全通路技術是一種跨渠道的商業模式，大多數企業會透過全通路技術來改善他們的顧客體驗，這些通路類型包括線上店鋪、線下店鋪以及大眾媒體傳播等等。近年來，全通路技術通過跨平台資訊整合來塑造零售業務以及使用者行為。隨著物聯網的蓬勃發展，人工智慧也逐漸成為一項重要的技術，藉由適當的訓練模型下，人工智慧可以預測消費者偏好並根據歷史數據提供建議，以實現電子商務中的精準營銷。但是，大多數訂購平台上現有的聊天機器人缺乏人工智慧的最佳化，導致需要向客戶提出許多問題。因此，本研究的目的主要是開發一個全通路平台，此平台包含iOS、Android和Web頁面當成前端系統，使人工智慧聊天機器人將能夠快速推薦合適的產品;並且基於機器學習相關方法，增強個人化服務和精準營銷，其結果透過實驗驗證得知在刪除掉兩項問題後，顧客接受度還是能高達80%並且有效降低流程時間。最後共享廚房案例研究展示了提出方法的優點也希望能夠將此系統應用於其他創新，例如服裝選擇或個性化服務等。

Omni-channel is a cross-channel business model involving shared data that allows enterprises to enhance and facilitate customer experience. Channel platforms include brick-and-mortar stores, telephones, social media, and online shopping etc. Omni-channel shapes retail business and shopper behaviors by coordinating data across platforms. The use of artificial intelligence (AI) has played an increasingly critical role in business analysis. With the proper training, AI can predict consumer preferences and provide recommendations based on historical data to achieve precision marketing in e-commerce. However, existent chatbots on most ordering platforms lack the AI refinement, resulting in the need to ask customers many questions before generating a reliable suggestion. The way in which to incorporate AI in an omni-channel platform has remained vague. Hence, the aim of this study is to develop an AI chatbot for an omni-platform that incorporates iOS, Android, and web components. The AI chatbot will be able to recommend the proper product quickly; enhancing personalized service and precision marketing based on machine learning methods. The result shows that the customer acceptance can still be as high as 80% and reduce the process time effectively through experiments after removing the two problems in chatbot. A shared kitchen case study demonstrates the advantages of the proposed method, which is transferable to other consumer applications such as clothing selection or personalized services.

1. Ailawadi, K L. and Paul W. F (2017), “Managing Multi- and Omni-Channel Distribution: Metrics and Research Directions,” Journal of Retailing, 93, 121–36.
2. Bengio, Y. (2012). Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning (pp. 17-36).
3. Berson, A., Smith, S., & Thearling, K. (2000), Building data mining applications for CRM. New York, NY: McGraw-Hill.
4. Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189(194), 4-7.
5. Brynjolfsson, E., & Rahman, J. (2013), “Competing in the Age of Omni-channel Retailing”, MIT Sloan Management Review, 54 (4). 23–29.
6. Cheng, X., Qiu, W., & Lu, C. (2018). “Research on artificial intelligence recommendation model based on genetic algorithm.” In Tenth International Conference on Digital Image Processing (ICDIP 2018), Vol. 10806, p. 108066X). International Society for Optics and Photonics.
7. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118
8. Fayers, P., & Machin, D. (2007). The assessment, analysis and interpretation of patient-reported outcomes.
9. Fischer, A., & Igel, C. (2012). An introduction to restricted Boltzmann machines. In Iberoamerican Congress on Pattern Recognition (pp. 14-36). Springer, Berlin, Heidelberg.
10. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psychology (Vol. 52, pp. 139-183). North-Holland.
11. Ji, Z., Lu, Z., & Li, H. (2014). An information retrieval approach to short text conversation. arXiv preprint arXiv:1408.6988.
12. Kamel, J. P., & Kay, M. (2011). Opening the door to omni-channel retailing. Apparel Magazine, 53(2), 1-4.
13. Kuligowska, K. (2015). Commercial chatbot: performance evaluation, usability metrics and quality standards of embodied conversational agents. Professional Center for Business Redearch, 2, 1-16.
