Paper A

The AIMx framework: integrating marketing mix modeling, attribution, and AI-driven analytics for adaptive decision systems

Thi Phuong Lan Nguyen — 2026 — Future Business Journal

peer reviewed journal article  ·   ·  watchlist

Key findings

• When companies use marketing mix modeling, attribution tracking, and incrementality testing all together in one AI-driven system — instead of separately — they can make budget decisions faster and with less second-guessing when the market changes.
• The simulation suggests that fragmented marketing measurement (each tool running independently) leads to slower, less consistent budget adjustments compared to a unified system where the tools feed into each other.
• The paper argues that AI in marketing should not replace human judgment — instead, the best system combines algorithmic analysis with human oversight for strategy, ethics, and accountability.
• The authors propose that if many companies adopt this kind of integrated analytics approach, the overall market could become more stable — but this is a theoretical prediction, not something tested with real data.

Marketing implications

• If your company uses MMM, attribution reporting, and A/B or incrementality tests in separate silos, this paper gives you a conceptual argument — and a named framework — for consolidating them into one connected decision loop. It won't tell you exactly how to build it, but it can help you make the case to leadership.
• Next time you're reviewing your measurement stack, ask: are your MMM results actually feeding back into your attribution model, and is your incrementality testing changing how you run both? If not, this paper suggests you're probably making slower, noisier budget decisions than you need to.
• Keep humans in the loop on strategy and ethics even as you automate more measurement — the paper argues this isn't optional for the system to work well long-term.