"Throughout the digital ad ecosystem today, AI tools are virtually everywhere-and for good reason. From data analysis and creative development to solving for identity loss, AI continues to unlock efficiencies, insights and opportunities for advertisers. Yet, despite these advancements, many advertisers still rely on manual or generic pre-built optimization models within demand side platforms (DSPs). This represents a major gap in AI adoption today."
"I believe bidding optimization is one of the most powerful AI use cases in the ad industry's tool kit, but it remains underutilized. Advertisers have long left the task of bidding strategy to DSPs, placing significant reliance on standard models designed for broad (rather than nuanced) campaign needs. However, AI has changed the game. Brands now have the means to BYOA-or "build your own algorithm"-and build custom bidding strategies that maximize efficiency, precision and performance according to specific needs."
AI tools now support data analysis, creative development and identity solutions across the digital ad ecosystem, unlocking efficiencies, insights and opportunities for advertisers. Many advertisers still depend on manual or generic pre-built optimization models within demand-side platforms, leaving bidding optimization underutilized. Brands can now build their own algorithms (BYOA) to create customized bidding strategies that maximize efficiency, precision and performance for specific campaign objectives. Standardized DSP models fail to address unique brand needs amid an unpredictable, fragmented ad marketplace, rising advertising costs, and evolving privacy regulations. Dynamic optimization adoption is growing as brands seek measurable gains in media performance.
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