Automotive Industry Thailand.
    描述: 碩士
    國立政治大學
    應用經濟與社會發展英語碩士學位學程(IMES)
    104266016
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0104266016
    数据类型: thesis
    显示于类别:[應用經濟與社會發展英語碩士學位學程 (IMES)] 學位論文

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