Configuring a Customer AI instance
Customer AI, as part of Intelligent Services enables you to generate custom propensity scores without having to worry about machine learning.
Intelligent Services provide Customer AI as a simple-to-use Adobe Sensei service that can be configured for different use cases. The following sections provide steps for configuring an instance of Customer AI.
Set up your instance
In the Platform UI, select Services in the left navigation. The Services browser appears and displays all available services at your disposal. In the container for Customer AI, select Open .
The Customer AI UI appears and displays all your service instances.
- You can find the Total profiles scored metric located in the bottom-right side of the Create instance container. This metric tracks the total number of profiles scored by Customer AI for the current calendar year including all sandbox environments and any deleted service instances.
Service instances can be edited, cloned, and deleted by using the controls on the right-hand side of the UI. To display these controls, select an instance from your existing Service instances . The controls contain the following:
- Edit : Selecting Edit allows you to modify an existing service instance. You can edit the name, description, and scoring frequency of the instance.
- Clone : Selecting Clone copies the currently selected service instance setup. You can then modify the workflow to make minor tweaks and rename it as a new instance.
- Delete : You can delete a service instance including any historical runs.
- Data source : A link to the dataset used by this instance.
- Last run details : This is only displayed when a run fails. Information on why the run failed, such as error codes are displayed here.
- Score definition : A quick overview of the goal you configured for this instance.
To create a new instance, select Create instance .
The instance creation workflow appears, starting on the Setup step.
Below is important information on values that you must provide the instance with:
- The instance's name is used in all places where Customer AI scores are displayed. Hence, names should describe what the prediction scores represent, for example, "Likelihood to cancel magazine subscription".
- The propensity type determines the intent of the score and metric polarity. You can either choose Churn or Conversion . Please see the note under scoring summary in the discovering insights document for more information on how the propensity type affects your instance.
- Data source is where the data is located. Dataset is the input dataset which is used to predict scores. By design, Customer AI uses Consumer Experience Event data to calculate propensity scores. When selecting a dataset from the dropdown selector, only ones that are compatible with Customer AI are listed.
- By default, propensity scores are generated for all profiles unless an eligible population is specified. You can specify an eligible population by defining conditions to include or exclude profiles based on events.
Provide the required values and then select Next .
Define a goal
The Define goal step appears and it provides an interactive environment for you to visually define a prediction goal. A goal is composed of one or more events, where each event's occurrence is based on the condition it holds. The objective of a Customer AI instance is to determine the likeliness of achieving its goal within a given time frame.
To create a goal, select Enter Field Name and select a field from the dropdown list. Select the second input and select a clause for the event's condition, then provide the target value to complete the event. Additional events can be configured by selecting Add event . Lastly, complete the goal by applying a prediction time frame in number of days, then select Next .
Will occur and will not occur
While defining your goal, you have the option to select Will occur or Will not occur . Selecting Will occur means that the event conditions you define need to be met for a customer's event data to be included in the insights UI.
For example, if you would like to set up an app to predict whether a customer will make a purchase, you can select Will occur followed by All of and then enter commerce.purchases.id and exists as the operator.
However, there may be cases when you are interested in predicting whether some event will not happen in a certain timeframe. To configure a goal with this option, select Will not occur from the top-level dropdown.
For example, if you are interested in predicting which customers become less engaged and do not visit your account login page in the next month. Select Will not occur followed by All of and then enter web.webInteraction.URL and equals as the operator with account-login as the value.
All of and any of
In some cases, you may want to predict whether a combination of events will occur and in other cases, you may want to predict the occurrence of any event from a pre-defined set. In order to predict whether a customer will have a combination of events, select the All of option from the second-level drop-down on the Define Goal page.
For example, you may want to predict whether a customer purchases a particular product. This prediction goal is defined by two conditions: a commerce.order.purchaseID exists and the productListItems.SKU equals some specific value.
In order to predict whether a customer will have any event from a given set, you can use the Any of option.
For example, you may want to predict whether a customer visits a certain URL or a web page with a particular name. This prediction goal is defined by two conditions: web.webPageDetails.URL starts with a particular value and web.webPageDetails.name starts with a particular value.
Configure a schedule (optional)
The Advanced step appears. This optional step allows you to configure a schedule to automate prediction runs, define prediction exclusions to filter certain events, or select Finish if nothing is needed.
Setup a scoring schedule by configuring the Scoring Frequency . Automated prediction runs can be scheduled to run on either a weekly or a monthly basis.
Below the schedule configuration, you have the ability to define prediction exclusions to prevent events that meet certain conditions from being evaluated when generating scores. This feature can be used to filter out irrelevant data inputs.
To exclude certain events, select Add exclusion and define the event in the same fashion as to how the goal is defined. To remove an exclusion, select the ellipses ( ... ) to the top-right of the event container and then select Remove Container .
Exclude events as needed and then select Finish to create the instance.
If the instance is created successfully, a prediction run is immediately triggered and subsequent runs execute according to your defined schedule.
Depending on the size of the input data, prediction runs can take up to 24 hours to complete.
By following this section, you have configured an instance of Customer AI and a prediction run was executed. Upon the run's successful completion, scored insights automatically populate profiles with predicted scores. Please wait up to 24 hours before continuing to the next section of this tutorial.
The following video is designed to support your understanding of the configuration workflow for Customer AI. Additionally, best practices and use case examples are provided.