Optimal 1-to-1 Experiences Powered by Advanced Machine Learning

Present each visitor the ideal promotion, banner, offer or experience with the highest probability of engagement and business value to your company.

Evergage Decisions™ enables B2C and B2B companies to leverage industry-leading machine learning to automatically determine and deliver the optimal promotion, offer, image or complete experience to individual website visitors, application users and email recipients. The first algorithm included in the Evergage Decisions module is Contextual Bandit, which utilizes sophisticated machine learning to evaluate both the likelihood of someone engaging with a particular offer as well as the business value of the offer to the company. For example, if a company has 15 different homepage hero images it could show someone, the model considers each image and all the data available about the person, and then delivers the most relevant experience with the highest potential value to the company – all in milliseconds.

UR Credit Union Image

With Evergage Decisions, you can upload numerous promotional offers for a specific area on your site – such as the premium credit card homepage offer shown here. Contextual Bandit will automatically determine what to display to a particular individual.

Kind of Like Magic
Rather than spend time defining rules about which experiences to show different audiences, Contextual Bandit frees business users to focus on creating powerful messaging and offers. In other words, you don’t need to worry about associating audience segments to particular personalization campaigns. The machine-learning capabilities of Contextual Bandit figures out the optimal experience each time, at the 1-to-1 level.

Offer Values
Contextual Bandit natively understands the value associated with an offer. If a retailer presents an offer for a pair of blue jeans, for instance, Contextual Bandit recognizes that it is worth $75 if a purchase is completed. For promotions that do not have a tangible dollar value associated to them, you can assign a synthetic value (e.g., $30 for an eBook download) to help the algorithm evaluate the best offer to display.

Extensive Data
Contextual Bandit factors in an expansive set of data when making decisions. While it’s always helpful to have as much information as possible about a particular visitor, Contextual Bandit functions effectively even when very little customer data is available. In addition to individual affinities and intent, which require behavioral tracking, the algorithm considers immediately knowable information like time of day and day of the week and visitor-specific data such as browser, device type, referring source, geolocation and time since last visit.

Simple Workflow
Within the Evergage platform, you simply add your creative assets, assign a value to each asset (if it’s not an offer associated with a purchase), set the content zone for the campaign on your website, in your app or in an email, and, if applicable, define a specific segment of users. Once deployed, Contextual Bandit uses continuous learning to calculate and present the best experience to each visitor.

Complement Recommendations
This solution is considered complementary to, rather than a replacement for, Evergage-powered recommendations. While recommendations focus on driving engagement and discovery by presenting products, content or other catalog aspects like brands, categories, styles, etc. based on an individual’s affinities, Contextual Bandit determines the optimal promotion, banner, offer or experience to show someone based on individual data and the business value to the company.

See for Yourself How Evergage Works