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ZineOne mines data throughout the funnel to optimize predictive engagement results.

January 4, 2021

ZineOne mines data throughout the funnel to optimize predictive engagement results

Milpitas, CA, January 4, 2021Predictive engagement leader, ZineOne, today announced RevPredict, the first in a series of enhanced capabilities to its popular Intelligent Customer Engagement (ICE) platform. Long the choice of major retailers and banks such as Men’s Warehouse, HDFC, The North Face, and more, the ICE platform enables business users to understand and respond in-the-moment with relevant 1:1 customer engagements to influence desired outcomes.

Via a real-time dashboard, RevPredict enhances the ICE experience by allowing brands to identify and quantify lost revenue opportunities on their website at no cost. Powered by ZineOne’s Early Purchase Prediction model, which predicts purchase outcomes based on ordered sequences of data early on in a visitor’s session, RevPredict identifies site visitors by type with a high level of accuracy. This allows businesses to motivate the most influenceable visitors to engage through different real-time actions that are immediate and relevant.

Actionable insights have yielded significant results for ZineOne’s enterprise customers, such as the following: 

  • Up to 45% increase in conversion rates for targeted audiences.
  • Up to 3% increase in overall conversion rates.
  • Over $25M in annual incremental revenue growth.

Visit today to use the RevPredict interactive dashboard and learn more about the 30 days, no-cost program.

Enabling Engagement at the Right Moment

Marketers spend a significant amount of resources to attract consumers to the top of the funnel, yet 70% of carts are abandoned and only about 3% of shoppers make a purchase. The reason often relates to the type of consumer data and where it resides in the funnel. 

Whereas many AI-powered personalization engines focus on data residing at the top of the tunnel, ZineOne’s ICE platform leverages this data as well as the riches of the data found in the middle and bottom of the funnel.

The data found here offers historical insights into the consumer’s journey, e.g., they may have used certain products, have engaged with certain areas of the site, or through other channels, which provide clues to the focus of their shopping journey. These insights create Customer DNA that brands can now leverage. Many other types of data further enhance the customer’s experience: location is critical, especially with people on their mobile devices; weather and events also help create highly relevant offers in-the-moment. 

RevPredict leverages this deeper data-mining of the funnel to enable engagement at the right moment by accurately and swiftly identifying the type of visitor early in their shopping journey. This allows businesses to motivate consumers to engage through different real-time actions, which can translate to unrealized revenues.

“Consumers expect relevant and helpful engagement, but they are most effective only when done at the right moments,” states Debjani Deb, CEO and co-founder of ZineOne. “We are constantly delivering new capabilities to ZineOne’s Intelligent Customer Engagement platform to allow companies and brands to uncover the precise ‘moment’ to interact with visitors across channels, right when they need it. RevPredict is another step in the journey we take with our customers and partners.”

About ZineOne

The ZineOne Intelligent Customer Engagement platform enables enterprises to understand their consumers and respond to them with in-the-moment, relevant 1:1 engagement.  ZineOne’s next-generation platform has positioned the company as a leading predictive engagement provider delivering about 1 billion dollars in new revenue for its customers. The patent-pending platform and its continuously learning models provide predictive insights into each and every visitor across digital and physical channels while delivering intelligent customer experiences in critical moments to delight customers, foster loyalty, and increase revenues.

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