Running Strategies on Search Terms
Geetanjali Tyagi avatar
Written by Geetanjali Tyagi
Updated over a week ago

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The strategies in the Rule Engine can be run on Search Terms for actions such as analyzing high-performing queries and adding them as new keywords or queries with high cost and no conversions to add as negative keywords, among others. 

You can choose to add queries as Negative Keywords or as positive keywords and set its new bids using a constant number, expression, or a single metric. 

Add New Keyword with Avg. CPC Bid

Add new keywords from search terms that are performing well.

  • Choose search terms that are performing well (e.g. queries with more than 2 conversions in the last 90 days) and that don't currently exist on your account as keywords, in order to avoid duplicates.

Set bids dynamically based on relevant metrics.

  • The minimum bid won't be lower than Avg. CPC maximum bid won't be higher than First Page CPC.

Later on, create an action to add the matching queries as keywords in the ad group, and define its new bids. In this case, we're setting the new bids as a standard metric: 

New Bid: Avg. CPC | Min. Bid: Avg. CPC | Max. Bid: First Page CPC

Attributes for Better Search Term Analysis

There are three attributes in Rule Engine available to help you analyze your search terms better before adding them as keywords -

  1. Search term same as Keyword - you can set a condition to check if the search term is the same as the matching keyword.

  2. Search term and keyword same order - this attribute analyzes if the order of the search term is the same as that of the Matching keyword. This checks for the exact order at this moment and doesn’t include synonyms, similar words, close variants. For example, the order for the search terms “eat cake” and the keyword “eating cake” will not be the same.

  3. Search term and keyword similarity percent - this attribute analyzes how similar are the search terms and their matching keywords. It does a strict search and finds the matching ratio by different letters. We calculate this percentage by counting the number of single-character changes required to make the search term and the keyword the same.

At this moment, the similarity of words, synonyms, close variants are not considered while calculating the similarity percentage. For example, the search term “selling pineapple” and the keyword “selling pineapples” will not have a 100% similarity index, as there is a difference of letter ‘s’ in the keyword.

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