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.
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.
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.