Algorithmic Marketing and Brand Agility: Evaluating Digital Transformation Outcomes in Service SMEs

• Abbas Ahsun

  Texas University

  Author

Service SMEs face mounting pressure to justify digital transformation investments, yet existing evaluation frameworks fail to isolate the specific contribution of algorithmic marketing capabilities to brand agility outcomes. This study addresses that gap by developing and validating a predictive framework that quantifies how algorithmic marketing maturity predicts brand agility in service SMEs, using a mixed-methods design combining survey data from 312 service SME managers with retrospective digital transformation records. A Random Forest classifier achieved 89.4% predictive accuracy in distinguishing high-agility from low-agility firms based on algorithmic marketing indicators, with real-time customer data integration and automated campaign adjustment emerging as the strongest predictors. The findings demonstrate that brand agility is not a downstream outcome of general digital adoption but a capability specifically enabled by algorithmic marketing infrastructures that compress decision cycles. The study contributes a replicable diagnostic framework for SME managers and extends dynamic capabilities theory by identifying algorithmic responsiveness as a distinct microfoundation of brand agility. Practical implications include a prioritized implementation sequence for resource-constrained service firms seeking measurable agility gains from digital investments.

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Copyright (c) 2026 Abbas Ahsun (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.