Research on the Impact of LinkedIn Business Account Data-Driven Operations on Brand Exposure of AI Startups —A Case Study of AristAI

Abstract

Addressing the core industry pain points of low conversion efficiency in conventional exposure models for low-budget AI startups and the lack of systematic methods for LinkedIn platform operations, this study takes the LinkedIn business account operation practice of AristAI as the core empirical case. Using a mixed-method approach, including case studies, data analysis, and comparative research, the study validates the operational model's effectiveness in achieving targeted brand exposure and commercial conversion for AI startups. It proposes and empirically examines a three-dimensional operational model of “content type–posting rhythm–data review”, and explores the plausible mechanisms through which data-driven operations may influence brand exposure and downstream conversion outcomes in AI startups. The research comprehensively adopts case study method, data analysis method and comparative research method. It realizes accurate statistics of content type proportion through Excel, constructs a "content - conversion" correlation analysis framework with Python, and comprehensively verifies the scientificity and effectiveness of the model by combining multi-dimensional data from LinkedIn background and in-depth research on target users. The results show that this three-dimensional operational model can achieve precise brand exposure and commercial value realization of AI startups at low cost, and has significant cross-industry reusability. It provides a standardized and replicable practical path for B2B social platform operations of small and medium-sized enterprises, filling the theoretical and practical gaps in LinkedIn refined operations for low-budget AI startups.