Social Media as A Source of Predictive Analytics for Customer Satisfaction: An Empirical Investigation of Standard Gauge Railways Users in Kenya - Ntale, Moses, Kizito, Njagi; Mathenge, Paul; Gikonyo, Barnabas (My University, 2020)
Predictive analytics is used to analyze the vast amounts of information generated through internal and external sources such as live public transit data, train schedules, and bus feeds. The collection, storage, and mining of big data is on an increase as more automated platforms come online and this is an issue that is gaining attention in all business environments raising privacy concerns. However, the amount of data and its variety in data analytics may cause data management issues in areas of data quality, consistency and governance; resulting from different platforms and data stores in big data architecture causes data silos. Furthermore, integrating big data tools into a cohesive architecture that meets an organization's big data analytics needs is a challenging proposition for the analytics experts, which have to identify the right mix of technologies and then put the pieces together. This study therefore, investigated the influence of social media as a source of predictive analytics on customer satisfaction of Standard Gauge Railways (SGR) users. This research followed a cross sectional survey research design. The study targeted the customers and employees of SGR Nairobi terminus from which a sample size of 68 respondents was picked using from the Nairobi Terminus station through use of convenient sampling technique. This study used a questionnaire to collect primary data. Data obtained from the field was converted into useful information using qualitative and quantitative description qualitative was done through observation and analyzed through use of content analysis. On the other hand, quantitative data was analyzed through inferential techniques namely correlation and multiple regression. The findings indicated that usage of social media for information, mode of payment, SGR classes, and rates/fare had significant effect on customer satisfaction. It was recommended that the management of SGR should: place regular offers of discounts or freebies and give away on its sites; frequently update its social media sites with interesting information and product updates; and make the social sites more interactive to allow online members to invite others who are non-members of these social sites to sign up for the SGR services.
Keywords: Big Data; Customer Satisfaction; Data Analytics; Predictive Analytics; Social Media