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Topological data analysis in digital marketing
Author(s) -
Lakshminarayan Choudur,
Yin Mingzhang
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2563
Subject(s) - computer science , markov chain , session (web analytics) , the internet , data mining , point (geometry) , digital marketing , topology (electrical circuits) , world wide web , theoretical computer science , data science , information retrieval , machine learning , mathematics , geometry , combinatorics
Abstract The ubiquitous internet is a multipurpose platform for finding information, an avenue for social interaction, and a primary customer touch‐point as a marketplace to conduct e‐commerce. The digital footprints of browsers are a rich source of data to drive sales. We use clickstreams (clicks) to track the evolution of session‐level customer browsing for modeling. We apply Markov chains (MC) to calculate probabilities of page‐level transitions from which relevant topological features ( persistence diagrams ) are extracted to determine optimal points (URL pages) for marketing intervention. We use topological summaries ( silhouettes, landscapes ) to distinguish the buyers and nonbuyers to determine the likelihood of conversion of active user sessions. Separately, we model browsing patterns via Markov chain theory to predict users' propensity to buy within a session. Extensive analysis of data applied to a large commercial website demonstrates that the proposed approaches are useful predictors of user behavior and intent. Utilizing computational topology in digital marketing holds tremendous promise. We demonstrate the utility of topological data analysis combined with MC and present its merits and disadvantages.