How DTC Brands Can Diversify Ad Spend Beyond Social

For years, direct-to-consumer (DTC) brands have followed a familiar playbook: pour money into search and social, optimize for conversions, and scale what works. However, that formula is getting harder to sustain. Platform saturation, rising costs, and tighter privacy regulations are forcing brands to rethink their media strategies. Growth still requires performance, but now it also requires diversification.

To move forward, DTC marketers need to expand beyond the walled gardens and embrace a broader set of channels: programmatic display, connected TV (CTV), digital out-of-home (DOOH), and retail media networks (RMNs). Historically, these formats were considered out of reach — too complex, too expensive, or too fragmented. But that’s changing fast.

From Fragmentation to Flexibility

Until recently, accessing these “nonsocial” channels required deep expertise, enterprise-level spend, and a patchwork of tech platforms. If you weren’t spending $600K a month on Facebook, you couldn’t get a call back from many performance DSPs. Licenses were limited. Managed service was the only option, which meant paying for expensive humans and giving up control.

Artificial intelligence is changing that. It’s lowering the barrier to entry and making these channels accessible to smaller, more agile teams. Campaigns that once required a team of specialists can now be run through a single, intuitive platform. Upload your objectives and creative inputs, and the system can generate high-performing ads, launch campaigns across multiple formats, and optimize in real time.

Just as Facebook and Google once democratized access to performance marketing, AI is now doing the same for streaming TV, mobile, DOOH, and more.

Smarter Strategies for a Fragmented Landscape

Channel diversification alone isn’t enough. To drive efficient growth, brands also need to evolve how they plan, measure and optimize.

Audience-first planning is one key shift. Instead of starting with a budget allocation and picking channels, brands can now start with their audience. Upload first-party data, CRM lists, and social campaign results. AI-powered platforms can use that information to build data-driven media plans, test creative variations, and continuously improve based on real-world performance.

Cross-channel measurement is another essential piece. Marketers have long benefited from the closed-loop reporting of platforms like Facebook. Now, similar feedback loops can be replicated across the broader media ecosystem. Rather than logging into multiple dashboards, analyzing results manually, and reallocating spend reactively, AI can synthesize performance data across channels and optimize budgets in near-real time.

The New Performance Playbook

Search and social still matter, but they’re no longer enough. Today’s DTC brands need a more resilient, multichannel performance strategy — one that reduces dependence on a single platform and reaches customers at every stage of the journey.

The next phase of growth lies in the open web — not just as a branding tool, but as a true performance engine. The technology now exists to make that vision a reality, not just for enterprise brands with massive budgets but for any marketer focused on outcomes.

That’s the new performance playbook: an AI-driven outcomes machine for the open web.

Mike Hauptman is the founder and CEO of AdLib Media Group, an advertising software company offering customizable user interfaces and APIs for streamlined and automated cross-channel media buying.