Marketing strategies for fintech companies: text data analysis of social media posts

Abstract

PurposeThis study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.Design/methodology/approachFirst, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.FindingsThis study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.Originality/valueThis study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.

The power of words: driving online consumer engagement in Fintech

“…The Fintech brands create value through active consumer engagement, education and information sharing on social media (Breidbach and Ranjan, 2017). The OCE, achieved through real-time social media communication as well as promotional activities, acts as a key enabler to enhance the brand's service reputation (Oh et al, 2023;Sahai et al, 2018).…”

“…However, OCE in the Fintech context has gained limited attention, and further research is needed to advance the understanding of branding financial institutions through effective social media engagement (Glavee-Geo et al, 2019;Savitha and Hawaldar, 2022). For digital-first sectors like Fintech, there is a high reliance on social media platforms to connect with customers and provide them with financial services (Oh et al, 2023). Hence, examining individuals' reactions, expectations and evaluations of social media messages relating to Fintech brands (Dootson et al, 2016) is essential.…”

“…, 2019; Savitha and Hawaldar, 2022). For digital-first sectors like Fintech, there is a high reliance on social media platforms to connect with customers and provide them with financial services (Oh et al. , 2023).…”

Sentiment Analysis of User Preference for Old Vs New Fintech Technology Using SVM and NB Algorithms

“…It is calculated as the ratio between the number of elements in common in two data sets and the number of unique elements in both sets [21,22,23,24,25]. The Jaccard Score ranges from 0 to 1, with a score of 1 indicating percentage identity (both sets have elements in common) and a score of 0 indicating no elements in common [26,27,28,29]. Jaccard Score is often used in data mining, text mining, and cluster analysis to measure the similarity between data sets.…”

Social Media Data Analytics in the Automotive Industry: A Study of the Interactive Impact of Marketing Strategies and User Ratings

“…Zhou Yanfeng and colleagues pointed out the importance of social media communication and consumer emotional tendency analysis in the "Micro-influencers' era", and their data analysis revealed the emotional tendency of consumers and the characteristics of consumer groups, which provided guidance for the marketing of internet celebrity brands [4]. In the field of fintech and product planning, Oh S et al explored how fintech companies can develop marketing strategies by analyzing text data [5]. Not only that, Fuciu M's research demonstrated that social media analysis can also help enterprises better understand and meet the needs of the target audience, so as to achieve higher financial benefits [6], while Jeong B et al used topic modeling and sentiment analysis to identify potential opportunities in product planning [7].…”