| Abstract: |
In marketing world, social media is playing a crucial role nowadays. One of
the most recent strategies that exploit social contacts for the purpose of
marketing, is referral marketing, where a person shares information related to
a particular product among his/her social contacts. When this spreading of
marketing information goes viral, the diffusion process looks like an epidemic
spread. In this work, we perform a systematic study with a goal to device a
methodology for using the huge amount of survey data available to understand
customer behaviour from a more mathematical and quantitative perspective. We
perform an unsupervised natural language processing based analysis of the
responses of a recent survey focusing on referral marketing to correlate the
customers’ psychology with transitional dynamics, and investigate some major
determinants that regulate the diffusion of a campaign. In addition to natural
language processing for topic modeling, detailed differential equation based
analysis and graph theoretical treatment, experiments have been performed for
generation of a recommendation network to understand the diffusion dynamics in
homogeneous as well as heterogeneous population. A complete mathematical
treatment with analysis over real social networks can help us to determine key
customer motivations and their impacts on a marketing strategy, which are
important to ensure an effective spread of a designed marketing campaign.
Pointing out possibilities of extending these studies to game theoretic
modeling, we prescribe a new quantitative framework that can find its
application to all areas of social dynamics, beyond the field of marketing. |