An Analysis of Post COVID-19 Scenario using Data Science in Digital Marketing

Abstract

The Purpose of the study is to analyze the uses of data science in digital marketing in post COVID-19 scenario. General design of the study includes survey of relevant research literature, primary data collection, structural modeling and critical analysis. The study uses Interpretive Structural Modeling (ISM) for structural modeling and Matriced' Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) for analysis. Introducing new products, personalizing customers’ online experience and improve user experience occupy top (Level I) and tracking social media commentary/interactions occupies bottom (Level IX) of ISM model. Analyzing user generated content, tracking social media commentary/interactions, analysis of online sales data, analyzing social media trends, analyzing product recommendations and reviews and analyze real-time big data are categorized as independent uses. Optimize customers’ preferences, optimize stock levels in e-commerce businesses, introducing new products, improve user experience and identify fake news & false content are categorized as dependent uses but others are categorized as linkage uses and no one is categorized in autonomous. It is an original study because it uses real time market survey data the findings of which are useful for folks of its stakeholders. It is particularly useful for marketers. It has serious implications for businesses since nowadays there is influx of data generation that has become a type of a noise for businesses. Use of data science not only converts this data noise into useful information but also an opportunity. This study provides lot of information about uses of data science particularly for marketing.