As mentioned before, since we first had to build brand awareness, we chose broad match and broad match modified keywords to catch as much whale watching related traffic as possible. We included as many negative keywords as we could find to curb the number of irrelevant searches.
The ads were based on their previous campaign, competitors and tested over time to discover what performed best. Key performance indicators for ads were: The click through rate, the conversion rate, cost per conversion and conversion rate. We would take the two best performing ads within an ad group which we would then combine to test as a new ad copy.
(This was before Google changed how they match keywords with searcher intent)
When we found profitable keywords that converted and led to sales or phone calls to the business we made these keywords exact match as to increase the impression share for that specific keyword, improve the quality score and decrease the cost per click.
The keywords that were performing exceptionally well and removed them from the more general ad groups and placed them in single keyword ad groups to help improve the overall performance of these keywords.
When we received enough conversions we tested switching from manual bidding to maximize conversions as to let Google help increase the number of conversions. Machine learning doesn’t always perform better, but with the help of rules, it can free up your time to work on other campaigns.
