A REAL LIFE WEB BASED MARKETING OPTIMIZATION FRAMEWORK with EXTERNAL DATA

Yıl 2018, Cilt: 2 Sayı: 1, 45 - 51, 26.06.2018

Şadi Evren Şeker

https://doi.org/10.30801/acin.356344

Öz

Big data and data science
studies in recent years are booming exponentially, parallel to the data
collected and increased processing speeds. As an inevitable consequence, most
of the web-based companies are migrating their business models to novel
technologies based on the big data and data science research. This paper is
based on a real life experience based on one of the web stores with highest
volume sales in Turkey. The project was building a data science model on big
data technologies to make estimations based on the external data, such as
weather conditions, customer demography, news at newspapers, current product
alternatives, financial facts (like currency exchange rate or stock market
values) and most importantly the sentimental analysis and opinion mining on
social network, blogs and news.  In the
paper, details of problems and possible solution alternatives and methodology
for problem solving and solutions and outcomes of the study are explained in
the given order.

Anahtar Kelimeler

Marketing, Opinion Mining, Targeted Marketing, Big Data, Data Science, Customer Behavior

Kaynakça

• [1] Sadi Evren Seker, "Real Life Machine Learning Case on Mobile Advertisement: A Set of Real-Life Machine Learning Problems and Solutions for Mobile Advertisement," in Computational Science and Computational Intelligence (CSCI), 2016 International Conference on, 2016.
• [2] Mehmet Lutfi Arslan, Sadi Evren Seker, and Cevdet Kizil, "Innovation driven emerging technology from two contrary perspectives: A case study of Internet," Emerging Markets Journal, vol. 3, no. 3, p. 87, 2014.
• [3] Sadi Evren Seker, Weka ile Veri Madenciliği.: Draft2Digital, 2015.
• [4] Sadi Evren Seker, Cihan Mert, Nuri Ozalp, and Ugur Ayan, "Time series analysis on stock market for text mining correlation of economy news," Int. J. Soc. Sci. Humanity Stud, vol. 6, no. 1, pp. 66-91, 2013.
• [5] Sadi Evren Seker, Yavuz Unal, Erdinc H Kocer, and Zeki Erdem, "Ensembled correlation between liver analysis outputs," International Journal of Biology and Biomedical Engineering, vol. 8, pp. 1-5, 2014.
• [6] Abhishek Gupta and Dejan Milojicic, "Evaluation of HPC Applications on Cloud," in Open Cirrus Summit (OCS) 2011, vol. 6, 2011, pp. 22-26.
• [7] Sadi Evren Seker and Cihan Mert, "A Novel Feature Hashing for Text Mining," Journal of Technical Science and Technologies, vol. 2, no. 1, pp. 37-40, 2013.
• [8] Marc-André Mittermayer, "Forecasting intraday stock price trends with text mining techniques," in HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04), vol. 3, 2004, pp. 64-73.
• [9] Robert P. Schumaker and Hsinchun Chen, "Textual analysis of stock market prediction using breaking financial news:The AZFin Text system," ACM Transactions on Information Systems (TOIS), vol. 27, no. 2, pp. 1-19, 2009.
• [10] Saman Halgamuge, Y Zhai, and Arthur Hsu, "Combining News and Technical Indicators in Daily Stock Price Trends Prediction," in Advances in Neural Networks - ISNN 2007 (Lecture Notes in Computer Science), vol. 4493, 2007, pp. 1087-1096.
• [11] Gabriel P. C Fung, Jeffrey X Yu, and Wai Lam, "News sensitive stock trend prediction," Lecture Notes in Computer Science, vol. 233, pp. 481– 493, 2002.
• [12] Breiman L, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
• [13] Breiman L, "Stacked regressions," Machine Learning, vol. 24, no. 1, pp. 49-84, 1996.
• [14] Breiman L, "Bagging predictors," Machine Learning, vol. 24, pp. 123-140, 1996.
• [15] Ho TK, "Random Decision Forests," Proceedings of the 3rd International Conference on Document Analysis and Recognitio, pp. 278-282, 1995.
• [16] Amit Y and Geman D, "Shape quantization and recognition with randomized trees," Neural Computing, vol. 9, no. 7, pp. 1545-1588 , 1997.
• [17] Watanachaturaporn P and Varshney PK, Arora MK Xu M, "Decision tree regression for soft classification of remote sensing data:," Remote Sensing of Environment , vol. 9, no. 3, pp. 322-336 , 2005.
• [18] Sadi Evren Seker and Atik Kulakli, "Macroeconomic ICT Facts and Mobile Telecom Operators via Social Networks and Web Pages," Journal of Business Economics and Management, vol. 4, no. 2, pp. 99 - 104, 2016.