What's a best practice when building an account structure designed to improve the performance of AI-powered solutions?

When building an account structure for AI-powered solutions, the primary goal is to provide the AI with sufficient data and a clear understanding of your business objectives to optimize effectively.

Analysis of Correct Answer(s)

• Focus account structure on business goals, not channel silos. This is the best practice because AI and machine learning models perform optimally with a broader dataset and clear, overarching business objectives (e.g., maximize conversions, drive revenue).
  • AI thrives on data consolidation: When campaigns are structured around specific business goals (e.g., a "lead generation" campaign, a "product sales" campaign), the AI receives a larger, more comprehensive pool of data across various dimensions (audiences, devices, placements, keywords). This allows the AI to identify patterns, make more informed predictions, and optimize across these factors more effectively.
  • Avoids artificial limitations: Structuring by "channel silos" (e.g., separate campaigns just for Google Search, separate for Google Display, separate for social media) can artificially segment data, preventing the AI from understanding the cross-channel impact and overall contribution to the business goal. Modern AI-driven solutions are designed to optimize holistically.
  • Simplified management: This approach often leads to a simpler, more manageable account structure, reducing complexity and allowing AI to leverage its full potential.

Analysis of Incorrect Options

• Segment account structure by device and match type to customize creative.

  • This approach leads to over-segmentation and data fragmentation. While historically useful for manual control, it hinders AI. AI-powered solutions are designed to automatically optimize across devices, match types, and ad creatives within broader campaigns. Creating separate campaigns for these dimensions fragments the data too much, making it harder for the AI to learn and find optimal solutions across a wider context. Modern platforms use responsive ad formats and automated bidding strategies that handle these variations within consolidated campaigns.

• Create as many campaigns as possible to achieve business goals.

  • This is generally a detrimental practice for AI-powered advertising. Creating too many small campaigns dilutes data signals and starves individual campaigns of the sufficient data volume needed for AI to learn effectively. AI models require a certain threshold of data (e.g., conversions, clicks) to make statistically significant decisions. Numerous small campaigns will each have less data, leading to less effective optimization and slower learning for the AI. It also significantly increases account complexity and management overhead.

• Design campaigns around specific manual optimization levers.

  • This approach undermines the core benefit of AI-powered solutions. AI is designed to automate, optimize, and often surpass manual optimization efforts. Building account structures specifically to enable manual control over individual levers (e.g., separate campaigns just to manage specific bid modifiers) forces the AI into a rigid framework and prevents it from leveraging its intelligence to find new, unexpected efficiencies or optimize beyond human capacity. The goal with AI is to give it a clear objective and enough flexibility to achieve it, not to constrain it with structures designed for manual intervention.