A campaign can hit its target and still leave marketers wondering whether they paid more than they needed to. This episode examines what attribution leaves unanswered about the cost of a media buy, how to use that information while a campaign is running, and who checks the decisions as more buying becomes automated.
Welcome to Modern Marketing and Measurement. Spencer Potts, Co-Founder and CEO, Precise.ai, joins us to discuss how agencies and brands can get a closer view of what their media dollars are doing. Using a prefab house, a game of Go Fish, and a healthy suspicion of black boxes, Spencer explains his approach to evaluating buying decisions, giving traders more useful information, and keeping a record that clients, procurement teams, and CFOs can examine.
Five Key Talking Points
- Attribution does not explain the whole purchase. Spencer distinguishes assigning credit for an outcome from examining what each component of a media buy contributed relative to its cost. His focus is on the economics underneath the campaign results.
- The components matter, and so do their combinations. Audience segments, data providers, publishers, and other signals all play a part in a buying decision. Spencer uses a prefab-house analogy to explain why examining those parts together could reveal a more efficient arrangement.
- Campaign intelligence needs to arrive in time to act. Precise is designed to recommend changes while campaigns are running, with forecasts and confidence estimates. Spencer describes how actual results feed back into subsequent recommendations.
- Automation still needs accountability. Spencer discusses how traders and AI agents could use the same economic information to make decisions. He also explains why Precise records its own recommendations and outcomes for review.
- Shared learning requires permission and a way to recognize contribution. Spencer’s longer-term vision involves organizations learning from one another’s data while retaining control over what they share. His Go Fish analogy illustrates how finding a missing piece could improve a result and create value for the contributor.
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