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