Smart Bidding Strategies in Google Ads

Today, automation is an essential and inevitable part of our daily business. Google is constantly improving its services by automating the processes so you can advertise your products more efficiently. For example, Smart Campaigns or Smart Bidding in Google Ads. In previous blogs, we have observed the first area. Now we will have a closer look at how to work with Google Ads Smart Bidding and what strategies it offers you.

So… What is Smart Bidding Strategy?

In short. Smart Bidding uses artificial intelligence to optimize your advertising bids. Based on historical data and many parameters, it predicts how different settings will affect your ad. They can undoubtedly save time and improve campaign performance, which will also have an impact on your return on investment.

If you would like to learn more about smart bidding, you can do so directly in Google Ads Help. We will now look at how they work in specific strategies.

Smart Bidding Strategies

  1. Maximize conversions

To test automation, start with Maximize conversions, which requires the least historical data. This strategy sets bids to get you as many conversions as possible within your budget.

If your goal is to get as many conversions as possible (at any price), switch from manual CPC to Conversions and choose Maximize conversion. Do not forget to check your daily budget (it cannot be shared!) as the strategy will try to run it out.

For optimal settings, machine learning may take approximately 2-3 weeks.

  1. Target cost-per-action (CPA)

This strategy adjusts bids to a set target cost-per-action (e.g., conversion), an average value. Based on historical data, it evaluates contextual signals and finds the optimal bid for your ad whenever it is eligible to display.

The set upper limit may affect the effectiveness of this strategy. If your CPA is too low, it may cause you to forgo clicks and lose conversions. Although Google believes that this strategy does not need initial data, we recommend setting it up if you have achieved at least 30 conversions in the last 30 days. Google can then use this data for the recommended value. You can initially set a CPA 10-30% higher and then lower it according to the results.

The same CPA should be set for campaigns with the same or similar value of orders. If multiple campaigns are similar, you can share your CPA settings, but think carefully about your advertising structure.

  1. Target Return on Ad Spend (ROAS)

With this strategy, bids will be adjusted to deliver more valuable conversions and higher revenue for the ROAS you set. Basically, ROAS is calculated as the ratio of revenue to advertising spend, multiplied by 100%:

€150 (revenue) ÷ €30 (ad investment) × 100% = 500% ROAS

This strategy requires data from previous weeks/months, ideally at least 50 conversions in the last 30 days. Based on this data, Google will recommend a ROAS upper limit. At the beginning it is recommended to work with the recommended target ROAS, respectively a number similar to the previous results. This can be optimized after a few weeks based on your results. However, you should not reduce it by more than 20-40%.

Unlike a target CPA, you don’t have to deal with the same value of orders. However, as with CPA, it is not recommended to set limits for they could limit automatic bid optimization.

Did you know that Target ROAS is very often used in Smart Shopping campaigns?

  1. Enhanced cost-per-click (ECPC)

If you want to continue with manual bidding, use Enhanced CPC. This strategy automates a manual click-through bid adjustment based on the likelihood of a conversion. Unlike Target CPA, it remains below the cost-per-click (CPC) value threshold.

If artificial Intelligence finds the opportunity that is most likely to convert, it will raise your CPC bid to the maximum set value. If the opportunity is not attractive enough, it will automatically lower the cost-per-click.

Smart Bidding: Further recommendations from us

These four smart bidding strategies in Google Ads can help you get your products as close to potential customers as possible. Be sure to try them and experiment. Do not be discouraged by the potential initial adverse results. Machine learning also needs its time to gather the necessary to calculate with. But surely, at the end of the day it saves you a lot of time that you can spend on the creative page of your advertising or devising other strategies.

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About the Author:

Account Manager and aspiring PPC specialist at BlueWinston. I like to learn about newest information about ecommerce business and Google Ads.