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Every time a visitor views a promotion on your website, Evergage captures the visitor's "context," including whether he or she is a returning visitor, the device being used, survey responses, and other information that will give insight into that unique individual. Evergage uses this context to predict the expected value of showing a specific offer to a particular individual by evaluating both the chance of completion as well as the business value of the offer to the company. Using these predictions, Evergage then determines what promotions to show other similar visitors during future visits, displaying the the offer with the highest expected value. 

Since you may not want every factor to affect the prediction, the Evergage Contextual Bandit gives you control over which contextual factors affect machine learning results for visitors. For example, let’s say your financial services company has a defined an area on the homepage for highlighting promotions. But with creative for five different offers – credit cards, mortgages, auto loans, checking accounts and 529 plans – you're not sure which one to display to each site visitor. Contextual Bandit can take away the guesswork by instantly analyzing the data points collected about this visitor and presenting the offer that will generate the greatest lift.

This Article Explains

This article details how you can use Contextual Bandit to optimize your personalization efforts.

Sections in this Article

Understand the Data Analyzed

Contextual Bandit instantly analyzes many unique data points when determining the right experience to present to a particular visitor. These are based on visitor behaviors as well as demographic data. In addition to the data points listed below, Contextual Bandit can reference additional custom dimensions you create in your Evergage Catalog along with data points you create from segments, Groovy Scripts, or uploaded custom data like loyalty programs or customer portfolios.

Select Context

  1. Log in to Evergage with ________________ permissions
  2. In Data Science, select Decisions > Feature Engineering
  3. Most options on this screen are not configurable, but the options that include  can be configured to ___________:
    1. Geography - 
    2. Collaborative Filtering Features - 
    3. Survey Responses - 
    4. Custom Attributes - 
    5. Affine Departments - 
    6. Affine Dimensions - 
    7. Custom Script - 
    8. Segment Membership - 
  4. Selected options will be included by the Contextual Bandit 
  5. Click SAVE to save changes

Adjust Catalog Settings

In addition to making selections in the Feature Engineering section, you must also configure the attribution mode in the Evergage Catalog. Please see the Create Promotions in the Evergage Catalog article for more information and specific instructions.

Get Started

The process for using the Contextual Bandit with promotions includes the following high-level steps:

  1. Create Content: To create promotions, you'll need the creative images for the offer. You can start with just a few images and build your promotional catalog from there. For each individual campaign experience, all imagery should have the same pixel height and width.

  2. Upload Content: Although Evergage can extract product and content catalog data from a webpage, promotions and offers must be uploaded into the Evergage Platform.

  3. Goals & Rewards: For each promotional image added to Evergage, you must define a goal (the action you hope the promotion will drive) and the reward (the business value associated with completing that goal). The contextual bandit will use these to weigh each promotion, with the goal of accurately optimizing for business value rather than some other approximate metric.

  4. Deploy Campaign: Select a group of promotions and deploy them to an area of your site. Evergage will automatically start collecting data and learning who to show which promotion. If you add or remove a promotion performance is not affected, and promotions appearing in multiple locations will share learning between them.

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