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Input and output in Customer AI

The following document outlines the different required events, inputs, and outputs utilized in Customer AI.

Getting started getting-started

Here are the steps to build propensity models and identify target audiences for personalized marketing in Customer AI:

Here are example configurations for your first model. The example model, built in this document, uses a Customer AI model to predict who is likely to convert for a retail business in the next 30 days. The input dataset is an 蜜豆视频 Analytics dataset.

Data overview data-overview

The following sections outline the different required events, inputs, and outputs utilized in Customer AI.

Customer AI works by analyzing the following datasets to predict churn (when a customer is likely to stop using the product) or conversion (when a customer is likely to make a purchase) propensity scores:

You can add multiple datasets from different sources if each of the datasets share the same identity type (namespace) such as an ECID. For more information on adding multiple datasets, visit the Customer AI user guide.

The following table outlines some common terminology used in this document:

Customer AI input data customer-ai-input-data

For input datasets, like 蜜豆视频 Analytics and 蜜豆视频 Audience Manager, the respective source connectors directly map the events in these standard field groups (Commerce, Web, Application, and Search) by default during the connection process. The table below shows the event fields in the default standard field groups for Customer AI.

For more information on mapping 蜜豆视频 Analytics data or Audience Manager data, visit the Analytics field mappings or Audience Manager field mappings guide.

You can use Experience Event or Consumer Experience Event XDM schemas for input datasets that are not populated via one of the above connectors. Additional XDM field groups can be added during the schema creation process. The field groups can be provided by 蜜豆视频 like the standard field groups or a custom field group, which matches the data representation in the Platform.

Standard field groups used by Customer AI standard-events

Experience Events are used for determining various customer behaviors. Depending on how your data is structured, the event types listed below may not encompass all of your customer鈥檚 behaviors. It is up to you to determine what fields have the necessary data that is needed to identify web or other channel-specific user activity clearly and unambiguously. Depending on your prediction goal, the required fields that are needed can change.

Customer AI uses the events in these four standard field groups by default: Commerce, Web, Application, and Search. It is not necessary to have data for each event in the standard field groups listed below but certain events are required for certain scenarios. If you have any events in the standard field groups available, it is recommended that you include it in your schema. For example, if you wanted to create a Customer AI model for predicting purchase events, it is useful to have data from the Commerce and Web page details field groups.

To view a field group in the Platform UI, select the Schemas tab on the left-rail followed by selecting the Field groups tab.

Additionally, Customer AI can use subscription data to build better churn models. Subscription data is needed for each profile using the Subscription data type format. Most of the fields are optional, however, for an optimal churn model it is highly recommended that you provide data for as many fields as possible such as, startDate, endDate, and any other relevant details. Please reach out to your account team for additional support on this feature.

Adding custom events and profile attributes add-custom-events

If you have information you wish to include in addition to the default standard event fields used by Customer AI, you can use the custom event configuration to augment the data used by the model.

When to use custom events

Custom events are necessary when the datasets chosen in the dataset selection step contain none of the default event fields used by Customer AI. Customer AI needs information about at least one user behavior event other than the outcome.

Custom events are helpful for:

The best candidates for custom events are data that contain domain knowledge that may be predictive of the outcome. Some general examples of custom events include:

The following are a selection of industry-specific custom event examples:

Custom events must represent user-initiated actions in order to be selected. For example, 鈥淓mail Send鈥� is an action initiated by a marketer and not by the user, so it shouldn鈥檛 be used as a custom event.

Historical data

Customer AI requires historical data for model training. The required duration for data to exist within the system is determined by two key elements: the outcome window and eligible population.

By default, Customer AI looks for a user to have had activity in the last 45 days if no eligible population definition is provided during the application configuration. Additionally, Customer AI requires a minimum of 500 qualifying and 500 non-qualifying events (1000 total) from historical data based on a predicted goal definition.

The following examples demonstrate the use of a simple formula which helps you determine the minimum amount of data required. If you have more data than the minimum requirement, your model is likely to provide more accurate results. If you have less than the minimum amount required, the model will fail, as there is not enough data for model training.

Customer AI employs a survival model to estimate the probability of an event occurring at a given time and identify influencing factors, alongside supervised learning which defines positive and negative populations, and decision-based trees like lightgbm to generate a probability score.

Formula:

To decide the minimum required duration of data existing within the system:

** If there is more than one condition for defining the eligible population, the eligibility lookback window is the longest one.

Examples:

Although Customer AI requires a minimum period of time for the data to exist within the system, it also works best with recent data. By using more recent behavioral data, Customer AI is likely to yield a more accurate prediction of a user鈥檚 future behavior.

Customer AI output data customer-ai-output-data

Customer AI generates several attributes for individual profiles that are deemed eligible. There are two ways to consume the score (output) based on what you have provisioned. If you have a Real-time Customer Profile-enabled dataset, you can consume insights from Real-time Customer Profile in the Segment Builder. If you don鈥檛 have a Profile-enabled dataset, you can download the Customer AI output dataset available on the data lake.

You can find the output dataset in the Platform Datasets workspace. All Customer AI output datasets start with the name Customer AI Scores - NAME_OF_APP. Similarly, all Customer AI output schemas start with the name Customer AI Schema - Name_of_app.

The table below describes the various attributes found in the output of Customer AI:

Next steps next-steps

Once you prepare your data and ensure that all your credentials and schemas are in place, refer to the Configure a Customer AI Instance guide, which walks you through a step-by-step tutorial to create a Customer AI instance.