Open data policy, e-commerce connectivity and catalog size predict a global brand’s online popularity rank

Abstract

Content marketing is increasingly important for online branding. However, brand popularity is not yet well-understood form a content marketing perspective. We used brand popularity and catalog data in combination with product reviews (n = 333 brands). Backward stepwise multiple linear regression was used to develop a predictive model of the brand popularity rank. We identified highly significant predictive factors for brand rank in our content marketing model: the brand’s data syndication policy, the number of connected e-commerce platforms, and a brand’s catalog size. Our model explains 78% of the variance of Global Brand Rank and has a good and highly significant fit: F (5, 327) = 233.5, p < 0.00001. We conclude that a content marketing model can adequately predict a global brand's popularity. In this model an open content syndication policy, more connected e-commerce platforms, and catalog size are each related to a better (lower) Global Brand Rank score.