Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Data Mining for Customers’ Positive Reaction to Advertising in Social Media


DOI: http://dx.doi.org/10.15439/2017F356

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 945948 ()

Full text

Abstract. The paper aims at 1) finding the most important factors influencing positive customer reactions and purchasing merchandises after seeing online social media advertising and 2) identifying characteristics of customer clusters having positive reaction, as well as of purchasing customer clusters, after seeing online social media advertising. Data from 370 respondents are collected by questionnaires using convenience sampling method. Attribute selection and clustering techniques are employed in data analysis to find important factors and identify customer clusters, respectively. It is found that there is a strong correlation between the reason for clicking advertisement on social media and the satisfaction with merchandise, and between purchasing merchandise online and saving information for further consideration. The findings also indicate the characteristics of ``Product conscious'' and ``Price Conscious'' clusters for customer's reaction and purchasing after seeing online social media advertising.


  1. M. J. A. Berry and G. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 3rd ed., John Wiley and Sons Ltd., Publication, UK, 2011.
  2. D. J. Hand, H. Mannila and P. Smyth, Principles of Data Mining, MIT Press, Cambridge, MA, 2001.
  3. J. Han and M. Kamber, Data Mining Concepts and Techniques Second Edition, Morgan Kaufmann Publishers, United States of America, 2006.
  4. B. Leventhal, An introduction to data mining and other techniques for advanced analytics, Journal of Direct, Data and Digital Marketing Practice, 12(2), 2010, pp.137–153.
  5. M. Negnevitsky, Artificial Intelligence, A Guide To Intelligent Systems 3rd ed., Pearson Education Limited, 2005.
  6. R. I. Magos and C. A. Acatrinei, Designing Email Marketing Campaigns - A Data Mining Approach Based on Consumer Preferences, A nales Universitatis Apulensis Series Oeconomica, 17(1), 2015, 15-30.
  7. M. Singh and K. Peszynski, “Organisational Value of Social Technologies: An Australian Study, The Electronic Journal Information Systems Evaluation, 17(1), 2014, pp.088-099, available online at www.ejise.com.
  8. C. Campbell, C. Ferraro, and S. Sands, “Segmenting consumer reactions to social network marketing”, European Journal of Marketing, 48(3/4), 2014, pp.432-452.