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.
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.
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:
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, Adobe Analytics, and Adobe Audience Manager 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.
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.
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.
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.
If you have additional information in addition to the standard event fields used by Customer AI to generate propensity scores, a custom events option is provided. If the dataset you selected includes custom events defined in your schema, you can add them to your instance.
To add a custom event, select Add custom event. Next, input a custom event name then map it to the event field in your schema. Custom event names are displayed in place of the fields value when looking at influential factors and other insights. This means user id’s, reservation id’s, device info, and other custom values are listed with the custom event name instead of the ID/value of the event. These additional custom events are used by Customer AI to improve the quality of your model and provide more accurate results.
Next, select the operator you wish to use from the available operators drop-down. Only operators compatible with the event are listed.
Lastly, enter the field value(s) if the operator selected requires one. In this example, we only need to see if a hotel or restaurant reservation exists. However, if we wanted to be more exact we could use the equals operator and enter an exact value in the value prompt.
Once complete, select Next in the top-right to continue.
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.
If your dataset contained any columns added as test data, you can add that column or event to an exclusion list by selecting Add Exclusion followed by entering the field you wish to exclude. This prevents events that meet certain conditions from being evaluated when generating scores. This feature can be used to filter out irrelevant data inputs or certain promotions.
To exclude an event, select Add exclusion and define the event. To remove an exclusion, select the ellipses (…) to the top-right of the event container, 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.
By following this tutorial, you have successfully configured an instance of Customer AI and generated propensity scores. You can now choose to use the Segment builder to create customer segments with predicted scores or discover insights with Customer AI.
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.