In a continuation of our blog series on Google Ads smart bidding and automated bid strategies we take a refreshed look at the current situation with these.
Google Ads marketers often choose ‘smart’ and ‘automated’ bidding solutions to make frequent bid optimisations using comprehensive data models. If done manually this can be difficult to get right due to the complex and dynamic nature of search auctions, as it can be like trying to hit a moving target.
To ‘help’ with this, Google provides ‘smart’ bidding and ‘automated’ bid strategies which set bids frequently during a day and use query-level data across the Ads account.
This provides significantly more data from which the bidding algorithms make predictive decisions, based upon keyword-level conversion data and other contextual signals that interact with each other to impact conversion rates.
– Uses Google’s machine learning technology to optimise for conversions across every ad auction.
– Offers these conversion and value-based bid strategies that help to meet business objectives of maximising conversions and conversion value:
Maximise conversions (with Target Cost-per-Acquisition setting)
This helps bid strategies by predicting the auction-time conversion rate before setting a bid.
Maximise conversion value (with Target Return On Advertising Spend setting)
This helps bid strategies by predicting the auction-time conversion value per click before setting a bid.
(Read about how in April 2021 Google Reorganised its Smart Bidding Strategies)
Automated Bid Strategies
Also offers awareness-based bid strategies that help to meet business objectives of maximising clicks or awareness.
Bids are adjusted up or down to ensure campaign budgets are reached while getting as many clicks as possible.
Target impression share
Help to show the ad in the Google search results in the optimal location based on the business’s goals.
Google promotes these bidding strategies as having these key benefits:
- Bid strategies that align to the business’s goals;
- True auction-time bid optimisation;
- Query-level performance modeling;
- Use a richer set of contextual signals;
- Algorithms that keep learning.
As with the implementation of any ‘automated’ or ‘Smart’ Google machine learning option, we highly recommend that you please get in touch with us first, as the apparent simplicity of such options often isn’t beneficial and they should be meticulously scrutinised for specific campaigns prior to proceeding or Google, rather then the businesses, can in fact be the beneficiary.