Amazon Advertising: How Sponsored Products, Brands, And Display Ads Work
Targeting methods and keyword management across Sponsored Products, Brands, and Display
Keyword targeting is central for search-driven product ads and often uses match types—broad, phrase, and exact—to control relevancy and reach. Broad match can surface additional queries but may require more negative keyword management; phrase and exact match typically yield more focused traffic. Search term reports commonly help identify high-cost or low-converting queries that may be added as negatives. Separating campaigns by match type can isolate performance differences and allow different bid strategies by match type.
Product and category targeting may be used to place ads on specific ASIN pages or within defined product categories. This method can target competitor listings or complementary items and may be useful for reaching shoppers already viewing related products. Product-targeted campaigns often show different click-through and conversion dynamics than keyword-based search placements, so tracking performance by target type can clarify where ad spend is producing conversions versus impressions or clicks without downstream orders.
Audience and remarketing targeting are more common with display-style placements and can rely on behavioral or view-based segments. For example, targeting shoppers who viewed a particular product in the last X days may serve display placements aimed at reengaging those visitors. Audience targeting typically requires sufficient traffic volume to be actionable and may suit higher-funnel or retention-focused objectives rather than immediate conversion tasks. These targeting approaches usually come with thresholds for segment size before active targeting can be applied.
Keyword and target maintenance are ongoing activities that often include routine search term analysis, negative keyword updates, and bid adjustments. Regular review cycles—weekly or biweekly depending on traffic—may help identify inefficient spend and new converting queries. Automated rules and scripts can assist with scaling routine changes, though manual review is often necessary for nuanced decisions, such as assessing creative relevance or catalog changes that affect targeting performance.