Content monetisation company Skimlinks is now able to use its masses of shopper intent data to reveal pre-purchase intent at a product level for the first time.
The development will allow programmatic advertisers and agencies to target customers and prospects by the “product they’re actively seeking” beyond just retailer type or brand, and doesn’t require users to visit a site first, like cookie-reliant retargeting.
The update forms part of Skimlinks’ Audiences - data initially made available to programmatic ad platforms in 2015 and now including data from over 1.5 million publishers - but says “pre-targeting” has only been made possible recently.
Unlike retargeting, which targets user intent based on the product pages they’ve already visited a retailer’s site, this doesn’t require any action from the consumer, based instead on content a consumer has engaged with, “especially when researching a product”.
“Microwaves or Sony products"
While most data vendors already use page impression data and URL analysis to predict interest, Skimlinks has used machine learning to analyse each page impression URL, all product links and the corresponding metadata for each product link on that page - including brand, product category and price.
That’s enabled them to produce 900 product categorizations, allowing programmatic advertisers to target ads for products as granular as “microwaves or Sony products”, for example.
The company claims to have shopping predictions available on 600 million of its 1.1 billion anonymously monitored customer profiles. The remaining 500 million, it’s confident, are “demonstrably not currently in the market for any specific type of product.”
“For data buyers today, scale is still important, but increased conversions and measurable performance are becoming key metrics,” said Navarro, “Shopping intent data that is granular and optimised towards achieving a conversion is going to delight advertisers more than broad and unspecific socio-demographic data, which has long been the industry norm.”