To help you get started with audience creation, this article provides a few sample audience ideas along with key details. These examples are based on real-world use cases we've encountered over time and are designed using a sample dataset. While your data and use cases may differ, the intent is to spark inspiration and guide your thinking.
Keep in mind that all Audiences are built using Attributes as their foundation. If you're unfamiliar with the Attributes, please refer to the Attribute documentation or consult your organization's Audiences Solution Owner to identify the appropriate Data Layer to use.
Each sample audience results in a dataset that includes visitor IDs and associated dates, which can be synced with downstream systems such as DSPs.
Note: The audiences in this article are based on the Sample Attributes outlined in a separate article.
Brand Audiences
Common e-commerce website audiences that marketers need to have available center around product-related information, such as brands. This section walks through how an audience may be built to determine the visitors who interacted with a brand but did not purchase within the last seven days.
It is common for certain marketing activities, e.g. retargeting, related activities for a user to want to know what visitors interacted with the website in a certain way and haven't purchased recently.
In the sample below, there are two sets of attributes being used to create an audience:
- Brands
- E-Commerce Last 7 days
An Audience group named Brand Last 7 Days has been created, featuring an audience value called Sony Non-Purchasers. The group uses a logical AND condition to combine attributes, effectively capturing the following logic:
“Return all visitor IDs who visited any Sony product in the last 7 days but didn't make any purchase”
Retention - 90 Days, retention is a metadata value passed to downstream systems telling them how long the audience should be persisted.
Machine Learning Model Audiences
After a machine learning model is built, the results can be in various forms, such as a raw numeric score or labeled categories, depending on the type and purpose of the model. Attributes derived from these are either passing the data point as-is or providing some categorization and labeling of the raw scores. The audiences built from ML scores typically are bucketing the results, i.e., High, Medium, Low, and possibly adding some behavior-related attributes to further refine your audience segments, e.g., visitors that have not purchased in the last 30 days and have been labeled as a high propensity to purchase.
In the sample below, a purchase propensity machine learning model was built where the data scientist provided the users with a table of raw scores ranging from 0 to 1, and attributes were built to set thresholds to define 'high propensity to purchase' and 'low propensity to purchase'. These attributes are being to derive audiences with retention set at 14 days.
E-Commerce Audiences
Most e-commerce websites will keep cart and purchase-related audiences in their commonly available stock. Audiences like cart abandoners and repeat purchasers are typically desired.
In this example, we've built several e-commerce-related attributes, and we want to build an audience that had any type of e-commerce-related action yesterday. The keyword ANY in the right value allows the app to locate any IDs where there is an 'E Comm Last 7 Days' attribute.