Best practice in Google Ads optimisation – smart bidding strategies

Rob Laver | 30th November 2021 | Paid Media

The new best practice in Google Ads bid optimisation involves the use of ‘smart’ bidding strategies. Previously, a manual method was preferred, however, as advertisers become more familiar with the use of these strategies they are becoming the go-to for maximising conversion rates.

What are smart bidding strategies?

Smart strategies use machine learning algorithms to find users that are more likely to record a conversion compared to the average user. Using historical data, as well as a wide range of contextual signals, these strategies will optimise your bids in the real-time auction. However, it is crucial to choose the right strategy in line with your overall goal. 

The strategies are categorised into three areas, those that focus on; eCommerce, lead generation and brand awareness. eCommerce activity, including Shopping campaigns, would benefit from Target ROAS or Maximise Conversion Value. The first of which allows you to optimise towards your ideal return on ad spend, whereas the second and more aggressive option optimises towards higher conversion values. However, if your goal is lead generation then you may opt for Target CPA which optimises your ideal cost per lead or Maximise Conversions which looks to generate as many conversions as possible for your spend. 

Most advertisers would stop here, however, there are certain actions you can take to limit the amount of risk you take by opening up your campaigns to machine learning. 

  1. Reduce risk by customising portfolio bid strategies bespoke to each campaign 
  2. Use seasonality adjustments to account for significant changes to conversion rates or upcoming events 
  3. Use data exclusions to protect historical data from faulty tracking

Portfolio bid strategies were originally created for convenience to let you quickly apply a custom bid strategy to accounts as a whole. The flexibility it provides, however, allows you to customise parameters like reducing your maximum cost-per-click bespoke to each of your campaigns, which aren’t otherwise available in the interface. Strategies like Max Conversions have a tendency to run away with CPC’s so this method allows you to reduce your risk by capping CPC but still benefit from machine learning. 

Seasonality adjustments are a way to inform the strategy of large upcoming changes to conversion rates like Black Friday, keeping in mind that data is key to performance. This will convey to the algorithm that this uptake isn’t a one-off and therefore shouldn’t be overly aggressive. Again, if this wasn’t in place then you could easily see an increase in spend and CPC.

Google has recently announced that data-driven attribution is set to become the default model, therefore increasing the importance of historical data. Data exclusions allow you to exclude short periods of time from the strategy to protect the conversion data you have previously collected. 

Google Ads continues to move down the route of smart, or responsive, advertising, therefore it becomes increasingly important to understand how we can control Ads campaigns while Google slowly removes that control. 

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