Assessing the Applicability of Fine-Tuning LargeLanguage Models for Designing and Deploying 24/7 Context-Aware Multichannel CRM

Abstract Modern Customer Relationship Management (CRM) systems must provide continuous, personalized support across diverse communication channels, including voice, text, and audio platforms. Traditional CRM solutions suffer from fragmented customer experiences due to their optimization for single-channel interactions, leading to operational inefficiencies and customer dissatisfaction. This research addresses the challenge of achieving consistent and efficient multichannel interactions using fine-tuned Large Language Models (LLMs), specifically the LLaMA 3.1 model. The model was fine-tuned using Low-Rank Adaptation(LoRA) techniques on proprietary datasets sourced from Mic & Mac Solutions. The dataset underwent comprehensive collection, cleaning, and structuring processes to ensure suitability for training. Our approach integrates Speech-to-Text(STT) and Text-to-Speech (TTS) pipelines alongside API-driven communication to ensure seamless transitions between channels. Initial training with basic hyperparameters (single epoch, learning rate of 1e-3) achieved moderate performance with a final training loss of 0.7-0.8, but exhibited optimization instabilities evidenced by gradient norm spikes exceeding 4.0. Following systematic hyper-parameter optimization—including extended training to 10 epochs, reduced learning rate (1e-4), and cosine annealing scheduling—the system demonstrated significantly improved stability with gradient norms maintained below 1.5 and reduced final training loss to 0.3-0.4. Experimental evaluation indicates that the optimized fine-tuned model delivers context-aware and coherent responses across all communication modalities while maintaining 24/7 availability. The findings highlight the transformative potential of fine-tuned LLMs in unifying multi-channel CRM environments, demonstrating substantial improvements in training stability and performance metrics, thereby setting a new standard for continuous, consistent, and efficient customer engagement in contemporary business operations.