本研究乃是鑑於研究生所處公司的經驗，深覺汽車市場已經連續三年不景氣，在在汽車銷售市場逐年萎縮，各車廠在保有台數越來越少，新車出貨逐年降低的情形下，製造端幾乎沒有獲利的情況下，導致公司裁員，協力廠倒閉，紛紛將目標轉為仍有廣大需求的售後服務市場，並盡力找尋新商機，以提升企業競爭力。但當專注在售後服務市場同時，零配件搭配新車銷售的議題往往被忽略掉，雖屬於售服配件，但與新車銷售息息相關，此次研究乃透過適當的體系樣本採集，並透過資料分析軟體，建置重要因子排列之模型，找出影響新車配件銷售的關鍵因子，並透過適當的促銷方案搭配予以對策改善。 本研究以C汽車公司為例，並利用資料探勘手法，配合Clementine軟體，建置貝氏網路及決策樹模型，經過三項模型比較後，得知以Markov建立預測模型可獲得最高之准確定，可作為後續各車廠配件開發銷售之參考。In the downturn of the automobile market and under the situation of earning a little profit, those automobile manufacturers shift their target to the after-sales markets, which still have big demand. Besides, In order to improve their company competition, they also try their best to research and development new accessory for searching new business. According to the experience, the accessory is usually be out of consideration .Even though it’s belong to after- market parts, but it’ll be closely linked to automobile sales. In this research, by appropriate sampling and the SPSS software Clementine, building the models by comparing priority of factors. This thesis will find the key point of new accessory sale. This thesis is using C company for research data. Besides, the thesis also uses data mining methodology and Clementine to build three prediction models. They are Tan, Markov and CHAID decision tree. After evaluating these three models, this thesis recommends using Markov decision to prediction model. It has better accuracy, and it can be a reference for motor company.
