Original scientific paper

https://doi.org/10.17559/TV-20181120082714

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

Ai Wang orcid.org/0000-0001-8877-3718 ; University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, China
Xuedong Gao ; University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, China

Abstract

Customers' demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management.

Keywords

customer satisfaction; scale transformation; short text clustering; social media mining

Hrčak ID:

217143

URI

https://hrcak.srce.hr/217143

Publication date:

16.2.2019.

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