Study on the Change of Marketing Strategy through Data Mining Technique

Jin, Kim Dae; Dain, Park; Park, Jong‐Seok

doi:10.17287/kbr.2018.22.2.177

Cited by 3 publications

(3 citation statements)

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Section: Methodsmentioning

confidence: 99%

“…The importance of each word or word bundle calculated in this way has higher reliability than simple frequency analysis because of the basic proposition that reliability is proportional to the number of occurrences but inversely proportional to the total number of text data mentioned (Kim et al, 2018). However, when this TF-IDF method is applied to Facebook and Twitter text data, words and keywords that can define fintech services, established through precedent research, may not be extracted, and words or word bundles that are irrelevant or have low relevance to the fintech service may be extracted.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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

Park

Kim

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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

Park

Kim

et al. 2022

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“…In addition, the number of customers is large, which is becoming more and more di cult to carry out marketing activities for customers. Moreover, with the continuous improvement of online shopping system, consumers' consumption psychology and consumption form are changing, and the traditional marketing method has been far from meeting the needs of today's consumers [1,2]. At this stage, many e-commerce companies do not have a clear target customers in their marketing that all users are publicized and promoted in a large quantity and full coverage way, which will make users receive a large number of messages without any di erence.…”

Section: Introductionmentioning

confidence: 99%

A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data

Zhang

Huang

2022

Mathematical Problems in Engineering

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In order to develop a more efficient and accurate marketing strategy for consumers’ purchase behavior, this paper establishes a user value model by modeling and learning the user historical data of e-commerce enterprises. The improved K-means algorithm is used to cluster the purchase behavior of users, and the customer value matrix is constructed from two dimensions of consumption frequency and average consumption amount. Finally, e-commerce users are classified into four categories by marking points. The test results show that the improved K-means algorithm is stable and efficient, and the analysis of user clustering characteristics is helpful to develop more accurate marketing strategies.

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Profiling the AI speaker user: Machine learning insights into consumer adoption patterns

Choi,

Lee

2024

PLoS ONE

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The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45–50 and 60–65, who are active on social networking platforms and have a preference for varied programming content, as the most likely future users. Additionally, the model reveals their distinct lifestyle patterns, such as higher internet usage during weekdays and increased cable TV viewership on weekends, along with a better understanding of 5G technology. This pioneering effort in IoT consumer research employs advanced machine learning to not just predict, but intricately profile potential AI speaker consumers. It elucidates critical factors influencing technology uptake, including media consumption habits, attitudes, values, and leisure activities, providing valuable insights for creating focused and effective advertising and marketing strategies.

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Study on the Change of Marketing Strategy through Data Mining Technique

Cited by 3 publications

References 0 publications

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

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

A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data

Profiling the AI speaker user: Machine learning insights into consumer adoption patterns

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