Impact of Social Media Marketing on Business Performance: A Hybrid Performance Measurement Approach Using Data Analytics and Machine Learning

Abstract

This study systematically applies Machine Learning (ML) and Data Envelopment Analysis (DEA) to analyze Twitter messages, Twitter metrics and organizational financial metrics to gain insights into impactful messaging typology on Social Media Network (SMN). Automated Machine Learning (autoML) is employed for the classification of tweets of select US Furniture Retail Stores while various DEA models are utilized to analyze multiple input metrics to obtain an efficiency ranking for the selected brands. Based on these analyses, the study discusses the implications of the findings for small and medium-sized enterprise (SME) marketing managers at the industry level. Recommendations for industry practice are also provided in addition to the directions regarding future research.