Abstract
Marketing mix modelling (MMM) remains a core technique for guiding budget allocation, yet its outputs are often difficult for non-technical planners to interpret and govern. At the same time, large language models (LLMs) offer new possibilities for translating complex model artefacts into narrative guidance, but raise concerns about hallucination, reproducibility, and alignment with model-risk governance. This thesis examines whether an open-source MMM framework can be engineered as a repro- ducible, governance-ready pipeline and then augmented with a tightly constrained LLM interpretive layer. The empirical setting is a multi-brand, multi-country retail portfolio with several years of digital marketing and outcome data across multiple online platforms. The MMM implementa- tion builds on Meta’s open-source Robynpackage and adds phase-gated execution, pre-registered acceptance gates, configuration-over-code design, and manifest-based artefact tracking so that models can be re-run and audited. On top of this stack, the thesis designs and implements an LLM interpretive layer that consumes a frozen “grounding bundle” of MMM artefacts via a command-line interface. The LLM is restricted to answering questions using these artefacts, governed by explicit refusal rules and provenance tagging. A technical evaluation based on a scripted set of command-line tests, executed by the researcher across the most reliable MMM models, assesses grounding fidelity, refusal behaviour, and alignment with the pre-registered governance gates. The contribution is twofold: (i) a concrete pattern for implementing a reproducible, phase-gated MMM pipeline using open-source tools; and (ii) a governance-aware LLM interpretive layer that can be evaluated technically and positioned for future user studies. The thesis concludes that LLMs can act as useful, controllable translators of MMM outputs when anchored to a strong governance substrate, while emphasising that broader user-centred evaluation and causal extensions remain important directions for future work.
Publication Date
12-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Sanjay Modak
Advisor/Committee Member
Ehsan Warriach
Recommended Citation
Bin Haider, Mohammad, "Assessing Large Language Models as an Interpretive Layer in Marketing Mix Modeling: Implications for Marketing Analytics" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12491
Campus
RIT Dubai
