The Unified Support-Engineering Flywheel: Data, AI and Shared Metrics to Maximize CSAT

This article introduces the Unified Support-Engineering Flywheel, a framework integrating data engineering, artificial intelligence (AI), and shared key performance indicators (KPIs) to dismantle organizational silos between support and engineering teams. By unifying support metrics (First Contact Resolution [FCR], Resolution Time), engineering metrics (Mean Time to Repair [MTTR], Log Error Rates), and Customer Satisfaction (CSAT) within a data lakehouse/mesh architecture, organizations can leverage AI-driven root cause analysis to proactively address systemic customer experience (CX) issues. Case studies demonstrate 25–40% improvements in CSAT within 6 months of implementation. The flywheel’s self-reinforcing cycle—where shared data enables faster issue resolution, improving CX and generating higher-quality data—proves critical for aligning cross-functional goals and maximizing customer satisfaction.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2024-12-20
DOI
https://doi.org/10.32628/cseit25113368
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.35
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article

The Unified Support-Engineering Flywheel: Data, AI and Shared Metrics to Maximize CSAT

Mohit Garg
1 citations
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Manufacturing Process and Optimization
0.35
article

The Unified Support-Engineering Flywheel: Data, AI and Shared Metrics to Maximize CSAT

Mohit Garg
article en
1 citations

Abstract

This article introduces the Unified Support-Engineering Flywheel, a framework integrating data engineering, artificial intelligence (AI), and shared key performance indicators (KPIs) to dismantle organizational silos between support and engineering teams. By unifying support metrics (First Contact Resolution [FCR], Resolution Time), engineering metrics (Mean Time to Repair [MTTR], Log Error Rates), and Customer Satisfaction (CSAT) within a data lakehouse/mesh architecture, organizations can leverage AI-driven root cause analysis to proactively address systemic customer experience (CX) issues. Case studies demonstrate 25–40% improvements in CSAT within 6 months of implementation. The flywheel’s self-reinforcing cycle—where shared data enables faster issue resolution, improving CX and generating higher-quality data—proves critical for aligning cross-functional goals and maximizing customer satisfaction.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 10(6)
Pabna University of Science and Technology (BD)
Openalex Percentile: Top 30%
Manufacturing Process and Optimization
0.35
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The Unified Support-Engineering Flywheel: Data, AI and Shared Metrics to Maximize CSAT — Mohit Garg · International Journal of Scientific Research in Computer Science Engineering and Information Technology (2024) | TGRS Research Map | TGRS