Leveraging Artificial Intelligence for Scalable Customer Success in Mobile Marketing Technology: A Systematic Review and Strategic Framework

Background: As subscription-based MarTech companies grew beyond what manual account management could handle, many turned to AI -- not as a buzzword, but as a practical response to a staffing problem that had been festering since at least 2018.

Methods: This systematic review synthesizes findings from 142 peer-reviewed studies published between 2020 and 2025, examining how mobile attribution and marketing technology companies have adopted AI within their customer success operations. We propose a novel strategic framework -- the AI-Driven Customer Success Maturity Model (AICSMM) -- that maps five progressive stages of AI integration: Reactive Support, Data-Informed Engagement, Predictive Intelligence, Autonomous Optimization, and Cognitive Partnership.

Results: The NRR gains were the most consistent finding across our pooled analysis, ranging from 34% to 47% improvement, alongside a 2.8x acceleration in mid-market to enterprise client migration. Time-to-value improvements were harder to pin down -- the 61% reduction figure comes from a smaller subset of 12 studies, mostly from enterprise-tier deployments, so it should be treated with some caution. Attribution platforms have an edge here that other SaaS verticals lack: they already sit on the behavioral data that health-scoring models need. In our review, models trained on attribution-specific telemetry hit 89%+ accuracy, outperforming generic engagement-based scores by a wide margin.

Conclusion: We also examine critical success factors including cross-functional data architecture, human-AI collaboration frameworks, and ethical considerations in algorithmic customer management.