AI-Driven Marketing Analytics Capability and Business Performance: The Mediating Roles of Marketing Agility and Customer Experience

Budi  Raharjo; Rahayu Nugraheni; Rachmat  Setiawan; Budi  Santoso

doi:10.51903/h6r9p922

Authors

Budi Raharjo Universitas Sains dan Teknologi Komputer, Kota Semarang, Jawa Tengah, Indonesia https://orcid.org/0009-0005-3425-5479
Rahayu Nugraheni Universitas Sains dan Teknologi Komputer, Kota Semarang, Jawa Tengah, Indonesia https://orcid.org/0009-0009-7409-2343
Rachmat Setiawan Universitas Sains dan Teknologi Komputer, Kota Semarang, Jawa Tengah, Indonesia https://orcid.org/0009-0008-9211-5775
Budi Santoso Universitas Sains dan Teknologi Komputer, Semarang, Jawa Tengah, Indonesia

DOI:

https://doi.org/10.51903/h6r9p922

Keywords:

AI-Driven Marketing Analytics Capability, Business Performance, Customer Experience, Marketing Agility, PLS-SEM

Abstract

This study examines the relationship between AI-Driven Marketing Analytics Capability and Business Performance, with Marketing Agility and Customer Experience as mediating variables. The study adopts a quantitative explanatory design and applies Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. A simulated dataset of 180 observations was used to demonstrate the proposed model and hypothesis-testing procedures. The measurement model was assessed through indicator reliability, internal consistency reliability, convergent validity, and discriminant validity, while the structural model was evaluated using R², Adjusted R², Q². Bootstrapping with 10,000 subsamples was used to test direct and indirect effects. Within the simulated model, AI-Driven Marketing Analytics Capability shows positive and statistically significant modeled relationships with Business Performance, Marketing Agility, and Customer Experience. Marketing Agility and Customer Experience also show positive and statistically significant modeled relationships with Business Performance. Furthermore, both variables show statistically significant indirect relationships in the simulated model, indicating complementary mediation through organizational responsiveness and customer-oriented value creation. Within the simulated framework, these results illustrate how AI-driven marketing analytics may contribute to business performance when analytical insights are translated into agile marketing actions and improved customer experiences. Because the study uses simulated data, the findings should be considered methodological evidence rather than empirical evidence from actual organizations. Future research should validate the proposed relationships using primary data from real organizational settings.

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