Press release: Faraday launches Recommendations product into beta

FOR IMMEDIATE RELEASE

BURLINGTON, VT — Faraday, the leading infrastructure provider for customer behavior prediction technology, has announced the beta release of its newest prediction: Recommendations.

Users can employ this new prediction to determine which action among several each customer is most likely to take. For example, a retail brand could use Faraday Recommendations to personalize an email campaign, highlighting the “next best offer” each individual customer is most likely to buy. An insurance carrier could use this new technology to predict which insurance line each customer is most likely to adopt next in order to shape a more responsive customer journey.

Recommendation joins Faraday’s other two generally-available predictions—Outcomes, which gauge the probability of each individual exhibiting or not exhibiting a certain behavior, and Personas, which organize a group of customers or other individuals into thematically coherent subgroups—to round out its platform. A fourth, Forecast, which predicts how much an individual will spend with a given brand (also known as lifetime value or LTV), is available in “alpha” with a broader release aimed for later this year.

As with all Faraday predictions, Recommendations are powered by best-in-class AI algorithms combined with panoramic built-in consumer data—over 1,500 attributes illuminating nearly every adult consumer.

“At Faraday we believe that every customer experience should and will be predictive,” said Andy Rossmeissl, the company’s CEO. “Our mission is to predict every customer behavior, responsibly, and our new Recommendations product is a major step in delivering that mission.”

Bespoke Post, a leading e-commerce subscription brand, used an alpha release of Faraday’s Recommendations offering to improve the “add-on” bundling experience at checkout. When this feature was used, it delivered a 10% conversion rate lift compared to a control group.

Meriwest, a large credit union operating in the San Francisco Bay and Silicon Valley areas, also used Recommendations in alpha, this time to predict which financial service each member is likely to adopt next. The credit union plans to use these predictions to personalize email campaigns, with the goal of boosting conversion rates.

At the technical level, Faraday’s Recommendations product employs a variety of time-tested algorithms which work in concert to deliver high-confidence predictions. Building a recommendation “model” involves two stages. First, the system uses matrix factorization to implement a “collaborative filtering“ approach. Next, the system uses findings from the collaborative filtering model to inform a decision tree-based approach (random forest ensemble). This technique allows Faraday to make predictions about existing customers as well as brand-new leads or even unknown prospects.

“As an AI engineering project, this was an exciting challenge,” said Dr. Mike Musty, a Faraday data scientist, who was instrumental in designing the product. “Delivering self-service recommendations at scale required several novel optimizations, including our hybrid algorithmic approach.”

Existing and new Faraday clients looking to take advantage of the new Recommendations product while in beta should contact their account manager or file a support request.

About Faraday

Faraday is an infrastructure platform that offers unified AI and data for predicting customer behavior. Based in Burlington, Vermont, the company makes billions of predictions every day for thousands of brands and their partners. Learn more at faraday.ai.