Next-Best-Action recipe

The Next-Best-Action (aws-next-best-action) recipe generates real-time recommendations for the next best actions for your users. The next best action for a user is the action that they will most likely take. For example, enrolling in your loyalty program, downloading your app, or applying for a credit card.

With Next-Best-Action, you can provide personalized action recommendations for your users as they use your application. Suggesting the right action for a user can result in more users taking your actions. Depending on the actions you want to recommend, you can increase customer loyalty, generate more revenue, and improve the user experience of your application. For a use case example that describes how personalized action recommendations can benefit an ecommerce application, see Use case example.

Amazon Personalize predicts the next best action from the actions you import into your Actions dataset. It identifies the actions that a user will most likely take based on their interactions with actions and items. If your action data includes the value of the action, Amazon Personalize accounts for the action's value. If a user is equally likely to take two different actions, Amazon Personalize ranks the action with the greater value higher.

When you get real-time action recommendations for a user, Amazon Personalize returns a list of actions that the user will most likely take within a configurable period of time (the action optimization period). For example, the actions they will most likely take in the next 14 days. The list is sorted in descending order by propensity score. This score represents the likelihood that the user will take the action.

Until you import action interaction data, Amazon Personalize recommends actions in your without personalization, and propensity scores are 0.0. An action will have a score after the action has the following:

• At least 50 action interactions with the TAKEN event type.

• At least 50 action interactions with the NOT_TAKEN or VIEWED event type.

These action interactions must be present at the latest solution version training, and must occur within a span of 6 weeks from the latest interaction timestamp in the Action interactions dataset.

For more information about the data the Next-Best-Action recipe uses, see Required and optional datasets.

When you create a solution with the Next-Best-Action recipe, you can configure the window of time Amazon Personalize uses when predicting actions by using the action optimization period featurization hyperparameter. For more information, see Properties and hyperparameters.

Use case example

Suggesting the right action for a user can result in more users taking your actions. Depending on the actions you want to recommend, you can potentially increase customer loyalty, generate more revenue, and improve the user experience of your application.

For example, you might have an ecommerce application that suggests the following different actions:

• Subscribe to loyalty program

• Download mobile app

• Purchase in Jewelry category

• Purchase in Beauty and grooming category

You might have a user who frequently shops at your site and has repeatedly taken the Jewelry and Beauty and grooming purchase actions. For this user, Amazon Personalize action recommendations and their scores might include the following:

• Subscribe to loyalty program

  Propensity score – 1.00

• Purchase in Jewelry category

  Propensity score – 0.86

• Purchase in Beauty and grooming category

  Propensity score – 0.85

With these action recommendations, you know to prompt the user to enroll in your loyalty program. This action has the highest propensity score and it is the action the user will most likely take. This is because the user frequently shops at your store and is likely to engage with the benefits from your loyalty program.

Recipe features

The Next-Best-Action recipe uses the following Amazon Personalize recipe features when generating action recommendations:

• Real-time personalization: Amazon Personalize uses real-time personalization to update and adapt action recommendations according to a user's evolving interest. For more information, see Real-time personalization.

• Exploration: With exploration, recommendations include new actions or actions with less interactions data. For more information about exploration, see Exploration.

• Automatic updates: With automatic updates, Amazon Personalize automatically updates the latest model (solution version) every two hours to include new actions in recommendations through exploration. For more information, see Automatic updates.

Required and optional datasets

To use the Next-Best-Action recipe, you must create the following datasets:

• Actions: You import data about your actions, such as their value, into an Amazon Personalize Actions dataset.

  In your actions data, you can provide an EXPIRATION_TIMESTAMP for each action. If an action has expired, Amazon Personalize won't include it in recommendations. You can also provide a REPEAT_FREQUENCY for each action. This indicates how long Amazon Personalize should wait before recommending an action again after a user interacts with it. For information about the data an Actions dataset can store, see Actions dataset.

• Item interactions: Your Item interactions dataset must have at minimum 1000 item interactions. Amazon Personalize uses item interactions to understand your users' current state and their interests. For information about the item interactions data, see Item interactions dataset.

The following datasets are optional:

• Action interactions dataset: An action interaction is an interaction involving a user and an action in your Actions dataset. You can import Taken, Not taken, and Viewed action interactions. Although this data is optional, we recommend that you import action interaction data for quality recommendations. If you don't have action interaction data, you can create an empty Action interactions dataset and record your customers' interactions with actions by using the PutActionInteractions API operation.

  Until you import action interaction data, Amazon Personalize recommends actions in your without personalization, and propensity scores are 0.0. An action will have a score after the action has the following:

  • At least 50 action interactions with the TAKEN event type.

  • At least 50 action interactions with the NOT_TAKEN or VIEWED event type.

  These action interactions must be present at the latest solution version training, and must occur within a span of 6 weeks from the latest interaction timestamp in the Action interactions dataset.

  For information about the action interactions data you can import, see Action interactions dataset. For information about recording action interaction events, see Recording real-time action interaction events.

  Note

  With Next-Best-Action, Amazon Personalize doesn't use impressions data or contextual metadata in an Action interactions dataset.

• Users: Amazon Personalize uses any data in your Users dataset to better understand your users and their interests. You can also use data in a Users dataset to filter action recommendations. For information about the user data you can import, see Users dataset.

• Items: Amazon Personalize uses any data in your Items dataset along with your Item interactions dataset to identify connections and patterns in their behavior. This helps Amazon Personalize understand your users and their interests. For information about the item data you can import, see Items dataset.

Properties and hyperparameters

The Next-Best-Action recipe doesn't support hyperparameter optimization. The Next-Best-Action recipe has the following properties:

• Name – aws-next-best-action

• Recipe Amazon Resource Name (ARN) – arn:aws:personalize:::recipe/aws-next-best-action

• Algorithm ARN – arn:aws:personalize:::algorithm/aws-next-best-action

The following table describes the featurization hyperparameters for the aws-next-best-action recipe. A hyperparameter is an algorithm parameter that you can adjust to improve model performance. Featurization hyperparameters control how to filter the data to use in training.

The table also provides the following information for each hyperparameter:

• Range: [lower bound, upper bound]

• Value type: Integer, Continuous (float), Categorical (Boolean, list, string)

• HPO tunable: Whether the parameter can participate in HPO

Name	Description
Featurization hyperparameters
action_optimization_period	The window of time Amazon Personalize uses when predicting the next best actions for a user. For example, the actions the user will most likely take in the next 14 days. If you don’t have much action interaction data, specify a larger value. If you aren’t sure what value to specify, use the default. Default value: 14 Range: [7, 28] Value type: Integer HPO tunable: No