Published January 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Deep Learning Application in Sales Automation and Customer Experience Personalization in Small and MediumSized Business: A Hybrid Approach Using Transformer-Based Large Language Models and Reinforcement Learning

Authors/Creators

  • 1. Darden School of Business, University of Virginia, Virginia, USA.

Contributors

  • 1. Darden School of Business, University of Virginia, Virginia, USA.
  • 2. Edge Hill University, Lancashire, UK.
  • 3. Kenan-Flagler Business School, University of North-Carolina at Chapel-Hill, North Carolina, USA.

Description

Abstract: Due to limited resources and fragmented technological infrastructures, omnichannel small and mediumsized businesses (SMBs) often face challenges in automating sales processes and delivering personalized customer experiences. This paper proposes a hybrid AI framework that integrates transformer-based large language models (LLMs) and reinforcement learning (RL) to address these challenges effectively. By combining LLMs' natural language understanding capabilities with RL’s dynamic decision-making, the framework aims to optimize customer engagement and sales automation in SMB contexts. The research employs LLMs to analyze customer behavior and deliver real-time conversational assistance through an AI concierge. This system provides personalized product recommendations, navigates shoppers to checkout, and collects data for customer insights. RL enhances this functionality by optimizing decision-making policies, such as dynamic pricing and resource allocation, based on long-term reward structures [4]. Key methodologies include multi-task learning to handle diverse customer interactions and offline simulators like Pseudo Dyna-Q to reduce deployment risks. Simulations based on retail scenarios demonstrated significant improvements: customer satisfaction scores increased by 20%, sales efficiency rose by 15%, and average order values grew by 40%. These findings highlight the potential of hybrid AI frameworks to empower SMBs by delivering scalable, resourceefficient solutions tailored to their unique operational constraints. The study also addresses ethical considerations, including fairness and transparency, by incorporating fairness-aware RL and explainable AI techniques to mitigate biases and build trust [15]. These measures ensure that the system promotes equitable outcomes, maintains user autonomy, and adheres to data protection standards. This research bridges the technological gap for SMBs and contributes to the democratization of advanced AI tools, enabling smaller enterprises to compete in increasingly customer-centric markets. The findings provide a robust foundation for further exploration of scalable and ethical AI-driven solutions in sales automation and personalization.

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Additional details

Identifiers

DOI
10.35940/ijisme.A1321.13010125
EISSN
2319-6386

Dates

Accepted
2025-01-15
Manuscript received on 14 December 2024 | First Revised Manuscript received on 20 December 2024 | Second Revised Manuscript received on 28 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025.

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