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Integrating Artificial Neural Network and BG/BB Model to Predict Online Customer Repurchasing
|Issue Date: ||2019-09-05 15:43:41 (UTC+8)|
|Abstract: ||長久以來，顧客流失一直被視為最重要的預測問題之一，例如：顧客未來回購的可能性，或是進一步找出確切的回購時機等。過去曾有許多不同的領域都在探討回購的預測，而這些領域也發展出個別的預測模型，然而，有關於不同領域的模型的比較與探討仍然相當匱乏。本研究整合了行銷領域常使用的BG/BB(Beta Geometric/Beta Bernoulli)機率模型，以及電腦科學及人工智慧領域常使用的類神經網路，提出兩種模型的混合模型。本研究結果顯示，使用當季交易資料與BG/BB參數的混合模型，相較兩個個別模型，在預測下一季回購的平均精準度(average precision)，可分別提升6.5%及8.1%。此混合模型在預測精準度的改善，表示以統計為基礎的行銷模型與類神經網路具有資訊的互補性。此外，本研究發現資料分群可以找出預測較為精準的顧客群，而使用Recency相較於用K-Means進行分群，其預測表現並沒有差別，但有更低的計算成本及更高的解釋性。而在混合模型與長短期記憶模型的比較中，本研究發現混合模型有較複雜時間序列模型更好的預測效果，其建模的成本也更低。上述研究成果，在實務面，可透過較低的成本，幫助企業提升預測精準度，進而提升預測及行銷的投資報酬率，而在學術面，回購預測在行銷領域及資料探勘領域有各自的發展，而本研究是首篇進行跨領域模型探討，並探討整合兩種領域模型之混合模型的過程與其績效評估的研究。|
Customer churn has long been recognized as one of the most important predictive issues. Through customer churn prediction, companies can know the likelihood of a customer repurchasing in the future, as well as the exact timing of the repurchase. In the past, there have been many different areas exploring the repurchase predictions, and these areas have developed individual prediction models. However, the lack of discussions and comparisons of models in different areas motives the research. This study proposes a hybrid model that integrates the BG/BB (Beta Geometric/Beta Bernoulli) probability model frequently used in the field of marketing, and the neural network commonly used in computer science and artificial intelligence. The hybrid model using transaction data of current season and the BG/BB parameters has improved the prediction performance (average precision) by 6.5% and 8.1% compared to the two individual models respectively. The improvement indicates that the statistical marketing model and the neural network can complement to each other. We further perform data clustering and identify the set of customers with better predictability. Especially, we find that using Recency instead of K-Means as the clustering indicator has lower computational costs and more interpretability while the prediction performance is similar. We also compare the hybrid model and the LSTM model. The finding indicates that the hybrid model has better prediction performance than complex time series model, and the cost of modeling is even lower. The study has both practical and academic contributions. First, the proposed hybrid model can help companies improve forecasting accuracy with relatively low cost, thereby improving the return on investment of marketing. In addition, while the development of repurchase forecasting in marketing and data mining has been very vigorous, this study is the first to integrate models across areas and presents the process of building the hybrid model and further evaluate its performance.
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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0106356007|
|Data Type: ||thesis|
|Appears in Collections:||[資訊管理學系] 學位論文|
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