According to the Adobe documentation, merge policies are a feature of Real-Time CDP that can be used to determine how data will be prioritized and what data will be combined to create a unified view of a customer profile. Merge policies define the rules and logic for merging profile attributes from different data sources into a single profile. Merge policies can be either predefined or custom, and they can be applied at the dataset level or the tenant level. References: https://experienceleague.adobe.com/docs/experience-platform/profile/merge-policies/overview.html?lang=en#merge-policies-overview

According to the Adobe documentation, contract is a type of data usage label that indicates data that has contractual limitations on how it can be used, such as opt-out events or third-party data. Contract data can be excluded from segments used in off-site targeting by applying data usage policies that enforce data governance rules. Reference: https://experienceleague.adobe.com/docs/experience-platform/data-governance/dule/dule-overview.html?lang=en#dule-overview

According to the Adobe documentation 3 , identity value and namespace are two elements that must match in the data sources for the profile to merge. Identity value is the unique identifier for a customer, such as an email address or a phone number. Namespace is the type or source of the identity value, such as Email or Phone Number.  When two data sources have the same identity value and namespace, they can be stitched together to form a unified profile. References:  3   https://experienceleague.adobe.com/docs/experience-platform/profile/identity-service/overview.html?lang=en#identity-service-overview

According to the Adobe documentation, lookalike modelling is a use-case that involves creating segments of high-value customers who share similar characteristics or behaviors and using them as seeds to find new prospects who are likely to have high lifetime value (LTV). This use-case could help the financial services company acquire more profitable customers by targeting them with relevant offers. Similarly, reaching the new segment with an email campaign reminding openers to set up recurring deposits is a use-case that involves creating segments of customers who have recently opened an account and encouraging them to take actions that increase their loyalty and retention. This use-case could help the financial services company increase customer satisfaction and revenue by offering them incentives and benefits for setting up recurring deposits. Reference: https://experienceleague.adobe.com/docs/experience-platform/segmentation/use-cases/lookalike-modeling.html?lang=en https://experienceleague.adobe.com/docs/experience-platform/segmentation/use-cases/customer-reengagement.html?lang=en

According to the Adobe documentation, DULE labeling is a framework that allows applying data usage labels (DULs) to data fields based on their usage category (Descriptive, Underlying, Linked, or Extracted). DULs can be applied at the schema level, which means they will propagate to all datasets and fields that use that schema. Reference: https://experienceleague.adobe.com/docs/experience-platform/data-governance/dule/dule-overview.html?lang=en#dule-overview

According to the Adobe documentation, segmentMembership.status is a field that indicates the current status of a profile’s membership in a segment, such as Active, Expired, or Realized. This field must be selected when mapping attributes to a streaming profile export destination to include Active and Expired members in subsequent incremental exports. Reference: https://experienceleague.adobe.com/docs/experience-platform/destinations/catalog/profile-export.html?lang=en#mapping-attributes

According to the Adobe documentation, suppression is a use-case that involves creating segments of existing customers who have already purchased a product or service and excluding them from paid campaigns that are aimed at acquiring new customers. This use-case could help the retailer meet their business goal of increasing ROAS by reducing wasted ad spend on existing customers and focusing on potential customers who have not bought yet. Reference: https://experienceleague.adobe.com/docs/experience-platform/segmentation/use-cases/suppression.html?lang=en