14. Lazaris, C. & Vrechopoulos, A., (2014), “Human-Computer vs. Consumer-Store Interaction in a Multichannel Retail Environment: Some Multidisciplinary Research Directions”, Proceeding of 16th International Conference on Human Computer Interaction, Heraklion, Crete, Greece. (Vol. 8527, pp. 339-349)
15. Liao, S. H., Chen, J. L., & Hsu, T. Y. (2009). Ontology-based data mining approach implemented for sport marketing. Expert Systems with Applications, 36(8), 11045-11056.
16. Marshall, T., Champagne-Langabeer, T., Castelli, D., & Hoelscher, D. (2017). Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs. Journal of Health information science and systems, 5(1), 13.
17. Mocanu, D. C. (2015). On the synergy of network science and artificial intelligence. In Symposium on, pages (Vol. 1278, p. 1281).
18. Nardi, B. A., & O’day, V. (1996). Intelligent agents: what we learned at the library. International Journal of Libraries and Information Studies, 46(2), 59-88.
19. Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C. (2006). Challenges and opportunities in Multichannel customer management. Journal of service research, 9(2), 95-112.
20. Newman, K. (2001). Interrogating SERVQUAL: a critical assessment of service quality measurement in a high street retail bank. International journal of bank marketing, 19(3), 126-139.
21. Nimavat, K. & Champaneria, T. (2017). Chatbots: An overview. Types, Architecture, Tools and Future Possibilities.
22. Nunnally, J. (1978). Psychometric methods.
23. Rätsch, G. (2004). A brief introduction into machine learning. Friedrich Miescher Laboratory of the Max Planck Society. https://arxiv.org/abs/1709.02840 viewed on 9/30/2018.
24. Reshmi, S., & Balakrishnan, K. (2018). Empowering Chatbots With Business Intelligence by Big Data Integration. International Journal of Advanced Research in Computer Science, 9(1), 627-631.
25. Rigby, D. (2011). The future of shopping. Harvard business review, 89(12), 65-76.
26. Rosa, M. I. (2012). Omni-channel retailing: is it Order online pick-up in store service viable in the Portuguese grocery industry?(Master’s Thesis), Universidade Católica Portuguesa, Portugal.
27. Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach: 3rd edition, Pearson Education.
28. Saghiri, S., Wilding, R., Mena, C., & Bourlakis, M. (2017). Toward a three-dimensional framework for Omni-channel. Journal of Business Research, 77, 53-67.
29. Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. In Security and Privacy (SP), 2017 IEEE Symposium on (pp. 3-18). IEEE.
30. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI (Vol. 4, p. 12).
31. Tan, J. T. C., Duan, F., & Inamura, T. (2012). Multimodal human-robot interaction with Chatterbot system: Extending AIML towards supporting embodied interactions. In Robotics and Biomimetics (ROBIO), 2012 IEEE International Conference on (pp. 1727-1732). IEEE.
32. Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-channel retailing to Omni-channel retailing: introduction to the special issue on Multi-channel retailing. Journal of retailing, 91(2), 174-181.
33. Wallace, R. S. (2009). The anatomy of ALICE. In Parsing the Turing Test (pp. 181-210). Springer, Dordrecht.
34. Wang, L., Xu, H., & Cao, Y. (2018). Research and Implementation of Precision Marketing System Based on Big Data Analysis. In IOP Conference Series: Materials Science and Engineering (Vol. 394, No. 3, p. 032115).
35. Xiao, K., & Hu, X. (2017). Study on Maritime Logistics Warehousing Center Model and Precision Marketing Strategy Optimization Based on Fuzzy Method and Neural Network Model. Polish Maritime Research, 24(s2), 30-38.
36. You, Z., Si, Y. W., Zhang, D., Zeng, X., Leung, S. C., & Li, T. (2015). A decision-making framework for precision marketing. Expert Systems with Applications, 42(7), 3357-3367.
37. Zhang, H., Zhang, L., Cheng, X., & Chen, W. (2016). A novel precision marketing model based on telecom big data analysis for luxury cars. In Communications and Information Technologies (ISCIT), 2016 16th International Symposium on (pp. 307-311). IEEE.
38. Zhengyu, Liu. (2007). Study on Precision Marketing Method. Journal of Shanghai Jiaotong University, 41(S1), 143-146.