Now, let’s take a look at how we make sense of shopper interest across opted-in retailers.
We receive thousands of product feeds from retailers and brands, but the way products are identified is inconsistent, and feeds don’t always contain brand and category attributes.
So how can we tell, across all the retailers opted-in to the interest map, which products, categories, and brands our shopper Diane is interested in from her past browsing and buying behavior?
A blue dress on one retailer’s product feed could be ‘product ID 12321340’, but in another retailer’s product feed, the very same product that is the same make and model could be ‘product ID 394234’, without any category or brand classification.
In order to consistently interpret products across thousands of retailer and brand product feeds, we first use Universal Catalog to standardize category and brand identifiers.
Universal Catalog is Criteo’s proprietary machine-learning technology that detects similarities between a retailer’s product feed and Criteo’s training set which contains products which have already been mapped to a Global SKU.
Universal Catalog then assigns each product a unique Global SKU using the product’s global trade item number, or GTIN – a de facto standard for product identification.
And if the product’s GTIN is not available, Universal Catalog uses similarities between the retailer’s product feed and products in our training set. We do this for every product from opted-in retailers.
So what does all this mean? Well, thanks to Universal Catalog being a part of interest map, we now know that both these blue dresses are in the same category and in fact, they’re actually the same product. Therefore, both products are assigned the same Global SKU.
And since Diane has looked at the same products, categories, and brands across multiple retailers in the past, we know that she’s interested in blue dresses.
And while we’re able to understand her shopper interest based on historic shopping data from thousands of opted-in retailers, interest map doesn’t contain or leverage any data that can be used to identify the names of those retailers or their account ID.