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

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.

Hybrid Variable-Scale Clustering Method for Social Media Marketing on User Generated Instant Music Video

“…Previous research shows that the VSC could solve the social media marketing problem accurately and effectively. Wang et al [24] studies the multifunctional product marketing on social media through combining the VSC with sentiment analysis, which respectively is responsible for customer segmentation and customer satisfaction prediction. Experiment results illustrate that the VSC is able to recognize the feature (that is interest and concerns) of every divided customer base, which could support managers making customer-centered marketing strategies and campaigns.…”