Abstract
Any brand’s presence on social networks has a significant impact on emotional reactions of its users to different types of posts on social media (SM). If a company understands the preferred types of posts (photo or video) of its customers, based on their reactions, it could make use of these preferences in designing its future communication strategy. The study examines how the use of SM technology and customer-centric management systems could contribute to sustainable business development of companies by means of social customer relationship management (sCRM). The two companies included in the study provide a general consumer good in the beverage industry. As such, it may be said that users interacting with the posts these companies make on their official channels are in fact customers or potential customers. The study aims to analyze customer reaction to two types of posts (photos or videos) on six social networks: Facebook, Twitter, Instagram, Pinterest, Google+ and Youtube. It brings evidence on the differences and similarities between the SM customer behaviors of two highly competitive brands in the beverage industry. Drawing on current literature on SM, sCRM and marketing, the output of this study is the conceptualization and measurement of a brand’s SM ability to understand customer preferences for different types of posts by using various statistical tools and the sentiment analysis (SA) technique applied to big sets of data.
Highlights
The current research deals with an essential topic of interest for any company that intends to have a sustainable development and to thrive in the current economic landscape which is ever more competitive
Though it belongs to the artificial intelligence (AI) domain, namely is considered a machine learning (ML) technique, sentiment analysis (SA) is a social media (SM) analytics tool that involves checking how many negative and positive keywords are included in a text message associated with a SM post
As social networks play an important role for the sustainable development in business, this study provides a deep analysis of customer reactions to SM posts using a set of artificial intelligence (AI) techniques, such as sentiment analysis, sentiment polarity classification (SPC) and mosaic plots
Summary
The current research deals with an essential topic of interest for any company that intends to have a sustainable development and to thrive in the current economic landscape which is ever more competitive Though it belongs to the artificial intelligence (AI) domain, namely is considered a machine learning (ML) technique, sentiment analysis (SA) is a social media (SM) analytics tool that involves checking how many negative and positive keywords are included in a text message associated with a SM post. The organizations should strive towards providing a consistent experience across communication channels and integrated business platforms This way, companies are able to reach a new level of competitive customer management as customers are no longer passive recipients but mostly proactive
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