Evergage’s new personalization algorithm, Contextual Bandit, is designed to leverage artificial intelligence (AI) to process vast stores of data, positioning companies to automatically deliver the most relevant and optimal content, offer or promotion to each website visitor, application user or email recipient.
With this insight, users can:
- Estimate the probability of each person interacting with each available offer;
- Apply continuous machine learning to predict the content for each visitor with the highest-value return;
- Present each visitor with the optimal experience — considering offer value and likelihood to engage; and
- Take “promotion-fatigue” into account — examining how often someone has seen an offer without acting and learning the right time to show something new.
Evergage is designed to natively understand the dollar value associated with each offer or promotion. In B2B marketing, where assets typically don’t have dollar values (whitepapers, webinars, etc.), marketers can assign “synthetic values,” so the algorithm works its magic. If a visitor has interest in multiple areas — even slightly more in a “lower-value” area — the algorithm may still feature the higher-value item, given the business value to the company.
The solution is designed for digital marketers at companies across industries, including B2B technology, financial services, retail and more.
It’s easy for companies to efficiently exchange information between Evergage’s customer data platform and other complementary systems, such as CRM, email and marketing automation, analytics systems, etc.
Evergage is delivered via software-as-a-service (SaaS) and licensed on an annual subscription basis. Contextual Bandit is included within the Evergage 1 solution offering.
Always learning and including first-of-their-kind capabilities, Contextual Bandit’s sophisticated machine learning is designed to blends what’s best for the customer with what’s best for the business, so companies can present each visitor, in real-time, with the optimal offers across channels.