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| Title: | 動態客戶獲取與市場競爭下的結構化銷售預測模型
A Structural Sales Forecasting Model with Dynamic Customer Acquisition and Market Competition |
| Authors: | 陳廷瑜
Chen, Ting-Yu |
| Contributors: | 周彥君
莊皓鈞
陳廷瑜
Chen, Ting-Yu |
| Keywords: | 銷售預測
存活分析
競爭動態
應用機率
sales forecasting
survival analysis
dynamic competition
applied probability |
| Date: | 2022 |
| Issue Date: | 2022-09-02 14:48:08 (UTC+8) |
| Abstract: | 一間公司的財務績效和市場評價與其未來的銷售量或用戶數息息相關，因此，多數企業會對市場的潛力進行估計，以推估未來所能取得的顧客或銷售量，此預測估計牽涉到營運、行銷、產能的投資規劃，甚為重要。本研究設計了一個機率性的模型來估計顧客的取得，考慮到銷售市場存在競爭關係，競爭對手的表現往往對企業影響甚巨，因此本研究模型將競爭對手納入模型中共同估計，展示考慮競爭的情況下顧客獲取動態的捕捉及銷售量的預測。
本文以台灣汽車市場的歷年銷售數據展示預測結果，分析結果顯示本研究模型不論是預測準確率或是趨勢走向均不遜於其他時間序列模型，甚至在多數時候表現得更好、更穩定。本文提出的模型僅需使用各品牌歷年的銷售量資料，便可以直覺的方式預測品牌未來的銷量，且其參數具相當的參考價值。除汽車產業數據外，本模型也可以使用於其他耐久財及服務（如手機、家電設備等）。
Companies` financial performance and market evaluation are closely related to their future sales and customer acquisition. Therefore, most companies evaluate their market potential by estimating the number of future customers or sales that can be obtained. Forecasting is essential as it affects a firm’s investment planning for operations, marketing, and production capacity. In this study, we design a probabilistic model to estimate a firm’s customer acquisition. Specially, we incorporate competitors into the model and estimate them jointly to show how market competition impacts customer acquisition and sales forecast dynamically.
We present the forecast result based on the sales data of Taiwan`s automobile market over the years. Compared to other time series models, our proposed model can better capture sales trends and have stable accuracy over different time periods. Furthermore, our model only depends on single input of historical sales, and its model parameters have market implications. In addition to automobile industry data, the model can also be used for other durable goods and services (such as mobile phones, home appliances, etc.). |
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Fader, P. S., & Lattin, J. M. (1993). Accounting for Heterogeneity and Nonstationarity in a Cross-Sectional Model of Consumer Purchase Behavior. Marketing Science, 12(3), 304-317.
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice. OTexts.
McCarthy, D. M., & Fader, P. S. (2018). Customer-Based Corporate Valuation for Publicly Traded Noncontractual Firms. Journal of Marketing Research, 55(5), 617-635.
Moe, W. W., & Fader, P. S. (2002). Fast-Track: Article Using Advance Purchase Orders to Forecast New Product Sales. Marketing Science, 21, 347-364.
Schweidel, D. A., Fader, P. S., & Bradlow, E. T. (2008). Understanding Service Retention within and across Cohorts using Limited Information. Journal of Marketing, 72(1), 82-94.
Vinod, H. D., & Lopez-de-Lacalle, J. (2009). Maximum Entropy Bootstrap for Time Series: The meboot R Package. Journal of Statistical Software, 29(5), 1 - 19. |
| Description: | 碩士
國立政治大學
資訊管理學系
109356005 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109356005 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201310 |
| Appears in Collections: | [Department of MIS] Theses
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| 600501.pdf | 5645Kb | Adobe PDF2 | 0 | View/Open |
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