Introducing Machine Learning Bid Suggestions in SearchAdsHQ

Managing bids is one of the key ways Apple Search Ads performance is optimized to drive qualified users. In order to make changes to keywords bids that will win auctions and deliver conversions and revenue, UA managers have to analyze multiple data on bids and performance, and make tedious manual calculations for each keyword in their accounts. 

SearchAdsHQ has taken first steps in letting the machines do the math for marketers on Apple Search Ads. We are introducing machine learning-based Bid Suggestions inside the platform to help you make more educated and effective bidding decisions and maximize your performance.

Overview of SearchAdsHQ Bid Suggestions for Apple Search Ads

Bids Suggestions in SearchAdsHQ are designed to predict effective keywords bids depending on the target metric that you set. Whether you optimize for Cost per Action or ROAS, bids are automatically calculated by our sophisticated machine learning algorithm not only to hit the target, but to maximize the performance of your Apple Search Ads campaigns.

How does it work? Let’s imagine that a $3 bid generates X conversions to hit Target CPA of $30.  But what if the same X number of conversions can be driven with a $2 bid with CPA at $25? 

Our machine learning algorithm is capable of predicting the outcomes of setting this or that bid. While doing so, the bid recommendation ensures competitiveness (chances of winning the auction at this bid value) as well as good performance (conversions and revenue). These components magnify the results while taking into consideration the set target KPI. 

With Bid Suggestions, SearchAdsHQ automatically calculates the best bid value based on the likelihood that your ad will result in revenue or conversion.

SearchAdsHQ Bid Suggestion Strategies 

Bid Suggestions are SearchAdsHQ’s first steps towards providing machine learning ground to optimize Apple Search Ads. We’re moving towards bid optimization strategies that set bids that get as many conversions or as much revenue as possible at the target metric.

For now we analyze keywords performance independently and offer bid recommendations for each separate keyword based on the chosen target metric and its value.    

Currently, there are two Bid Suggestions Strategies in SearchAdsHQ:

1 . Target Cost-per-Action (CPA)

Goal: receive the maximum number of conversion events from a keyword while meeting the specified target value.  

This Strategy will use historical data from your campaign to calculate the best bid for a specific keyword which would drive as many conversions as possible at your Target Cost-per-Action (CPA). 

For whom: it is suitable for those marketers on Apple Search Ads who know the average amount they want to spend each time a user completes the action, which can be:

• Cost-per-Install
• Cost-per-Trial
• Cost-per-Subscription
• Cost-per-Purchase
• Cost-per-Engagement
• Cost-per-Game Event
• Cost per Other Conversion

2 . Target Return on Advertising Spend (ROAS)

Goal: receive the maximum return on ad investment from a keyword while meeting the specified target ROAS.  

This Strategy will analyze your historical data to determine the best bid for a specific keyword that would maximize the conversion value or revenue at the specified Target ROAS. 

For whom: it is suitable for marketers on Apple Search Ads who focus their efforts on driving the highest value of conversions, not trying to receive the biggest number of conversions. 

Here are the metrics you can configure for the Target ROAS Strategy:

• ROAS
• ROAS D1
• ROAS D3
• ROAS D7
• ROAS D14
• ROAS D30

As you see, SearchAdsHQ Bid Suggestions Strategies account for both apps and games in terms of the metrics they commonly optimize Apple Search Ads campaigns for. Note that although the calculated bid values may be the same, bids are still unique to each auction. 

If you want to learn more about how to convert your business goals into bid values with SearchAdsHQ machine learning Bid Suggestions, reach out to us for more information.