Innovative Strategies for Scaling Service Businesses in the U.S.: From Local Service to a National Network

Vladislav Kislov

Citation: Vladislav Kislov, "Innovative Strategies for Scaling Service Businesses in the U.S.: From Local Service to a National Network", Universal Library of Engineering Technology, Volume 02, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study analyzes innovative strategies for scaling service businesses in the U.S. amid the digital transformation of the economy. It examines key methods, including franchising, AI automation, data-driven advertising, and logistics optimization, as tools for transitioning from a local service to a national network. Based on a literature review of academic research, empirical analysis, and case studies, the study finds that integrating modern digital technologies accelerates market expansion, enhances operational efficiency, and strengthens the competitiveness of service companies. The results indicate that the synergistic effect of a comprehensive application of innovative methods contributes to the development of sustainable business models capable of effectively responding to dynamic market conditions. The findings will be valuable for entrepreneurs, investors, and consultants interested in developing and implementing effective growth models for service companies in a highly competitive and technologically evolving environment. Additionally, the study will benefit economists and professionals in digital transformation and franchise management, offering insights into the adaptation of innovative technologies, marketing strategies, and operational efficiencies necessary for successfully expanding a local business to a national scale.


Keywords: Scaling, Service Business, Digital Technologies, Franchising, AI Automation, Data-Driven Advertising, Logistics Optimization.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0201009