How are your brand’s Data Health and Product Completeness Score calculated?

A brand’s Data Health score (0-100%) is the average of the completeness scores of all individual product data sheets (PDSs) of the respective brand in the Icecat PIM or Free Vendor Central. Currently, we only include PDSs with Quality=Icecat in this calculation. In addition, we consider limiting the Brand Data Health score calculation to only a brand’s active products.

How is the Completeness Score calculated?

To interpret a brand’s Data Health score, one must first understand the meaning of the completeness score on the product level. In the Icecat Vendor Central, we visualize each product completeness score as a 0-100% bar. A product completeness score below 50% means that a product data sheet does not meet minimum requirements, and a score below 20% indicates that only logistical data is present. A score of 51-75% means that a product data-sheet is sufficiently informative. A 75-100% score means that a product data sheet is excellent as it contains many additional, nice-to-have information and rich media. In other words, the score is originally a content health indicator for (brand) editors on the PDS level, which we now aggregated on Brand-level.

In a pop-up – visible after a user clicks on the score bar – we show the break-down of the completeness score on the digital asset or attribute level.

An attribute or asset that contributes to the score changes color from red to green.

Optional fields are only included after all the Obligatory fields are filled in first. Below are the details for each asset and its value in the total product score.

How to improve Data Health?

The only way to improve the overall Brand Data Health score is thus to improve the score of individual PDSs. The best PDSs in our database score close to 97%. Therefore, the practical maximum of a brand with only great multimedia PDSs is around 97%. Many major brands score close to 55% on average, given their broad and deep portfolios. For most products, it’s sufficient to have a 50% completeness score, which means that all mandatory fields and media are included. Over time, the number of mandatory specifications increases as retailers up their requirements. Therefore, good PDSs might get a lower completeness score over time because new retailer requirements are not met. This naturally depresses also the overall score.

Nevertheless, an e-commerce-focused brand must improve on its Data Health score. In the case of small portfolios, it’s sufficient to look through the individual PDSs and determine which ones to complete. In the case of larger portfolios, a more structural approach is needed. Can a product feed be pushed to Icecat? A structured brand feed generically contributes to better data sheets, more rich media assets, more language-specific content, a higher chance that mandatory specs are filled in, and thus a higher overall Data Health score. Depending on the focus on certain channel partners, markets, and products, it’s good to improve respective individual product data sheets.

Is there an ideal Data Health score? No, there is not. But, it provides an indication of how much can be improved to satisfy complete e-commerce requirements.