Earticle

초록

영어

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

목차

Abstract
1. Introduction
2. Existing Research
2.1 Research to Predict Purchase Timing
2.2 Recurrent Neural Network
3. Proposed Deep Neural Network Models
3.1 Proposed Architecture of Neural Networks
3.2 Learning and Implementation of Neural Networks
4. Performance Evaluation
4.1 Data Set for Experiment
4.2 Performance Metric for Evaluation
4.3 Experiment Results
5. Conclusion
References

키워드

저자

• Hee Seok Song [ Department of Global IT Business in Hannam University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005