WooCommerce Product Recommendations (the official Woo.com extension) runs rule-based and amplifier engines for cross-sells, frequently-bought-together blocks, recently-viewed and more. Each engine logs impressions and clicks per location, and attributed orders carry the engine name in order meta. The plugin's analytics screen lists engines row by row with summary numbers, which is right for a glance and wrong for the recurring question of which engines actually earn their cart slot.

SleekView reads those tables and renders Engine, Location, Impressions, Clicks, CTR and Attributed revenue as sortable, filterable columns. Filter to engines below a CTR threshold to retire the dead weight. Sort by attributed revenue descending for the portfolio view. Group by location to compare cart, checkout and product-page engagement on the same grid.

The plugin keeps owning the rules, the amplifier and the rendering of recommendation blocks. The table view owns the audit surface, so the impression, click and attribution data the plugin already writes stops hiding inside a row-by-row summary and becomes a portfolio dashboard CRO and merchandising teams can actually open.

Product Recommendations runs several engines at once and cart real estate is finite, so each engine has to earn its slot. The plugin records the impressions, clicks and attributed revenue that should make that decision easy, but the default surface lists engines row by row without supporting comparison or retirement workflows. SleekView reads the same logs and renders engine, location, impressions, clicks, CTR and attributed revenue as a sortable, filterable portfolio grid.

Filters stack so retirement candidates, location audits and per-engine briefs become one-click views. The plugin keeps owning the rules and the amplifier; CRO and merchandising get the per-row surface the dataset always deserved.