The most likely explanation for the dramatic drop in segment size is that the majority of browsers on the website are not authenticated. This means that they do not have a loyalty account or a unique identifier that can be matched with the gender attribute from the loyalty system. Therefore, they are excluded from the segment that ANDs the gender attribute with the browsing behavior. To increase the segment size, the retailer could use other methods to identify or infer the gender of anonymous browsers, such as cookies, device IDs, or third-party data.

According to the Adobe documentation3, 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. Reference: 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, profile data is a type of data label that indicates data that describes characteristics or traits of an individual or group, such as demographic information, preferences, or interests. Geographic data can be categorized as profile data, as it describes the location of an individual or group. Reference: https://experienceleague.adobe.com/docs/experience-platform/data-governance/labels/data-labels.html?lang=en#data-labels

According to the Adobe documentation, primary identity is an identity that must be set in a schema in order to use it in Real-Time Customer Profile. Primary identity is an identity that uniquely identifies each profile within a dataset and across datasets within a tenant. Primary identity can be either predefined or custom, but it must have cardinality of one-to-one or many-to-one. Reference: https://experienceleague.adobe.com/docs/experience-platform/profile/identity-service/schema.html?lang=en#primary-identity