Contextual multi-armed bandits

  • Updated
  • Optimizely Web Experimentation
  • Optimizely Personalization

This feature is currently in beta. Contact your Customer Success Manager to learn more.

Use contextual multi-armed bandits (CMABs) with Optimizely to maximize your conversions by delivering personalized variations that match user attributes. While multi-armed bandits (MABs) look for the single best-performing variation for all users, CMABs pick a winning variation for each user based on their contextual profile and impact on the primary metric.

The benefits include:

  • Provides the most personalized experience for every user.
  • Increases the chances of conversions on the primary metric.
  • Adapts to changes in visitor behavior, dynamically serving the best variation in every session.

Like MABs, CMABs optimize toward the primary metric but also account for user attributes in that optimization. They do not have sticky bucketing like A/B tests. See Experimentation distribution modes for information on Optimizely's automated distribution modes and use cases.

Distribution goals

To set up a CMAB, pick a distribution goal. Distribution goals allocate rates of exploration and exploitation to identify the purpose of the CMAB. Exploration is the rate at which users are served variations by random chance. Exploitation is the rate at which users are served personalization variations given a user's attributes. Exploration must be at least 5% for the machine learning model to continue learning over time and cannot exceed 50%. Otherwise, users are served non-personalized variations the majority of the time.

  • Automated (default) – Dynamically adjusts the exploration/exploitation rates over time as Optimizely's machine learning model learns more. Choose this goal if you want Optimizely's machine learning model to decide the traffic distribution.
  • Maximize Personalization – Sets the exploration rate to 10% and the exploitation rate to 90%. Choose this goal to maximize the number of users who see a personalized variation. Although it maximizes lift, it is harder to know what the lift is from because of the emphasis on personalization.
  • Evaluate Algorithm – Balances exploration and exploitation with a 50/50 split. Choose this goal to get the most accurate estimate of the lift. This is helpful if you have random variations.
  • Manual – Lets you set the exploration and exploitation rates manually. Choose this goal if you know exactly the rates you want to configure.

DistributionGoal.png

Configure a CMAB

  1. Create a new campaign or select an existing one.
  2. Click Create New Experience.

    CreateNewExperience.png

  3. Select your audiences.

    NewExperience.png

  4. (Optional) Name your experience. If you do not enter a name, the audience name is used.
  5. Select Contextual Multi-armed Bandit for the Distribution Mode.

    CMAB.png

  6. Select the Distribution Goal from the drop-down list: Automated, Maximize Personalization, Evaluate Algorithm, or Manual. The default is Automated.

    ChooseDistributionGoal.png

  7. (Optional) Set the holdback.
  8. Add User Attributes the contextual bandit should target for the variation. These are attributes available under Audiences > Attributes, including any custom attributes, external attributes, and out-of-the-box attributes. The attributes include last visit, membership groups, locations, or past purchases.

    UserAttributes.png

  9. Click Create Experience.
  10. Use the Visual Editor to edit your variations for the selected user attributes.
  11. Test your campaign then publish by clicking Start Campaign.