AI-Powered VOC/SOV Monitoring and Reporting Automation Platform

Project Overview

This is an internal operational analytics platform built to automatically collect and analyze customer reactions (VOC) and share of voice (SOV) for both owned and competitor products using global community data from Reddit, enabling business teams to turn external market signals into actionable insights more quickly.

Previously, teams had to manually browse community posts and comments, select relevant discussions, summarize findings, and turn them into reports. This created a high level of repetitive work and also introduced inconsistency in analysis and reporting speed depending on the judgment of individual team members.

To address these inefficiencies, this project restructured the entire workflow into a single operational system covering data collection, AI analysis, curator review, and email report delivery. The key value of the project lies not in building a simple monitoring tool, but in designing a practical AX platform that reflects real organizational roles and working processes.

Challenges Addressed

VOC analysis based on global communities offers strong advantages for understanding market reactions in real time, but when operated manually, it places a heavy burden on working teams and limits operational efficiency.

Before this project, staff had to manually explore posts and comments, which resulted in repetitive work. In addition, the processes of data collection, analysis, review, and reporting were fragmented across different steps. There was also no strong structure for comparing and utilizing reactions to both owned and competitor products in an integrated way, making it difficult to accumulate insights consistently and operate the process as an ongoing system.

For this reason, the project aimed not only to automate data collection, but to transform community-based research work into an operational analytics and reporting process.

Expected Impact and Business Value

• Reduces repetitive community research work through automation

• Establishes a foundation for VOC/SOV analysis that enables side-by-side comparison of owned and competitor product reactions

• Improves issue identification and reporting speed through AI-generated summaries

• Provides a practical platform that can be used by both operational managers and working-level teams

• Creates a consistent end-to-end operating process from data collection to reporting