Dynamic Intent-Response Framework

Project Title: From Static Segments to Liquid Audiences: Solving the "Measurement Crisis" in Generative Marketing. Lead Researcher: Furkat Kasimov (Independent Researcher / Strategy Consultant) Location: London, UK (Remote collaboration welcome) The Premise: Traditional marketing relies on static demographic "buckets" (e.g., "Women, 25–34") that suffer from a "latency problem"—data is often obsolete before a campaign launches. With the rise of Large Language Models (LLMs), we can now generate "Segments of One," but this capability has outpaced our ability to measure or govern it, leading to a "Productivity Paradox" where increased volume leads to higher consumer fatigue. The Methodology: I am developing the Dynamic Intent-Response Framework (DIRF) to operationalize "Liquid Audiences"—segments that exist only for the duration of a specific intent signal. The framework consists of three phases: Signal Ingestion: Replacing database queries with real-time intent detection (e.g., "Nano-Segments" based on live behavior). Generative Assembly: Assembling unique messages in real-time using context, history, and behavioral constraints rather than pre-written templates. Immediate Dissolution: Removing users from the segment immediately after the intent window closes to prevent "marketing noise". The Research Gap (Where I need help): While the conceptual framework is in place, I am seeking collaborators to address the "Measurement Crisis" and Privacy/Trust thresholds. I am looking for PhD students or researchers to collaborate on: Attribution Modeling: How do we statistically attribute success when every single message is unique? We need to move beyond "A/B testing" toward measuring the quality of AI decisions. The Privacy Paradox: Investigating the "Uncanny Valley" of text. Where is the line between helpful relevance and "creepy" surveillance when using first-party behavioral signals?. Governance Protocols: Developing "Brand Safety Guardrails" for autonomous agents. How do we shift human oversight from approving content to approving guidelines?. Ideal Collaborator Profile: Background: Marketing Analytics, Data Science, Human-Computer Interaction (HCI), or Behavioral Economics. Interest: Generative AI, Algorithmic bias, Consumer Privacy, or Marketing Technology. Goal: Co-authorship on a paper targeting journals such as the Journal of Marketing Technology or conference proceedings on AI in Business. How to Apply: Please send a brief summary of your research background and why this framework interests you. I am specifically interested in partners who can assist with statistical modeling or experimental design regarding user trust.