Full Answer

Prediction requires patterns, and patterns require granularity. When Meta's Advantage+ algorithm decides which audiences to target, it evaluates conversion signals at the event level — not just who bought, but which events preceded the purchase and how confidently each event matches a real user profile. The same applies to Google's Performance Max and emerging AI tools that optimise bidding, creative, and audience selection.

Order summaries flatten this complexity. A WooCommerce order record contains a timestamp, a total, a list of products, and billing details. It contains no information about the browsing session, the attribution source, the cart modifications, or the checkout hesitation that preceded the final payment. Two orders with identical totals may represent completely different customer journeys — one an impulse buy from a social ad, the other a researched purchase from organic search. An AI model trained on order summaries treats them identically.

Event-level data preserves the differences. Each action — page_view, add_to_cart, begin_checkout, purchase — carries its own timestamp, session context, and attribution parameters. AI models trained on event sequences can identify that visitors who view three or more products before adding to cart have a 3x higher lifetime value than single-product impulse buyers. That insight is invisible in order summaries.

The practical requirement is a pipeline that captures events in real time at the application layer and stores them with full context in a warehouse like BigQuery, where AI tools can query the complete sequence rather than a compressed summary.