Remarketing is a strategic approach in digital advertising that allows businesses to target users who have previously interacted with their website or mobile application. The foundation of this technique relies on a small snippet of code, such as the Google Ads tag or the Meta Pixel, placed across a site. While this standard tag implementation successfully initiates the process, it only captures the most basic information, primarily revolving around simple pageview events. Defining sophisticated audience segments requires moving beyond this default setup, as technical limitations and platform policies prevent the immediate creation of highly granular or sensitive user groups. This article explores the technical mechanisms and policy restrictions that dictate which remarketing audiences cannot be defined without advanced configuration.
The Baseline: Defining Default Audiences
When a standard remarketing tag is installed, it immediately collects data sufficient to build simple audience groups without further customization. This out-of-the-box functionality segments users primarily based on the page Uniform Resource Locator (URL) they visited. The most fundamental segment is the “All Website Visitors” list, which includes every user who triggers the base code within a specified lookback window.
Default segments can be created by including or excluding users who visited a specific page path or hostname. For instance, an advertiser can target users who landed on the `/contact-us` page but did not proceed to the `/thank-you` page. These definitions also incorporate time-based segmentation, allowing refinement based on the recency of the visit. This basic segmentation, however, only tracks that a user saw a page, not what they did on it.
Custom Parameters for Deep Behavioral Segmentation
Moving beyond simple URL-based segmentation requires implementing custom parameters, which are variables passed alongside the base tag event. These parameters segment users based on specific actions or attributes that are not inherently contained within the URL structure. A user’s engagement level, for example, is a valuable behavioral metric that cannot be captured by default.
To define an audience of highly engaged users, a custom parameter must be configured to pass data points such as scroll depth achieved or the exact time a user spent on a specific page. This requires custom JavaScript coding or using a tag management system like Google Tag Manager (GTM) to push these events into the data layer. The resulting audience might target only those who scrolled past 75% of a long-form article, signaling genuine interest.
Furthermore, businesses often maintain internal user classifications or membership tiers managed within their own backend systems. Segmenting users based on a loyalty program status or a tiered membership level requires passing these proprietary attributes as custom parameters. The platform cannot access this internal data unless the advertiser explicitly configures the tag to transmit a specific, non-URL-based value, such as `user_status: ‘gold_member’`.
Dynamic Remarketing Segments and Product Feeds
Dynamic remarketing requires audiences to be defined by custom parameters and by linking an external structured data feed to the advertising platform. These segments rely on enhanced e-commerce events and a catalog of products or services. A standard tag cannot, by default, identify the specific product ID a user viewed or added to their cart.
To define an audience of users who abandoned a shopping cart, the tag must be configured to fire an enhanced e-commerce event that includes the specific product IDs and values associated with the cart items. The required parameters, such as `ecomm_prodid` or `dynx_itemid`, must map precisely to the unique identifiers listed in the external product feed. This allows the advertising platform to segment the user and dynamically serve ads featuring the exact products they left behind.
Segments based on product viewers also depend entirely on this structured feed linkage. The platform must cross-reference the `product_category` parameter passed by the tag with the category defined in the feed. Without the correct event schema and the corresponding data feed, the platform cannot attribute the behavioral data to a specific product or service, making these granular segments impossible to create.
Offline Data and Customer Match Lists
Some of the most valuable remarketing audiences are derived entirely from external data sources, completely bypassing the website tracking tag. These audiences, known as Customer Match lists, are built from offline data collected through Customer Relationship Management (CRM) systems, point-of-sale (POS) terminals, or lead generation forms. A default website tag has no mechanism to collect the personally identifiable information required to build these lists.
Customer Match lists are defined by uploading a file containing hashed user data, typically email addresses or phone numbers, to the advertising platform. The platform then matches this data against its own user base to create a segment of identifiable users. This process is distinct because the audience definition is generated from a proprietary business system, not from on-site user activity tracked by the pixel.
The maintenance and definition of these lists require either a manual file upload process or a direct API integration between the company’s CRM and the advertising platform. This method allows businesses to target users based on their entire customer history, such as lifetime value or recent support interactions. Therefore, an audience of “High-Value Customers” or “Customers with Expiring Subscriptions” can only be defined by leveraging this external, offline data source.
Policy Restrictions and Sensitive Interest Categories
Regardless of technical capability, certain audience segments cannot be defined due to strict platform policies governing user privacy and sensitive interest categories. Advertising platforms like Google Ads and Meta prohibit targeting users based on data related to protected legal classes or sensitive personal information. This acts as a final barrier to audience definition.
The technical data might exist—a user may visit a webpage discussing a specific health condition—but defining a remarketing audience based on that page visit is strictly prohibited. Policies forbid the segmentation of users based on health status, financial status, sexual orientation, race, or religion. Attempting to create an audience list that implies knowledge of such a sensitive user characteristic will result in the list being rejected by the platform’s automated compliance review. This ensures that even with advanced tracking, advertisers cannot exploit highly personal data for targeted advertising purposes.