Testing Meta’s AI-powered Advantage+ targeting: What we learned and what to expect

If there is one thing Meta has been pushing on paid social practitioners over the past couple of years, it’s automation.

From automatically “enhancing” creative to fit the needs of the different placements (and people) on the platform to suggesting copy for your ads, Meta is making its ad platform as approachable as possible.

And that’s the goal. Not just to encourage digital laypersons to strike out solo and start spending ad dollars on it’s simple self-service platform but to make the ongoing tweaks necessary to keep the results flowing.

Their ambitions for turning ads manager into a paid social autopilot have reached a new altitude with AI-powered Advantage+ audience targeting.

A new addition to the ‘Advantage+’ suite

Advantage+ targeting is the latest in Meta’s suite of (rebranded) automation products aimed at helping advertisers achieve better results.

Advantage+ targeting joins older products like Advantage+ shopping and Advantage+ creative to automate many of the setup and optimization needs of campaigns. 

The goal is to achieve better results by “trusting the algorithm” and giving it the freedom and flexibility to make certain decisions without manual intervention – specifically: 

• What ads to serve.
• What creative to use.
• And now, what people to target.

When you see “Advantage+” on the Meta platform, you can easily replace that term with “Automated” and get the gist.

Dig deeper: Meta advertising: 5 best practices

How does Advantage+ targeting work?

Put simply, you’re letting Meta determine who should see your ads. You can (optional) provide “suggestions” on who to target. But Meta will use AI and performance data to determine who the right audience is.

If you would like to provide input, advertisers have two options: 

• Audience controls.
• Audience suggestions.

Audience controls set the must-have criteria for your campaigns. Advertisers can select geographic locations, minimum age, custom audiences (lists) to exclude, and language. These are requirements, and Meta will not ignore them.

Advantage+ audience suggestions can also be made. By selecting custom audiences, age ranges, genders and even detailed (interests and behaviors) targets, you can help point Meta’s AI in the right direction to get things started.

Anything in the Advantage+ section will be treated as a suggestion but should help with the initial testing.

How are Advantage+ audiences different?

Functionally, Advantage+ audiences are essentially the same as broad or open targeting.

With open targeting, you leave the detailed audience targeting blank (open) and give Meta the option to target anyone within the geo and age range you selected. 

This has been a viable targeting option on Meta for a few years, given the platform’s ability to react quickly based on data. With open targeting, you’re essentially telling Meta that you don’t care who sees your ads as long as you get the result you’re looking for.

Advantage+ targeting is largely the same. Instead of telling them who to target, you’re telling them who not to target, and letting the machine test its way into the ideal audience.

Fewer restrictions = better results. At least, in theory.

Testing Advantage+ audience

We approached one of our clients, a major B2B company, with a proposal to test Advantage+ audiences with the goal of reducing average CPLs. 

The company uses Meta lead generation forms to collect potential handraisers, who are then contacted by an internal sales team.

Meta is one of the company’s highest-volume channels, but quality is an ongoing issue. CPLs are cheaper than just about any other channel, but conversion rates pale in comparison to paid search, which is higher intent.

Our goal was to test Advantage+ audiences in as much of an optimal “real-world” scenario, not as a strict A/B test.

So, we added an Advantage+ audience to an existing campaign that contained an interest-based audience (based on the client’s target customer profile) and a website and engagement retargeting audience. 

Over the next week, we let Meta (through Campaign Budget Optimization) decide how much to spend on what audience, using only the goal of maximum leads as our KPI.

Advantage+ targeting test results

After a week’s worth of data and following the completion of the learning phase, performance patterns started to emerge. Here’s what we observed:

• Advantage+ targeting spent slightly more than our interest-based audience as a percentage of daily spend.
  • CBO allocated nearly 40% of the budget to Advantage+ vs. 33% to the Interest-based audience.
• The leads generated from Advantage+ were less expensive compared to both the interest-based prospecting audience and our retargeting audience.
  • Advantage+ leads drove a 20% lower CPL compared to interest targeting.
• Despite the less-expensive CPLs, Advantage+ leads did not convert to MQLs as efficiently as other audiences.
  • Cost per MQL for leads driven from Advantage+ targeting were nearly 2x higher than interest.

Efficiency at the expense of quality

The poorer post-lead conversion of the Advantage+ audience is sadly consistent with similar tests we’ve run for different clients.

Open targeting has also delivered poorer-performing B2B leads than even slightly targeted (but still large) audiences.

With its lack of business firmographic targeting, B2B lead generation is already a tough enough proposition on Meta.

And while Meta has clearly made big strides in reducing costs, if the lead quality issue isn’t seriously addressed, it will continue to lose ad revenue from B2B advertisers in favor of LinkedIn.

How to improve Meta lead quality

Being successful at driving quality B2B leads on Meta requires you to craft a strategy that exploits the platform’s advantages while protecting yourself from the inevitable poor-quality leads you’ll likely get. 

Resist the temptation to go too narrow

• Meta’s B2B targeting is pretty lackluster, especially on dimensions like job title and seniority.
• Continue testing broader audiences and use lead filtering techniques to help weed out unqualified leads.

Filter and qualify within the form

• Use lead filtering questions within the Meta lead form to automatically disqualify leads that don’t fit your ideal customer profile.
• It’s counterintuitive, but creating more friction in the conversion process can actually improve quality by weeding out low-effort submitters.
  • Ask more questions. Use manual inputs. Always use “high intent” lead forms.

Use Meta’s Conversions API whenever you can

• This allows advertisers to stop optimizing towards just the completion of a lead form and instead optimize towards behind-the-scenes quality metrics that really make a difference.

Meta’s Advantage+ targeting: Deliberate testing is key

Meta’s new Advantage+ targeting is a useful tool to help less experienced and/or lower intensity advertisers drive get better cost pers. But if lead and conversion quality is a big concern, you’ll need to be deliberate and careful with your testing.

Using Advantage+ targeting will likely get you cheaper upfront leads but with a lower likelihood for them to convert. But that doesn’t mean you shouldn’t test it. Instead, you must develop strategies to remove lower-quality leads before they are submitted.

On Meta, the front line of the quality battle needs to be the form itself, not the targeting. If you’re fighting against what makes Meta great, you’ll lose.