Building smarter digital content: a CRITIC – DEMATEL framework for leveraging large language model optimization in marketing

The study responds to address the practical problem faced by the digital marketers, content creators and digital business agencies on creating content, which is both human-readable and LLM-compatible. The present study identifies and analyses the key factors influencing content optimization for Large Language Models (LLMs) to develop a strategic framework for Large Language Model Optimization (LLMO) that aligns with modern search paradigms. This research employs a two-phases multi-criteria decision-making (MCDM) approach combining CRITIC (Criteria Importance Through Intercriteria Correlation) to determine factor weights, and DEMATEL (Decision-Making Trial and Evaluation Laboratory) to map causal relationships. A panel of 15 experts across three countries (India, UAE and USA) rated the influence of five identified factors. The study identifies five critical factors for LLMO: Retrieval Augmentation, Readability Enhancement, Content Quality Assurance, Filtering of Unsafe Content and User-Centric Content Design. Retrieval Augmentation and User-Centric Design emerged as key causal factors, while Readability and Content Quality acted as bridges or effects. Although factor weights were relatively balanced, the DEMATEL analysis revealed interdependencies highlighting the dynamic nature of LLMO. The results provide actionable guidance to digital marketing experts and agencies, content strategists, marketing heads and developers to structure web content that is both human-readable and LLM-compatible. The study offers insights to organizations on how they can enhance their digital visibility and authority in AI-powered search ecosystems. This study fills a critical gap by offering the first integrated CRITIC-DEMATEL framework for LLMO. It distinguishes LLMO from traditional SEO and offers a novel causal model to support the development of holistic, future-ready content strategies.