As AI capabilities and quality of first-party data become more refined, targeting tactics are increasingly able to produce worthwhile results. With the rise of signal marketing and optimizing on the fly to refine outcomes, advertisers have choices when it comes to strategies, and two that rise to the top are broad targeting and lookalike targeting.
Whether looking to scale awareness or grow successful segments, each targeting tactic has nuanced considerations, but each could improve results for efficient campaigns. Here’s what to know about both approaches.
Broad Targeting
What Is Broad Targeting?
Broad targeting is an audience targeting tactic where the algorithm figures out the best audience for an ad. With minimal constraints, the system tests, iterates, and learns what works based on engagement and, ultimately, conversions. Simply put, you provide the goal for which to optimize, and the platform finds the audience.
How It Works
Broad targeting works by bringing together clear campaign objectives and high-quality creative to test from a large pool of users. It refines outcomes and keeps testing different creative on different audiences to see which approach produces optimal results.
Benefits
One benefit of broad targeting is that the largest pool of prospective audiences is tapped, maximizing ad dollars early and lowering CPC and CPM, while the algorithm learns and adjusts. Another bonus is that minimal human intervention is needed for this approach once a campaign launches, since parameters are set and a large pool of creative loaded early on.
Since little identifiable information is put into the platform to begin with, broad marketing relies less on specific identifiers and demographic information, making it a more attractive option for privacy compliance.
Considerations
While broad targeting is generally an efficient campaign tactic, it takes some learning to refine the approach. This can result in inefficient spend and conversions early in the campaign, until the algorithm learns what works. In this phase, stepping back and waiting for results is important: Starting with solid creative to test and refine, then stepping back and letting the tool refine its audience, is key.
Use Cases
Broad targeting is a good tactic when you’re promoting mass-market products, such as e-commerce, or in a learning or testing phase (for example, looking to identify new, high-converting audiences, or ahead of a larger campaign push).
Lookalike Targeting
What is Lookalike Targeting?
Lookalike targeting is the practice of building lists of potential customers similar to your existing ones, based on shared characteristics and behaviors. The thinking here is that marketing efforts will reach new users likely to convert, since they mimic your current customers.
How It Works
Establish lookalike targeting by defining an audience from first-party data (for example, customer list, CRM segment, or users/visitors), which serves as a seed list. The lookalike targeting tool will then compare this grouping against potential users likely to convert. Once identified, campaigns are run against the modeled audiences.
Benefits
Since lookalike audiences are based on people who have already converted, there is generally less guess work in figuring out the correct approach. Having been built based on an engaged audience, it often delivers better conversion rates and lower cost per acquisition than other advertising campaigns. With lookalike targeting using first-party data, it’s also a safe option as privacy concerns and regulations increase.
Considerations
Outcomes are dependent on seed information, so if that list is outdated, incomplete, or based on a small sample size, there might not be enough quality data to create a high-converting lookalike audience. With lookalike targeting using first-party data, there can be geographic and platform limitations, too, and should privacy laws change, lookalike targeting may need to be adjusted.
Use Cases
If you want to bring on new customers through a cost-effective method, lookalike targeting can be a smart way to expand reach, since it’s designed to mimic people who have already converted.
How Does Broad Targeting Compare to Lookalike Targeting?
| Feature | Broad Targeting | Lookalike Targeting |
| Privacy Compliance | Very high privacy compliance since this tactic relies on contextual data and behavioral signals | High, especially when started with compliant and consented first-party seed data |
| Campaign Goal | Best suited for discoverability and reach | Acquisition– and ROAS-focused |
| Setup Complexity | Low complexity since broad targeting learns and adjusts over time | More complex since a strong source audience is needed for better results |
| Audience Scalability | As scalable as budget and inventory allow | Limited to seed and market size |
| Immediate Performance | It takes time, as performance improves with learnings | More stable sooner, especially with high-quality seed |
| Long-Term Brand Lift | Creates additional brand awareness higher in the funnel | Limited to people similar to current converting customers |
| Cost Efficiency | Generally lower, and more aligned for high-volume conversion | Typically higher conversion rates, but needs a stronger seed list |
| Automated AI Integrations | Very commonly native to advertising platforms | Often integrated alongside advertising platforms |
| Brand Safety/Suitability | Relies on platform controls | Safer since it mimics a known audience |
| A/B Testing | Best suited for testing creative and messaging | Best suited for testing audience segments |
How to Decide When to Choose Broad Targeting
The goal of broad targeting is to focus on reach and awareness. It’s a wise choice when you have a consumer-facing product or are looking to reach a wide audience, want to lean into creating more awareness, or don’t have historical data or audience learnings.
How to Decide When to Choose Lookalike Targeting
The goal of lookalike targeting is to reach more of a specific type or makeup of audience. With quality data and a leaner budget, you’ll more likely reach people who are closer to converting.
Key Takeaways
Both broad targeting and lookalike targeting are first-party data- and privacy-resilient. The quality of data dictates success with lookalike targeting, while the success of broad targeting is based on high-quality and diverse ad creative from which to learn and adjust. Before beginning, take a look at what your own team can support (creative vs. data quality) and the goals for the campaigns, for realistic expectations and aligned outcomes.
Frequently Asked Questions (FAQs)
When launching a campaign with broad appeal (e.g., a mobile game or utility app), is it better to use a lookalike audience to guide the initial spend, or go broad and let the AI find my audience from scratch?
For broad-appeal campaigns, it’s recommended to start — and stay — broad, allowing AI to find audiences. These campaigns learn as they go and become more cost-conscious with spend, figuring out what works, and when to lower CPC over time.
I have no pixel history and a small daily spend; which method minimizes my risk?
With no pixel history and small daily spend, broad targeting is a better strategy, as lookalike targeting relies on higher-quality historical data, and could incur a higher cost to reach the intended audience. Broad targeting allows you to be more wise with spend, finding competitive pockets of opportunity.
My lookalike costs are rising; can I achieve the same ROAS with cheaper inventory?
For lookalike targeting alone, costs may increase as competition intensifies for the market size and audience. To account for a better return on ad spend, a blended advertising approach that includes both broad and lookalike targeting could help lower cost, keeping both the middle funnel and upper funnel healthy.
