Provide a Strategic Model based on Machine Learning Approach to Automatically Opinion Assessment and Explore Product Information in Digital Marketing

Subject Areas : Strategic Management Researches

Alireza AshouriRoudposhti ¹ , hormoz mehrani ² , Karim Hamdi ³

1 - Department of Business Management, Islamic Azad University- Science and Research Branch, Tehran, Iran
2 - Department of Management, Ghazali Higher Education Institute, Qazvin, Iran
3 - Department of Business Management, Islamic Azad University- Science and Research Branch, Tehran, Iran

Received: 2020-06-06 Accepted : 2020-08-28 Published : 2020-08-22

Keywords: Machine Learning, Artificial Intelligence, Digital Marketing, Marketing capabilities, Opinion Assessment,

Abstract :

The present study has tried to provide an automated strategic model for classifying and exploring the opinions presented about a particular product, brand or service by using machine learning and survey techniques. Applying such a strategic model can be very effective in identifying the characteristics of brands and factor clustering between them and provide very valuable information in this regard. The results of this evaluation can be used in the development of marketing management strategies and quantitative or qualitative improvement of this factor. The model based on machine learning and deep neural network identifies related opinions, measures different characteristics at different levels of evaluation, and automatically categorizes opinions depending on the quality of the presentation. The output of this model is efficiently imported by using marketing capabilities to improve the sales of defined goods / brands / services. The data set used in this study is related to the collection of comments of Persian language users of the online sales site of Digikala and Holokish, which was uploaded as an educational-experimental (70% educational data and 30% experimental data) in three alternate models in order to identify and the classification of the various properties of the goods and services provided in the dataset has been used. The proposed model uses error functions to calculate the amount of computational error to evaluate the capability to be able to provide the degree of deviation from the correct values for its predicted information. For this purpose, the mean square error and the square root mean square error have been used. The results show the high accuracy of the model of evaluations and prediction of different conditions.

References:

Al-Hajjar D & Syed A.Z. (2015). Applying sentiment and emotion analysis on brand tweets for digital marketing. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), doi: 10.1109/AEECT.2015.7360592.

Berthon, R.P., Pitt, L.F., Plangger, K & Shapiro, D. (2012). Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy. Business Horizons, Vol. 55. No. 3. 261- 271.

Bhor H.N., Koul T., Malviya R & Mundra K. (2018). Digital media marketing using trend analysis on social media. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC), doi: 10.1109/ICISC.2018.8399038.

Chiu H.C., Hesieh Y., Li Y & Lee M. (2005). Relationship marketing and consumer switching behavior. Journal of Business Research, 58(12): 1681- 1689.

Chollet F (2017). Deep Learning with Python. Manning Publications, 384 p.

Dou, W., Lim, K. H., Su, C., Zhou, N & Cui, N. (2010). Brand positioning strategy using search engine marketing. MIS Quarterly, 261-279.

Garant A (2017). Social media competitive analysis and text mining: a case study in digital marketing in the hospitality industry. MSc. Thesis, Mikkelin kampus, Aalto University.

Hassoun M. (2003). Fundamentals of Artificial Neural Networks. A Bradford Book press, 511.

Lamberton C & Stephen A.T. (2016). A Thematic Exploration of Digital, Social Media, and Mobile Marketing Research's Evolution from 2000 to 2015 and an Agenda for Future Research. Journal of Marketing, 80(6): 146-172.

Liu, B (2012). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, 381 p.

Malcolm, H (2015). Cyber Monday clocks record sales as mobile sales surge, USA Today, Dec 2. Accessed http://www.usatoday.com/story/money/2015/12/01/cyber-mondaysalesresults/7660 2534/.

Pathak, X & Pathak-Shelat, M (2017). Sentiment analysis of virtual brand communities for effective tribal marketing. Journal of Research in Interactive Marketing, 11(1): 16-38.

Phillips E (2015). Retailers scale up online sales distribution networks. The Wall Street Journal, November 17- 2015. Accessed http://www.wsj.com/articles/retailers-scale-up-online-salesdistribution- networks-1447792869.

Saura J.R., Palos-Sanchez P.R & Correia M.B (2019). Digital Marketing Strategies Based on the E-Business Model: Literature Review and Future Directions. In Organizational Transformation and Managing Innovation in the Fourth Industrial Revolution; IGI Global: Hershey, PA, USA, pp. 86-103

Sharda R., Delen D & Turban E. (2014). Business Intelligence and Analytics: Systems for Decision Support. Pearson, 656 p.

Toubia O & Stephen A.T (2013). Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter?. Marketing Science, 32(3): 368-392.

Wedel M & Kannan P.K. (2016). Marketing Analytics for Data-Rich Environments, Working Paper, Robert H. Smith School of Business, University of Maryland, College Park, MD 20742.

Witten I.H., Frank E., Hann M.A & Pal C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques.Morgan Kaufmann Pub, 654 p.

Yadav M.S & Pavlou P.A. (2014). Marketing in Computer-Mediated Environments: Research Synthesis and New Directions. Journal of Marketing, 78(1): 20-40.