Ensuring the Spread of Referral Marketing Campaigns: A Quantitative Treatment

Kumar Gaurav, Sayantari Ghosh, Saumik Bhattacharya and Yatindra Nath Singh

No 6spnr, SocArXiv from Center for Open Science

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.

Date: 2019-09-12
New Economics Papers: this item is included in nep-mkt and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:6spnr

DOI: 10.31219/osf.io/6spnr

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