Algonomy unlocks the power of social cues to algorithmically aid shopper decisions in Winter ’21 Release
Algonomy, a leader in Algorithmic Decisioning, announced the Winter ’21 Release of their platform that features several new and advanced uses of AI in real-time customer engagement. One of the main highlights of the release is Social Proofing, the ability to algorithmically build credibility and aid consumer decisions using social approval.
Sarath Jarugula, Chief Product Officer at Algonomy said, “The Winter ’21 Release extends our core focus on algorithmic decisioning to additional areas of marketing and commerce lifecycle. Our new capabilities are self-serve, enterprise grade, and proven with early adopter customers realizing breakthrough results from these technologies.”
Social Proof Messaging – With an expanding range of catalogs, assortments and products to choose from, digital consumers face a high degree of decision fatigue. To cut through the clutter, it’s helpful to indicate which products have the confidence of other shoppers. Social Proof offers a way to create trust, urgency or credibility during the shopping journey. Social Proof messaging content is algorithmically optimized in real time to show a variety of tactics like popularity – product views, purchases, add-to-carts, or urgency – such as low stock levels. The product is enterprise grade, real-time and self-serve for marketers, not requiring any developer support for set up.
Hyperlocal Search & Recommendations – Retailers often want to prioritize products that are closest to the shopper to ensure efficient delivery or pick-up to improve the omnichannel experience. Algonomy Find™ now offers geo-search capabilities, to surface products or services that are within a zip code or within a shopper’s geo coordinates. Brands that offer location based offerings can run personalization algorithms on such location filtered results. Hyper-localized search is now available for early adopters, and complements the existing hyper-localized product recommendation capabilities in Algonomy Recommend™.
Comments are closed.