Act-On Software has unveiled a new layer of machine-learning technology that helps marketers make predictive and intelligent recommendations.
Act-On Software, a marketing automation company, officially launched Adaptive Journeys on Monday to help marketers delivering the best message at the best time and across the best channel.
Adaptive Journeys, those same automated campaigns can also adapt in real-time, based on a customer’s preferences, interests and engagement history.
Adaptive Journeys leverages machine-learning algorithms to make predictive recommendations on a variety of challenges marketers face, from finding the correct audience to selecting the optimal channel that marketing communications should be sent to.
Adaptive Segmentation combines CRM data and engagement behavior to automatically segment audiences for more personalized communication. An Adaptive Forms feature helps marketers deliver the correct communication by dynamically changing conditional follow-up questions, based on previous responses.
Act-On’s Adaptive Sending feature automates send-time optimization based on past behavior.
Additional product features includes Adaptive Scoring, for more data-driven lead scoring, as well as an Adaptive Channel tool that leverages machine learning to select email, Web, mobile or social as the correct channel to deliver a marketing message.
Michelle Huff, CMO at Act-On Software, explains customers are beginning to expect adaptive marketing, as opposed to static batch-and-blast campaigns, adding Act-On want to assist marketers in building more authentic relationships with their customers.
“You have to first understand the person,” says Huff, before you can send personalized and optimized content to them.
Comparing Adaptive Journeys to the popular mapping technology Waze, Huff describes how Act-On creates a marketing platform that leverages large data sets to adapt to the customer journey.
Huff highlights Act-On’s cloud-based architecture as a major differentiator for the company. Whereas other large competitors are still restructuring their products so they can be integrated and work together, Act-On’s platform was built in-house on a modern, NoSQL cloud architecture. It already has the capability to capture large data sets at scale.
As is the case with any machine-learning algorithm, Act-On’s solution will become more powerful the more it is used. “The more data you have and the longer you use it, the better it gets,” says Huff. “You get more accuracy and better recommendations.”
The company plans to prioritize building out its predictive engagement engine in its 2017-18 product roadmap. “This is not a one-quarter release and we’re done,” says Huff, describing how wants to see lead scoring become more adaptive and intelligent in the future.