Evergage Unveils New Machine-Learning Innovations to Improve the Impact of and Ability to Analyze Personalization Efforts

New Data Science Workbench Provides Data Scientists with Direct Access to All the Rich Data in Evergage, Along with Analysis, Modeling and Visualization Tools

Evergage, The 1-to-1 Platform company, announced two new machine-learning innovations to help companies drive greater results with 1-to-1 personalization. With Evergage Decisions and the Evergage Data Science Workbench, companies can tap into their rich customer data, maintained in the Evergage platform, to maximize the performance of their personalization campaigns, and improve conversions and loyalty.

Evergage Decisions 
For companies with multiple content assets – such as promotions, messages and images – it can be challenging to match the ideal experience to a specific visitor in real time. For example, a financial services firm might have a defined area on its homepage to highlight promotions – such as for credit cards, mortgages, auto l**** and 401k plans – and wants to display the optimal one to each visitor, considering the likelihood of engagement and the value to the business.

With the new Evergage Decisions algorithms, B2C and B2B companies can apply industry-leading artificial intelligence (AI) to automatically deliver the most relevant content – or complete experience – to each website visitor, application user and email recipient.

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Complementing Evergage’s already-powerful, machine-learning-driven, 1-to-1 personalization and recommendation capabilities, Contextual Bandit, the first algorithm launched as part of Evergage Decisions, goes further – delivering the most relevant offer or experience with the highest potential value to the company. This is computed by taking into account both the probability of engagement at the user level and the revenue opportunity or synthetic value (for non e-commerce use cases) at the business level.

Factoring in deep behavioral data, including a visitor’s digital engagement and the context of each session, along with other situational and attribute criteria (e.g., referral source, browser, device type, lifetime value, geolocation, etc.), Contextual Bandit:

• Estimates the probability of each person interacting with each available offer or experience on a given channel (website, web app, mobile app, email) in real time.
• Uses advanced machine learning to predict the content for each visitor with the highest-value return – weighing the probability of someone accepting a particular offer or promotion, with the business value of that offer to the company. And unlike A/B testing methodologies, which can only determine the best choice for all visitors, Contextual Bandit delivers automatic personalization to determine the best experience for each individual visitor.
• Frees up marketers to focus on creating powerful messaging and offers, rather than spending lots of time defining rules about which experience to show which audience every time.

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Evergage will be hosting a webinar on 14 November 2018 at 1 p.m. ET, to discuss and demonstrate the power of Contextual Bandit.

“Evergage’s platform and full suite of personalization capabilities – now including Evergage Decisions –  represent the future of personalization at the individual level,” said Karl Wirth, Evergage CEO and co-founder, and author of the award-winning book “One-to-One Personalization in the Age of Machine Learning.” “Contextual Bandit is a win-win – blending what’s most helpful to the customer with what’s best for the business. These advanced capabilities go beyond what any other personalization provider today offers – underscoring Evergage’s market leadership and commitment to helping companies improve customer engagement, loyalty and results.”

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