A Two-Stage Variable-Scale Clustering Method for Brand Story Marketing of Time-Honored Enterprises

Abstract

Brand story, as a communication tool of brand's core values, is proven to be effective in building consumers' trust and loyalty. This paper focuses on the story's protagonist identification and story's audience segmentation problem of consumers' brand storytelling, so as to support managers making brand marketing decisions. After building the scale space signature that characterized the story feature, an object pair scale transformation mechanism (Object Pair-ST) is proposed, which enables to realize the collaborative transformation of the encoded scale feature of both story's protagonists and audiences. According to the Object Pair-ST mechanism, a two-stage variable-scale clustering method (Two-stage-VSC) is put forward. The method could obtain the matched story clues (including the elements of plots, characters, causality and chronology) for every audience cluster as well as the matching degree, which are ordered by the hotness degree of consumers' interest. Experiments collected 11877 real review data on the 2022 first sales platform of time-honored enterprises, Tmall. The comparative experimental results indicate that for various types of brands, the proposed method Two-stage-VSC could always keep the granular deviation of each audience cluster at a low level while accurately identifying its protagonist cluster's story clues with the highest matching degree.

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

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