Ensuring the spread of referral marketing campaigns: a quantitative treatment.

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 and hierarchical clustering based analysis of the responses of a recent survey focused 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 have been carried out to explore the conditions of success for the campaign in terms of realistic parameters both for homogeneous and heterogeneous population structure. Finally, experiments have been performed for generation of a recommendation network to understand the diffusion dynamics in realistic scenario. A complete mathematical treatment with analysis over real social networks helped 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. Because of its systematic generalized formulation, the prescribed quantitative framework may be useful in all areas of social dynamics, beyond the field of marketing.