What is machine learning?
In order to understand the future of automation & machine learning in Google Ads, we first must understand what it is. Put simply, machine learning is the use of computer systems which can learn and adapt by using algorithms and statistical models to make changes based on patterns in data. Machine learning is an integral tool that Google is using in other products such as Google Maps, Google Translate, Text to speech and more.
Current use of machine learning in Google Ads
Machine learning is being used by Google Ads. Below are a few instances of its implementation:
Responsive Search Ads
By utilising 15 headlines & 4 descriptions, Google learns which ad creative performs best for any search query. So people searching for the same thing might see different ads based on context.
Performance Max Ads
This ad type uses audiences in conjunction with creatives and ad copy. The use of Audiences and ad copy together give Google more data for machine learning which means that Google is able to find context more easily and also provide relevant ad copy to the user based on the data provided.
Smart Bidding
Smart bidding is a key feature that most advertisers use whether advertising on Google, Bing or any paid social media ads platform. Smart bidding allows Google to use machine learning to optimise your bids in order to reach your specified campaign goal.
Machine learning advantages
Trend identification and optimisation
Google Ads utilises machine learning in tools such as the keyword planner and the recommendation tab to analyse data and suggest changes based on the data gathered. This can be really useful when analysing trends and for making proactive decisions in regards to optimisation.
Automation
Since machine learning is automated, it can analyse, make predictions and suggest/implement changes from this data. This means that the advertiser can spend less time making smaller changes and focus more on the overarching strategy of the account. Automation is also more reactive in comparison to manual optimisation, meaning Google will analyse data more regularly than is typically humanly possible.
Handling complex data
Google’s algorithm analyses a lot of data & does so frequently. It would be improbable for a human to be able to analyse the volume of data that can be analysed by machine learning and to make changes from this data. This means that machine learning can make more accurate and informed decisions.
Machine learning challenges
Limited control
Automation and machine learning have faced a lot of scepticism. One of the most common concerns is that Google Ads lacks transparency. For example, if you are running responsive search ads, you cannot see the performance data for each individual asset. This means that advertisers have no other option but to trust the algorithm.
Time and data
Google requires time in order to gather enough data to suggest changes. This can be apparent in the first few weeks of a campaign going live. We tend to start to see an improvement in performance after the initial first few weeks of performance. It is common practice among advertisers to turn off conversion-optimised bidding in the first few weeks of a campaign going live as there is no conversion data for Google’s algorithm to analyse.
Lack of context
Google does have the ability to analyse complex sets of data however, it lacks information that is available to the advertiser, such as the profit margin of the products and industry-specific events. Since Google lacks this data in order to maximise performance you will have to make some manual changes to be sure that you are running your ads account as efficiently as possible.
How to overcome the challenges of automation
1. Time - Give Google time to gather enough data to suggest changes. You will need to avoid making changes too soon, as this can hinder Google’s efforts to gather data and suggest changes from the data.
2. Testing - Once enough data has been gathered, make sure to test different strategies and ad copy to see what works best. This can be done using different bidding strategies, ad copy, ROAS targets etc.
3. Expertise - Using your expertise and experience to make informed decisions will allow you to overcome automation challenges. There is a myth that automation means advertisers can do nothing but set up a campaign and let Google handle the rest; this is incorrect. You will need to test strategies consistently, report on performance, analysing Google’s recommendations, regular optimisation, and you will need to be there to make changes whenever there are issues.
Summary
In summary, Google Ads automation & machine learning can help us advertisers achieve better results and reach their marketing goals more effectively. However, it's important to understand that automation is not a one-size-fits-all solution and requires ongoing monitoring and optimisation to drive efficient results.
