To Motivate Buyers Habit based on WhatsApp Data using Data mining Techniques

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-41 Number-1
Year of Publication : 2016
Authors : D.Sakthivel, M.Premkumar
  10.14445/22312803/IJCTT-V41P105

MLA

D.Sakthivel, M.Premkumar "To Motivate Buyers Habit based on WhatsApp Data using Data mining Techniques". International Journal of Computer Trends and Technology (IJCTT) V41(1):26-28, November 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The increased popularity of social networking sites and mobile apps in general has led to uptake in marketing research [1]. In this paper using data mining techniques to motivate the buyers to increase their buying habit based on WhatsApp data [1]. The buyer’s habit is a measured of how products and services supplied by a company meet User expectation. User satisfaction is critical if a company is to register high sales profits. The study of on this project helped me to apply data mining technique to improve and motivate buyers in current market scenarios of WhatsApp and also the variety of services that it offers to the users.

References
[1] Suraj singh, Arun Prasanth and A.K.Subramani, (2015), User satisfaction towards whatsapp. International Journal of Multidisciplinary Management Studies, Vol.5 (6).
[2] Dr. Avani maniar*; MS. Aanal modi, (2014), Educating Whatsapp generation through "Whatsapp". International Journal of Multidisciplinary Research, Vol.4 (8).
[3] Michael Karikari Appiah, (2016), Influence of Whatsapp on study habit of university students in Ghana, International Journal of Research in Economics and Social Sciences, Volume 6, Issue 3.
[4] Avi Rosenfeld, Sigal Sina, David Sarn, Or Avidov, Sarit Kraus, (2013), WhatsApp Usage Patterns and Prediction Models Institute for Advanced Computer Studies.
[5] Arun K Pujari, (2001), Data Mining Techniques, University Press, INDIA.

Keywords
Buyers Habit; Data set; Association rule; Decision tree; Cluster algorithms [4]