SAN FRANCISCO, April 6, 2021 /PRNewswire-PRWeb/ -- Vidora, the leading No-Code Machine Learning Platform for marketing, today announced the general availability of Uplift Modeling within its flagship product, Cortex. The launch enables any marketing or product team to quickly integrate Uplift Models across dozens of use cases to drive higher ROI, increased offer response rates, reduced churn, higher ASPs, and larger order sizes.
Uplift Modeling, which directly optimizes the impact of marketing or product interventions on users, is a well-known tool for marketing and product initiatives. Despite Uplift Modeling's benefits, the technology is deployed sparingly because of the difficulty in building high-performing Uplift Models and the challenges associated with integrating models into the Martech stack. Vidora's launch solves both issues making Uplift technology accessible to all businesses.
"During our early customer testing of Uplift Modeling we recognized its ability to drive ROI across marketing, product, and ad-tech experiences," noted Vidora product manager Michael Firn. "This launch takes away most of the complexity associated with Uplift Modeling, enabling teams to deploy the technology liberally throughout their marketing and product experiences."
Uplift Modeling is used extensively by large B2C marketers, including Uber, US Bank, and Wayfair. Use cases for Uplift Modeling include dynamic paywall decisioning, retail coupon pricing and targeting, churn reduction, attribution modeling, and measuring the brand-lift of advertising.
"Sophisticated marketers have recognized the value in Uplift Modeling for years. It turns out that dozens of common challenges across marketing and product teams can be optimized using Uplift Modeling. The business impact to organizations is tremendous." Added Mark Donnigan, Virtual CMO for disruptive innovation startups and Principal at Growth Stage Marketing.
Uplift Modeling is now available to Vidora customers within Cortex along with other machine learning models like predictions, recommendations, and look-alike models. Contact [email protected] for more information.