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題名 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
貢獻者 林左裕
Lin, Calvin
馬克斯
zur Muehlen, Maximilian
關鍵詞 ADL
銷售預測
Google趨勢
消費者行為
ADL
Sales forecasting
Google trends
Consumer behavior
日期 2017
上傳時間 31-Jul-2017 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。\n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。\n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。\n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.\nDesign/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.\nFindings – 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.\nValue – 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.
參考文獻 Carrière-Swallow, Y., & Labbé, F. (2010). Nowcasting with Google Trends in an Emerging Market. Central Bank of Chile.\nChoi, H., & Varian, H. (2009). Predicting the Present with Google Trends. Google Inc.\nChoi, H., & Varian, H. (2012). Predicting the Present with Google Trends. Economic Record, Vol. 88, No. 1, 2-9.\nCrain Communication Inc. (2013). Top Suppliers. Automotive News.\nDharmani, S., Anand, D., & Dr. Demirci, M. (2013). Shifting Gear - Capacity management in the automotive industry. Ernst & Young Global Limited.\nFantazzini, 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.\nGoel, S. H. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, Vol. 107, No. 41, 17486 – 17490.\nGredenhof, M., & Karlsson, S. (1997). Lag-length Selection in VAR-models Using Equal and Unequal lag-length procedures. 177.\nGreen, K. C., & Armstrong, J. S. (2015). Simple vs. complex forecasting: The evidence. Journal of Business Research, Vol. 68, 1678–1685.\nHill, C. R., Griffiths, W. E., & Lim, G. C. (2010). Principles of Econometrics. John Wiley & Sons, Incorporated.\nHofstede, G. (n.d.). Cultural Dimensions. (ITIM International) Retrieved March 12, 2017, from https://geert-hofstede.com/\nHülsmann, M., Borscheid, D., Friedrich, C. M., & Reith, D. (2012). General Sales Forecast Models for Automobile Markets and their Analysis. Vol. 5, No. 2 (2012) 65-86.\nKarlsson, E., & Annie, R. (2014). Development of a Sales and Operation Planning Process. Göteburg, Sweden.\nKinski, A. (2016). Google Trends as Complementary Tool for New Car Sales Forecasting: A Cross-Country Comparison along the Customer Journey. University of Twente.\nKotler, P., & Keller, K. L. (2016). A Framework for Marketing Management. Pearson Education Limited.\nMarklines. (n.d.). Automotive Industry Portal Marklines - Vehicle Sales. (MarkLines Co., Ltd.) Retrieved February 17, 2017, from Automotive Industry Portal Marklines: https://www.marklines.com/en/vehicle_sales/index\nPan. B., W. D. (2012). Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology, Vol. 3, No. 3, 196-210.\nReturn On Now. (2015). Search Engine Market Share. Retrieved May 27, 2017, from Returnonnow: http://returnonnow.com/internet-marketing-resources/2015-search-engine-market-share-by-country/\nRieg, R. (2010). Do Forecasts Improve over Time? A case study of the accuracy of sales forecasting at a German car manufacturer. IJAIM, Vol.18, No.3, 222-236.\nRogers, S. (2016, July 2). Retrieved May 22, 2017, from https://medium.com/google-news-lab/what-is-google-trends-data-and-what-does-it-mean-b48f07342ee8\nSchmidt, 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.\nShende, 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.\nSwanson, D. A., Jeff, T., & T.M., B. (2011). MAPE-R: A rescaled measure of accuracy for cross-sectional forecasts. Springer.\nThe World Bank. (n.d.). The World Bank - Global Economic Monitor. (The World Bank Group) Retrieved February 18, 2017, from The World Bank IBRD IDA: http://data.worldbank.org/data-catalog/global-economic-monitor\nThe World Bank. (n.d.). World DataBank. (The World Bank Group) Retrieved February 23, 2017, from The World Bank IBRD IDA: http://databank.worldbank.org/data/home.aspx\nWharton. (2011, September 15). Wharton Magazine. Retrieved April 25, 2017, from http://whartonmagazine.com/blogs/importance-of-simple-forecasting-methods/\nWikipedia. (2017). Augmented Dickey Fuller Test. Retrieved April 10, 2017, from Wikipedia: https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test\nzur 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
資料類型 thesis
dc.contributor.advisor 林左裕zh_TW
dc.contributor.advisor Lin, Calvinen_US
dc.contributor.author (Authors) 馬克斯zh_TW
dc.contributor.author (Authors) zur Muehlen, Maximilianen_US
dc.creator (作者) 馬克斯zh_TW
dc.creator (作者) zur Muehlen, Maximilianen_US
dc.date (日期) 2017en_US
dc.date.accessioned 31-Jul-2017 11:36:20 (UTC+8)-
dc.date.available 31-Jul-2017 11:36:20 (UTC+8)-
dc.date.issued (上傳時間) 31-Jul-2017 11:36:20 (UTC+8)-
dc.identifier (Other Identifiers) G0104266016en_US
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=128447-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用經濟與社會發展英語碩士學位學程(IMES)zh_TW
dc.description (描述) 104266016zh_TW
dc.description.abstract (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。\n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。\n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。\n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。zh_TW
