Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98057
標題: 利用邏輯斯迴歸方法建構銷售管道預測分析模型-以健身器材產業為例
Sales Pipeline Prediction and Analysis by Using Logistic Regression: A Case of Fitness Equipment Business
作者: 王汎嫈
Fan-Ying Wang
關鍵字: 大數據;預測分析;需求預測;銷售管道;客戶關係管理;銷售漏斗;銷售業績預測;big data;predictive analysis;sales pipeline;CRM;sales funnel;sales prediction
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摘要: 
大數據熱潮的興起,如何使用數據分析來幫助企業解決問題,並做為更好的決策參考,已經引起學者與企業的高度關注。預測分析是大數據應用中廣泛被討論的應用技術之一,並且多方面應用於銷售,營運,行銷,風險評估等等商用業務中。大部分的業務預測研究多著重在對終端使用者(B2C)的需求預測上,對於企業對企業(B2B)的交易預測的研究則相對少了許多。

相較於B2C的需求預測多在討論單一消費者的採購屬性與特定機型銷售數量等,B2B的交易預測則更關注企業於每一個商機提案(Leads)的可能成功率。準確的B2B交易預測對企業資源分配最佳化、降低庫存,改善金流等有相當的助益,對資本密集與交貨期高度敏感的產業來說更是不可或缺的工具。 當交易預測顯示對某一商機提案有高度的贏案率,企業即可開始展開計畫生產排程,以便縮短交期。反之,當贏案率預測機率低時,企業可以針對其銷售策略進行調整,來提高其贏案機率,甚或在適當的時機及時放棄該提案來減少損失。

本研究重點在於使用企業現有的業務管理系統資訊,開發B2B交易預測分析模型。具體的使用邏輯斯迴歸分析來估算可能的贏案率,研究結果顯示,建立於銷售管道系統中的客戶、交易金額、付款條件、保固期、交期、銷售渠道等因素對成功贏案皆有顯著影響。預測模型相對現況,有非常顯著的準確度提升。足以說明本研究方向正確並對業務管理有實際的助益。

On big data analysis trend, how to use transaction data analysis to support problem solving in every corporate has drawn high attention of scholars and cooperate. Predictive analysis is one of often mentioned and discussed technology. It has been adopt in sales, operation, marketing, and risk analysis. Most of sales prediction analysis focus more on B2C, but studies of B2B transaction analysis are less discussed.

Instead of discussion end users purchasing attribute on B2C predictive analysis, B2B transaction prediction care more on the win rate of each leads in pipeline. By more accurate transaction result prediction, cooperate would be able to better allocate limited resources, reduce stock inventory, improvement cash flow.

This study is to use logistic regression to predict transaction result of sales pipeline final progress by using extracted data from sales pipeline management module of CRM system. The factors being booked in the sales pipeline system, such as customer, estimate revenue, payment term, warranty, lead time, sales channel have shown obvious effect to the independent factor. The predictive win rate by our calculation is much higher than actual situation, thus we could conclude that our study is in good direction and the predictive result could be a helpful tool to sales managers.
URI: http://hdl.handle.net/11455/98057
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