Discovery Auto

The Discovery Auto strategic objective helps to uncover new search terms from Auto Campaign efficiently.

Navigation: Automation > Auto Campaign

Ads Type: Auto Campaign.

Automation Details

• [Dayparting] Enables ad from 6 am to 9 pm.
• [Bidding] Adjusts targeting clause bids to achieve 20% ACOS.
• [Negative] Aggregates search terms across Sponsored Products ads, adds search terms with Low CTR and 0 orders as Negative Exact keywords.
• [Negative] Aggregates search terms across Sponsored Products ads, adds search terms with >= 20 clicks and 0 orders as Negative Exact keywords.
• [Negative] Aggregates search terms across Sponsored Products ads, adds product targets search terms with >= 5 clicks and 0 orders as Negative targets.
• [Negative] Aggregates search terms within own campaign, adds search terms with >= 10 clicks and 0 orders as Negative Exact keywords.
• [Negative] Aggregates search terms within own campaign, adds search terms with <= 5% conversion and 60% ACOS as Negative Exact keywords.
• [Negative Word] Aggregate and split search terms across Sponsored Products ads into individual words, add words with >= 25 clicks with 0 orders as Negative Phrase keywords.
• [Blacklist] Adds blacklisted keywords as Negative Exact/Phrase.

Considerations: If there are import rules in other ads that harvest search terms with sales, consider adding a Negative rule for search terms with sales.

Notes

• Search terms may perform differently in different campaigns due to different placements.
• A search term in an auto campaign may perform poorly due to poor placements, while the same search term may do better with better placements in another campaign.
• Some Negative rules aggregate performance across multiple ads to find poor-performing search terms regardless of placements, enabling it to make negation decisions quicker with more data.
• To negate poor-performing search terms within its campaign due to bad placements, some Negative rules only aggregate performance within its campaign so that it can isolate differences due to placements and optimize performance locally.