A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it

TL;DR

This survey comprehensively reviews AI-driven proactive customer care, focusing on predictive maintenance from business impact to deployment, and provides practical use-cases, data sources, and step-wise methodologies for effective appliance maintenance.

Contribution

It offers a complete overview of AI-enabled predictive maintenance, including system components, deployment strategies, and practical use-case templates, unlike existing surveys that focus only on algorithms.

Findings

Provides exemplar predictive maintenance use-cases with public datasets.

Highlights the importance of a step-wise approach for accurate fault diagnosis.

Discusses data sources and deployment considerations for AI-driven customer care.

Abstract

In recent times, advances in artificial intelligence (AI) and IoT have enabled seamless and viable maintenance of appliances in home and building environments. Several studies have shown that AI has the potential to provide personalized customer support which could predict and avoid errors more reliably than ever before. In this paper, we have analyzed the various building blocks needed to enable a successful AI-driven predictive maintenance use-case. Unlike, existing surveys which mostly provide a deep dive into the recent AI algorithms for Predictive Maintenance (PdM), our survey provides the complete view; starting from business impact to recent technology advancements in algorithms as well as systems research and model deployment. Furthermore, we provide exemplar use-cases on predictive maintenance of appliances using publicly available data sets. Our survey can serve as a template needed to design a successful predictive maintenance use-case. Finally, we touch upon existing public data sources and provide a step-wise breakdown of an AI-driven proactive customer care (PCC) use-case, starting from generic anomaly detection to fault prediction and finally root-cause analysis. We highlight how such a step-wise approach can be advantageous for accurate model building and helpful for gaining insights into predictive maintenance of electromechanical appliances.