Why Google’s Smart Shopping campaigns outperform.
The Bigger Boat has been optimising Google Shopping campaigns since they were called Google Product Ads, and before that, when they were known as Google Product Searches.
Here, we take a look at how Google Shopping has evolved and discuss our key learnings from years of working with it and running successful campaigns.
The introduction of Smart Shopping
We’ve seen Google’s Shopping ads evolve into what they are today, along with the rise of its machine learning-based ad format, Smart Shopping.
Google’s Smart Shopping format uses machine learning to optimise the performance of your campaign to achieve maximum conversion value at your given budget. It achieves this by using past campaign data to automate bidding, ad targeting and delivery.
Campaign-based machine learning isn’t new. However, it’s improved significantly during the last few years. The first versions of Google’s ROAS strategies used to be very hit and miss – even with a well-structured product feed.
In the past, we could achieve better results by manually optimising Shopping campaigns – tweaking bids, excluding search terms and grouping products, for example. Nowadays, for the most part, things have changed.
Moving forward with even better results We were one of the first agencies to test Google’s Smart Shopping format back in 2019. Since then, it has managed to outperform the majority of manual campaigns we had running at the time. And it’s become our preferred ad format.
There are now more variables to optimise a campaign – many of which we don’t get to see, or control, from within our ad accounts. Google has more data points on its audience than we ever will, which help to clarify the intent of somebody searching. Understanding the search terms input by a user, along with information about other sites they’ve visited, helps build a picture of their intent before they even see or click on your ad.
By understanding user intent, Google can more effectively target people who search and only serve your Shopping ads to a user who is deemed more likely to convert.
This isn’t to say the process of optimising a Shopping campaign is now fully automated. You still need to understand the fundamentals of what makes a campaign tick and when to take certain actions.
Pulling together campaign strategy Building a campaign from scratch, with a solid foundation and safely scaling to higher daily budgets, still requires manual input and technical know-how.
Your strategy should be to help train Google’s machine learning by building a well-structured product feed, containing as much data about your products as possible. The more data Google has to work with, the better it’s able to match your products to user searches.
Machine learning looks for patterns and trends in your campaign’s conversion data, so making sure your campaign runs consistently is important. Significant and frequent changes to your campaign budget can interrupt Google’s strategy and reset the campaign’s learning phase.
Making sure your campaign isn’t limited by budget or restricted by a very high ROAS target should mean Google has enough data to learn properly. Meanwhile, moving quickly to test new campaign formats and machine-learning strategies can give you a significant advantage over other advertisers and agencies that might be slow to implement.
It’s safe to say if you’re running Google Shopping campaigns and not leveraging Google’s machine-learning strategies, in the majority of cases, your campaigns will underperform.