Advize is an AI-powered performance marketing agency that defaults to broad targeting for DTC clients at scale while maintaining specific use cases for detailed targeting in testing and niche scenarios. This blog addresses the question that has defined DTC Meta advertising debate in 2026: should you run broad targeting or detailed interest-based targeting, and what does the evidence actually say?
Why the 2026 Meta Algorithm Has Made Broad Targeting the Default for Most DTC Brands
Meta's Andromeda update and the ongoing investment in algorithmic audience identification have fundamentally shifted the targeting question for DTC brands. In 2019 and 2020, interest-based targeting provided genuine signal differentiation: targeting people interested in yoga had a meaningfully different composition than targeting a broad audience because the algorithm lacked sufficient behavioral data to replicate the segmentation automatically. By 2026, Meta's algorithm has processed billions of conversion events and can identify which users are most likely to convert for a given product with more precision than most manually constructed interest stacks.
The ASC campaign type, which removes targeting controls and lets Meta's algorithm determine delivery, represented 62 percent of ecommerce conversion spend in 2026. This is a markedly significant shift that reflects where the industry's evidence has landed. Brands investing heavily in manual interest stacks and exclusions are spending account management time on a layer of control that the algorithm is increasingly better at than humans.
When Detailed Targeting Still Adds Value Over Broad
Detailed targeting retains value in three specific scenarios. First, niche products with enthusiast identifiers that the algorithm has limited conversion data to model. A product targeting competitive powerlifters, professional beekeepers, or competitive chess players has a highly specific audience whose conversion characteristics may not yet be well-captured in Meta's broad algorithmic models, which are calibrated against mainstream consumer product purchase patterns.
Second, new accounts with fewer than 50 conversion events per week. Meta's algorithm requires a minimum data threshold to exit the learning phase and optimise delivery effectively. Below this threshold, broad targeting produces highly variable and often poor performance because the algorithm is guessing rather than optimising. Detailed targeting at this stage imposes some human signal to constrain the algorithm's search space until sufficient conversion data accumulates.
Third, creative testing where controlling one variable at a time produces cleaner signal. When testing two creative concepts and the goal is to understand which concept is stronger rather than which audience responds to each concept, holding targeting constant by using a specific defined audience removes targeting as a confounding variable and produces cleaner creative performance signal.
How to Structure Your Meta Account for Broad-First Delivery in 2026
The recommended account structure for DTC brands at ₹5 lakh or more monthly Meta spend in 2026 is two campaigns: a testing campaign using a controlled, relatively specific audience for creative angle validation, and a scaling campaign using ASC or broad targeting to scale proven creative to the full eligible audience.
The testing campaign: use a defined audience with at least 5 million in potential reach to give the algorithm sufficient room while maintaining some targeting guidance. Turn off Advantage Plus Audience in this campaign to keep the audience relatively controlled for testing purposes.
The scaling campaign: use ASC or a broad audience with Advantage Plus enabled. This campaign receives the creatives validated in the testing campaign that have cleared the hook rate benchmark. Let the algorithm determine delivery across the full eligible audience rather than constraining it with interest stacks.
Do not run interest-based detailed targeting in the scaling campaign unless the product falls into one of the three niche scenarios above. The algorithm's audience identification is more accurate than most manually constructed interest stacks for mainstream consumer DTC products in 2026.
The Short Version
The 2026 evidence favors broad or algorithm-managed targeting for most DTC brands at scale. ASC campaigns represent 62 percent of ecommerce conversion spend on Meta and outperform manual setups by 22 percent on average. Detailed targeting retains value for niche products with identifiable enthusiast audiences, new accounts with fewer than 50 weekly conversions, and creative testing where audience control improves signal clarity. Structure accounts as two campaigns: testing with a controlled audience and scaling with ASC or broad targeting for proven creative.
Conclusion
The broad versus detailed targeting debate for Meta has been resolved by the data rather than by preference. The industry's collective evidence, reflected in the shift of 62 percent of ecommerce conversion spend to algorithm-managed campaigns, is the most reliable signal available on where Meta performance is actually coming from in 2026. Advize builds account structures around this evidence while maintaining the specific use cases where human targeting guidance still adds value over algorithmic optimisation.