Due to signal loss from cookie deprecation, no single model like MTA or MMM is sufficient. The new gold standard is using all available algorithms together in a machine learning framework, allowing them to influence each other for a more accurate ROI picture.
A common attribution error is assigning all sales to paid marketing activities. In reality, most brands have a strong "baseline"—sales that would occur even without marketing. Accurate measurement requires modeling this baseline first, then attributing only the incremental lift from campaigns.
The future of data analysis is conversational interfaces, but generic tools struggle. An AI must deeply understand the data's structure to be effective. Vertical-specific platforms (e.g., for marketing) have a huge advantage because they have pre-built connectors and an inherent understanding of the data model.
As companies grow from 30 to 200 people, they naturally become slower. A CEO's critical role is to rebuild the company's operating model, deliberately balancing bottom-up culture with top-down strategic planning to regain speed and ensure everyone is aligned.
To avoid the customization vs. scalability trap, SaaS companies should build a flexible, standard product that users never outgrow, like Lego or Notion. The only areas for customization should be at the edges: building any data source connector (ingestion) or data destination (egress) a client needs.
Cookie deprecation blinds ad platforms like Google and Meta to on-site conversion quality. Marketers can gain a significant performance edge by creating a feedback loop, pushing their attributed first-party data (like lifetime value and margins) back into the platforms' AI systems in near real-time.