dc.description.abstract (摘要) 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.\nDesign/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.\nFindings – 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.\nValue – 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.en_US
dc.description.tableofcontents 1 Introduction 1\n1.1 Background 1\n1.2 Statement of the Problem 2\n1.3 Research Goal 3\n2 Literature Review 4\n2.1 Decision Model for Purchases – Five-Stage Model 4\n2.2 Google Trends as a Source of Consumer Behavior 8\n2.3 Empirical Findings 11\n3 Methodology 13\n3.1 Conceptual Model 13\n3.1.1 Time Lag 13\n3.1.2 Autoregressive Distributed Lag Modeling 15\n3.2 Data Collection and Scope 15\n3.2.1 Dependent Variable 15\n3.2.2 Macro Data Variables 16\n3.2.3 Google Trends Keyword Selection 17\n3.2.4 Variable Correlation 18\n4 Empirical Model 20\n4.1 Adjusted R² and Akaike Information Criterion 20\n4.1.1 Testing Residuals 24\n4.2 Forecasting 25\n4.2.1 Evaluation Measurements 27\n5 Results 28\n5.1 Data Analysis and Results of Empirical Analysis 28\n5.2 Key Findings 31\n6 Conclusion 33\n6.1 Discussion 33\n6.2 Practical Application and Limitations 34\n6.2.1 Implementation in the Automotive Industry 34\n6.2.2 Major Implications for Sales 34\n6.3 Limitation of this Research 36\nReferences 37zh_TW
dc.format.extent 3817919 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104266016en_US
dc.subject (關鍵詞) ADLzh_TW
dc.subject (關鍵詞) 銷售預測zh_TW
dc.subject (關鍵詞) Google趨勢zh_TW
dc.subject (關鍵詞) 消費者行為zh_TW
dc.subject (關鍵詞) ADLen_US
dc.subject (關鍵詞) Sales forecastingen_US
dc.subject (關鍵詞) Google trendsen_US
dc.subject (關鍵詞) Consumer behavioren_US
dc.title (題名) 用消費者行為改進銷售預測zh_TW
dc.title (題名) Improved sales forecasting with consumer behavioren_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Carrière-Swallow, Y., & Labbé, F. (2010). Nowcasting with Google Trends in an Emerging Market. Central Bank of Chile.\nChoi, H., & Varian, H. (2009). Predicting the Present with Google Trends. Google Inc.\nChoi, H., & Varian, H. (2012). Predicting the Present with Google Trends. Economic Record, Vol. 88, No. 1, 2-9.\nCrain Communication Inc. (2013). Top Suppliers. Automotive News.\nDharmani, S., Anand, D., & Dr. Demirci, M. (2013). Shifting Gear - Capacity management in the automotive industry. Ernst & Young Global Limited.\nFantazzini, 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.\nGoel, S. H. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, Vol. 107, No. 41, 17486 – 17490.\nGredenhof, M., & Karlsson, S. (1997). Lag-length Selection in VAR-models Using Equal and Unequal lag-length procedures. 177.\nGreen, K. C., & Armstrong, J. S. (2015). Simple vs. complex forecasting: The evidence. Journal of Business Research, Vol. 68, 1678–1685.\nHill, C. R., Griffiths, W. E., & Lim, G. C. (2010). Principles of Econometrics. John Wiley & Sons, Incorporated.\nHofstede, G. (n.d.). Cultural Dimensions. (ITIM International) Retrieved March 12, 2017, from https://geert-hofstede.com/\nHülsmann, M., Borscheid, D., Friedrich, C. M., & Reith, D. (2012). General Sales Forecast Models for Automobile Markets and their Analysis. Vol. 5, No. 2 (2012) 65-86.\nKarlsson, E., & Annie, R. (2014). Development of a Sales and Operation Planning Process. Göteburg, Sweden.\nKinski, A. (2016). Google Trends as Complementary Tool for New Car Sales Forecasting: A Cross-Country Comparison along the Customer Journey. University of Twente.\nKotler, P., & Keller, K. L. (2016). A Framework for Marketing Management. Pearson Education Limited.\nMarklines. (n.d.). Automotive Industry Portal Marklines - Vehicle Sales. (MarkLines Co., Ltd.) Retrieved February 17, 2017, from Automotive Industry Portal Marklines: https://www.marklines.com/en/vehicle_sales/index\nPan. B., W. D. (2012). Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology, Vol. 3, No. 3, 196-210.\nReturn On Now. (2015). Search Engine Market Share. Retrieved May 27, 2017, from Returnonnow: http://returnonnow.com/internet-marketing-resources/2015-search-engine-market-share-by-country/\nRieg, R. (2010). Do Forecasts Improve over Time? A case study of the accuracy of sales forecasting at a German car manufacturer. IJAIM, Vol.18, No.3, 222-236.\nRogers, S. (2016, July 2). Retrieved May 22, 2017, from https://medium.com/google-news-lab/what-is-google-trends-data-and-what-does-it-mean-b48f07342ee8\nSchmidt, 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.\nShende, 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.\nSwanson, D. A., Jeff, T., & T.M., B. (2011). MAPE-R: A rescaled measure of accuracy for cross-sectional forecasts. Springer.\nThe World Bank. (n.d.). The World Bank - Global Economic Monitor. (The World Bank Group) Retrieved February 18, 2017, from The World Bank IBRD IDA: http://data.worldbank.org/data-catalog/global-economic-monitor\nThe World Bank. (n.d.). World DataBank. (The World Bank Group) Retrieved February 23, 2017, from The World Bank IBRD IDA: http://databank.worldbank.org/data/home.aspx\nWharton. (2011, September 15). Wharton Magazine. Retrieved April 25, 2017, from http://whartonmagazine.com/blogs/importance-of-simple-forecasting-methods/\nWikipedia. (2017). Augmented Dickey Fuller Test. Retrieved April 10, 2017, from Wikipedia: https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test\nzur Muehlen, M. (2016). Automotive Industry Thailand.zh_TW