Best Practices for Advantage+ Shopping Campaigns
Since the fallout from Cambridge Analytica and the initial iOS 14 updates that started a few years ago, Meta has lost much of its targeting capabilities. This has, in turn, forced Meta to improve their products and prove that their platform can continue driving results for marketers despite losses in data.
They have combated this in many ways, including introducing the Conversions API (CAPI) that was designed to create a more direct and reliable source for marketing data. In 2021, Meta expanded CAPI’s capabilities to include app events and offline conversion data. Now, the next iteration of providing value to an advertiser’s business comes in the form of a new campaign type: Advantage+ shopping campaigns.
What are Advantage+ shopping campaigns?
In August 2022, Meta announced a new campaign type that utilizes automation to help advertisers increase sales and growth. Advantage+ shopping campaigns (ASCs for short) utilize machine learning to help advertisers automate the creative process and enable new ways to optimize social campaigns. These new machine learning models quickly uncover which creative combinations are working while optimizing against the set marketing budget.
What are the key differences between ASCs and traditional shopping campaigns?
There are three important distinctions between ASCs and traditional shopping campaigns.
AI machine learning
One of the main differences between Meta’s traditional shopping campaigns and ASCs are the AI models that the new technology utilizes. ASCs open the doors for advertisers by supplying new ways to optimize campaigns via machine learning. ASCs can incorporate up to 150 combinations of creative into a single campaign — a huge jump from the previous 50 combination limit — and include static single image and carousel ad options, both of which are unavailable in traditional shopping campaigns. With more creative options available, ASCs can automatically adjust creative for each consumer who views the ad, selecting a creative asset that is most likely to elicit a click and/or conversion.
Defining campaign audiences
Another key difference is that ASCs have both prospecting and retargeting audiences included in one campaign. Traditionally, shopping campaigns have been broken out into two categories: dynamic retargeting and Dynamic Ads for Broad Audiences (DABA). ASC is different in that it combines an advertiser’s retargeting and prospecting audiences into the same campaign and uses machine learning to determine how budget is allocated between the two. While advertisers still have some control — specifically, they can set a percentage of budget allocated to existing customers — this feature is designed to achieve greater reach, optimize campaigns toward the most qualified customers, and reduce audience saturation. Meta believes that by letting its algorithm decide when to serve ads to various audiences, it will help reduce the frequency with which users in retargeting audiences are served ads.
Broadening reach and targeting
Another difference between ASCs and traditional shopping campaigns is that the newer campaign type is designed to drive broader reach and use broad targeting. As the campaign learns, it will identify users who are not in the targeted audiences but have similar behavior to those users. Originally, the campaigns would target entire countries while traditional shopping campaigns could target more granularly (states, cities, postal codes, etc.). However, Meta shifted to requiring the campaign to serve nationally to assist in data gathering and ensure the audiences within the campaign are broad enough for advertisers to expand their reach and increase campaign efficiency.
Will testing ASCs help improve performance?
For many ecommerce and retail advertisers looking to increase online sales, this solution has delivered impressive results. Meta has stated that, on average, advertisers who have utilized ASCs have seen a 17% reduction in cost per acquisition (CPA) and a 32% lift in return on ad spend (ROAS) when compared to traditional shopping campaigns.
Meta has also released numerous case studies from different advertisers in the retail and ecommerce spaces who have seen a similar increase in performance. Shaheer Usmani, Head of Marketing at Azadea Group, said “Meta’s new Advantage+ shopping campaigns are an absolute game changer. We saw a reduction in cost per conversion of 52%, which led to an immediate decision to use Advantage+ shopping campaigns in all markets. We have been able to scale spends across all markets while continuing to lower our costs per action.”
While these initial results are promising, they don’t explore how a test should be set up and measured for success. To help, Meta provided guidance on initial test setup and how marketers can measure success of an ASC through a conversion lift study.
Specifically, Meta recommends running a two-cell conversion lift study for two-to-four weeks to measure if adding an ASC to an advertiser’s media mix has an incremental impact on performance. For example, a marketer would have their business-as-usual (BAU) shopping campaign testing against an ASC. Their BAU campaigns and Advantage+ shopping campaign that would have 30% of the existing shopping campaign budget allocated to it. With overall budget and targeting being the same, at the end of the test, the advertiser would have conclusive results to see if the ASC drove a lift by measuring cost per conversion, ROAS, and conversion rates between the two cells.
So, what does it all mean?
In recent years, advertisers have seen many platforms lean into automation and machine learning. As many long-standing methods of collecting and utilizing data have now become obsolete or non-existent, marketers have had to rely on advertising platforms’ algorithms to help identify and reach their customers. ASCs are Meta’s approach to helping advertisers navigate this landscape and improve the performance of their marketing campaigns. While it is natural for marketers to want as much control over their advertising budget as possible, consolidation and automation continue to be the directions that Meta and other ad platforms are going. Similar updates on other platforms like Google have been made in recent years that have proven automation can help marketers drive better results.
Advertisers must increasingly use these tools to help optimize marketing budgets, especially as new laws and regulations surrounding consumer privacy have impacted the ability to hyper-target core customers. However, while advertisers need to continually adjust their strategies, it’s not a one-way street. Thanks to requests from their advertisers, Meta has recently announced two important updates to their Advantage+ shopping campaigns:
- First, as of May 1, 2023, advertisers will be allowed to add location exclusions to their ASCs that include states/provinces, DMAs, and postal codes. Previously, all ASCs had to run nationally, even for regional retailers and/or brands unable to ship products nationally
- Second, advertisers will now be able to increase the minimum age targeting in these campaigns from 18 (the previous default) to 25. This is critical, as many brands don’t focus on the 18–24 age demographic as their core audience, like a high-end furniture retailer
These updates are a clear indication that while Meta wants advertisers to test into these new campaigns, they also must listen to advertisers’ concerns over audience consolidation and adjust campaign functionality accordingly. With these new updates and the results many advertisers have seen from testing ASCs, the time has finally come for marketers to start testing them alongside traditional Meta campaigns to see if they can yield a lift in performance.
