A marketing team wants to create a segment of customers who have purchased a product in the last 30 days and have not engaged with any promotional emails. Which segmentation approach is most appropriate in Adobe Real-Time CDP?
For segments that require evaluating long-term historical behavior (e.g., a 30-day lookback window) and cross-referencing multiple data types like offline/online purchases and email engagement logs, Batch Segmentation is the most appropriate and common approach.
Batch segmentation is designed to scan the entire Real-Time Customer Profile store to identify profiles that meet specific criteria over a significant duration. While Streaming Segmentation is excellent for immediate actions (e.g., 'just clicked a link'), it has specific guardrails regarding lookback windows and complexity. Evaluating a lack of engagement (the 'NOT' condition for email engagement) across a 30-day period often involves processing large volumes of historical event data that are most efficiently handled by the daily batch jobs.
Option A is incorrect because Edge Segmentation is restricted to a subset of data available on the Edge Network and is intended for in-session personalization, not 30-day historical analysis. Option C is insufficient because it ignores the email engagement requirement and lookback depth. Option D is the 'old way' of working; Adobe Real-Time CDP is specifically designed to eliminate manual exports by centralizing this logic within the platform. Batch segmentation ensures that the audience is accurately calculated using the full breadth of the unified profile's history, providing a stable list for the marketing team's scheduled campaigns.
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