Adhatus Solichah Ahmadiyah, Faridatul Aidah, Navinda Meutia, Denise Rahmadina, Daniel Lumbantobing, Ratih Anggraini


Remarketing is a powerful tool for marketers to offer products over and over to existing customers or potential customers. By using remarketing, the marketers target to further down their sales funnel. As in traditional marketing, most online marketers find it challenging to deliver the best way of advertising their products according to what customers need or like. This research aims to achieve the right promotional media alternatives based on customer preferences. A clustering method was used to perform behavior segmentation on sales data. Then, customer reviews on the purchased products collected from online platforms were analyzed to obtain customer preferences. Finally, customer preference was mapped to some suitable promotion media. The experiment result showed that pipelining sales data and product reviews could obtain definite and distinct promotional media based on customer preference. Overall, this research may help online marketers bundle specific remarketing content into promotional media that matches to customer favorites.

Full Text:



V. B. Raut and D. D. Londhe, "Opinion Mining and Summarization of Hotel Reviews," in Proc. International Conference on Computational Intelligence and Communication Networks, pp. 556-559, 2014.

C. Rangu, S. Chatterjee, and S. R. Valluru, "Text Mining Approach for Product Quality Enhancement: (Improving Product Quality through Machine Learning)," in Proc. IEEE International Advance Computing Conference, pp. 456-460, 2017.

N. Mangaonkar and S. Sirsat, "Customer product experience analysis using text mining: A neuro linguistic programming approach," in Proc. International Conference on Computing Methodologies and Communication, pp. 216-219, 2017.

A. R. Hanni, M. M. Patil, and P. M. Patil, "Summarization of customer reviews for a product on a website using natural language processing," in Proc. International Conference on Advances in Computing, Communications and Informatics, pp. 2280-2285, 2016.

M. A. Camilleri, "Market Segmentation, Targeting and Positioning. In Travel Marketing, Tourism Economics and the Airline Product", Springer, Cham, Switzerland, chapter 4, pp. 69-83, 2018.

S. A. Khedkar and S. K. Shinde, "Customer Review Analytics for Business Intelligence," in Proc. IEEE International Conference on Computational Intelligence and Computing Research, 2018.

Z. Singla, S. Randhawa, and S. Jain, "Sentiment analysis of customer product reviews using machine learning," in Proc. International Conference on Intelligent Computing and Control, 2017.

J. Jin and P. Ji, "Mining online product reviews to identify consumers' fine-grained concerns," in Proc. International Symposium on Operations Research and its Applications in Engineering, Technology and Management, 2015.

P. V. Rajeev and V. S. Rekha, "Recommending products to customers using opinion mining of online product reviews and features," in Proc. International Conference on Circuits, Power and Computing Technologies, 2015.

F. V. Ordenes, B. Theodoulidis, J. Burton, T. Gruber, and M. Zaki, "Analyzing Customer Experience Feedback Using Text Mining," Journal of Service Research, vol. 17, pp. 278 – 295, 2014.

A. Craft, "Neuro-linguistic Programming and learning theory?," The Curriculum Journal, Routledge, vol. 12, no. 1, pp. 125-136, 2001.

F. Pedregosa et al., "Scikit-learn: Machine Learning in Python", J. Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

M.H. Amirhosseini and H. Kazemian, "Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing," Cognitive Processing, 2019.



  • There are currently no refbacks.