Facebook is announcing three new offerings to drive mobile and offline retail sales. They’re really product evolutions or extensions of existing solutions previously introduced: store sales optimization, Tabs for Canvas and product categories for dynamic ads.
All of them utilize Facebook interest and behavioral data for varying degrees of personalization.
Store sales optimization uses machine learning “to help retailers show ads to people most likely to make an in-store purchase, even if they are not an existing customer.” Early customer Michael Kors used this in connection with a branded Instagram video campaign to generate an 11 percent incremental lift in in-store sales, which is significant — especially if these are new customers.
Facebook is also adding personalization to its Collection ads. The new capability is called “Tabs for Canvas,” and when users click on product-centric ads in the News Feed, they’ll be taken to a full-screen mobile ad experience with a “personalized catalog organized according to their interests”:
Tabs for Canvas are curated using the same product recommendations engine that powers dynamic ads today. This enables us to show shoppers the parts of a catalog most relevant to them, both within news feed and in the fullscreen experience, based on their changing interests and shopping behavior.
With Tabs for Canvas, Facebook says Sephora saw a 32 percent higher return on ad spend.
The final offering, “Categories for dynamic ads,” is an effort to reach shoppers earlier in the purchase cycle. It enables retailers to dynamically showcase products in a particular category that a shopper has displayed in, but hasn’t yet decided on a specific product or item. Here again, Facebook cites impressive results from a beta customer, TechStyle Fashion Group.
All three of these new capabilities are intended to help (mostly traditional) retailers sell more and come on the same day that Google announced its own efforts to help retailers perform better via mobile and voice search and compete more effectively against Amazon.