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

Background: content marketing is increasingly important for online branding. Brand popularity can be more easily determined online than sales-based measures but is not yet well-predicted using content marketing insights. Promising predictors are open data syndication policies, connectivity to e-commerce platforms, product reviews, data health, and the depth and width of a brands product portfolio. A predictive content marketing model can help brand owners to understand their e-commerce potential. Methods: we used brand popularity and catalog data in combination with product reviews from an independent content aggregator. For all datasets, we selected the overlapping dataset for brand popularity and brand reviews based on a period of 90 days from June 10, 2022, till September 24, 2022 (n = 333 brands). Backward stepwise multiple linear regression was used to develop a predictive content marketing model of the brand popularity rank (Global Brand Rank). Results: through stepwise backward multiple linear regression five highly significant (p < 0.01) predictive factors for brand rank are selected in our content marketing model: the brand’s data syndication policy, the number of connected e-commerce platforms, a brand’s number of products, its number of products per category, and the number of product categories in which it is active. Our model explains 78% of the variance of Global Brand Rank Global Brand Rank and has a good and highly significant fit: F (5, 327) = 233.5, p < 0.00001.Conclusions: we conclude that a content marketing model can adequately predict a Global Brand Rank based on online popularity. In this model an open content syndication policy, more connected e-commerce platforms, and catalog size, i.e., presence in more categories and more products per category are each related to a better (lower) Global Brand Rank score