Icecat Rank: What is it and How to Improve on it?

Icecat Rank Manual thumbnail

Icecat ranks every brand, every category and every product in its database. These rankings are based on billions of annual product datasheet (PDS) downloads by its channel partners and views by consumers. A lower rank number means a better score. The better the rank, the more views or downloads are needed to improve on the ranking. The ongoing Icecat brand rank project is part of the brand dashboard project.

What are the key drivers for the Icecat brand or category rank?

  • The number of unique products: the more unique products belonging to a category or a brand in its online portfolio the higher the total hits on the content of all products combined. Further determinants are the depth and breadth of a manufacturer’s portfolio.
  • Popularity: the popularity of individual products is an important driver. Sometimes it’s a relatively small number of products that is generating most traffic.
  • Stock: the more products are on stock in the market, the more they are visible in shop search engines.
  • Active products: more products for sale, i.e., visible in online catalogs or shops leads to more PDS views or downloads.
  • Data Health or PDS completeness: described products receive far more attention than undescribed products. Certain product features, media assets, and product relations help to improve a product’s attractiveness. Brand editors can complete PDSs for free via the Icecat Vendor Central.
  • Channel (platform) coverage: more connected channel partners lead to higher downloads.
  • Product reviews: more and favorable product reviews help to generate product interest by consumers and the channel. Of course, they need to be genuine and trusted.
  • Open content policy. An open content syndication policy (like Open Icecat) makes it easier for consumers and channel partners to view and download a brand’s PDSs in respective categories.

Findings from Brand Rank Research

Our research indicates that the Brand Rank is a meaningful variable, that can be well-explained by a manufacturing brand’s content marketing strategy. Especially, impactful are the adoption of an open content syndication policy, the number of connected e-commerce platforms in a brand’s ecosystem, and the size, depth and breadth of a manufacturers online portfolio. Data Health, based on the completeness scores of product datasheets, correlates with the average review score, which in turn correlates with brand rank as well. The same factors that drive brand popularity drive category popularity as well, given that it is both based on PDS download measures.

We didn’t study yet the factors pricing and stock. A better understanding of the relationship between reviews score and data health is also a goal in our data science project.

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