Log Analysis

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toss3 min readCurated summary

How Do TAMs Solve Problems in Fast-Moving Organizations?

TAM CONNECT 2025 brought together Technical Account Managers from Toss and Kakao Pay to compare how they connect technology, business, customers, and internal teams. Although their organizations differ, they face similar challenges: operational complexity, cross-team coordination, recurring incidents, and the need to improve customer experience. The event framed TAMs not as basic support staff, but as technology-driven problem solvers whose role is expanding through automation and AI. ## The Scope of a TAM’s Role - TAMs resolve partner integration issues and advise on API adoption. - They coordinate multiple teams during incidents and improve operational processes. - Their work includes automating recurring problems and influencing product and platform design. - Depending on the situation, a TAM may act like a developer, product manager, or incident leader. - At Toss, their responsibilities span authentication, Face Connect, financial platforms, online and offline payments, and partner APIs. ## Reducing Alert Noise and Preventing Recurring Problems - Toss’s Dayoung Park presented a problem-solving framework that began by redefining which alerts truly require attention. - Excessive notifications can obscure serious incidents, so the team focused on identifying meaningful operational signals. - They structured incident patterns, detected recurring issues automatically, and analyzed the root causes of settlement discrepancies. - The goal was not merely to resolve incidents faster, but to build systems that prevent them from recurring. ## Making Operations Independent of Individual Owners - PayToss’s Gimun Lee discussed reducing dependency on specific people’s knowledge. - Response histories and operational information were shared transparently so anyone could handle an issue. - Their Discord developer community used n8n workflows, LLM-based log analysis, and automatically generated incident-cause and resolution suggestions. - These tools helped the team maintain an average response time of under ten minutes. ## Using Customer Experience to Improve TAM Work - Toss’s Seongmin Chun drew on previous experience working for a customer organization. - Understanding customer frustrations and the information needed during incidents influenced his communication and support practices. - The team used the PDCA cycle to continually improve integration guides, standardize repeated communications, and structure operational processes. - Effective TAM work means not only solving current problems but also ensuring the same problems do not happen again. ## Shared Challenges Across Companies - TAMs must balance customer expectations with internal development teams. - They operate in rapidly changing services with increasingly complex systems. - The role requires simultaneous understanding of technology, business priorities, and customer needs. - TAMs often appear to sit ambiguously between support, operations, development, and business, but their actual work involves structuring complex problems and mobilizing multiple teams. - The event characterized TAMs as technology-based problem solvers rather than simple support personnel. ## AI and the Future of TAM Work - AI is already being applied to: - Log analysis - Incident-cause recommendations - Operations guide generation - Automated responses to recurring inquiries - Anomaly detection - Document search and summarization - As AI handles repetitive responses, TAMs are likely to focus more on complex problem-solving, structural improvements, cross-team coordination, customer-experience design, and operational strategy. TAM CONNECT demonstrated that TAM organizations can learn significantly from one another. As technology and services become more complex, TAMs will likely become increasingly strategic, combining automation and AI with the judgment needed to improve systems, processes, and customer experiences.

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This session from NAVER ENGINEERING DAY 2025 explores the implementation of visual data tools to interpret complex user behavior within Naver’s Integrated Search. By transforming raw quantitative click logs into intuitive heatmaps and histograms, the development team provides a clearer understanding of how users navigate and consume content. This approach serves as a critical bridge for stakeholders to find actionable evidence for service improvements that are often obscured by traditional data analysis. ### Visualizing User Intent through Heatmaps and Histograms * Click logs from Naver Integrated Search are converted into heatmaps to pinpoint exactly where users are focusing their attention and making their "first clicks." * Histograms are utilized alongside heatmaps to provide a temporal and frequency-based perspective on user interactions, making it easier to identify patterns in data consumption. * The visualization system aims to help developers and designers who struggle with raw quantitative data to gain an immediate, intuitive grasp of user experience (UX) performance. ### Handling Dynamic Data in Real-Time Search Services * The system is designed to respond to the "real-time evolution" of Naver Search, where content and UI layouts change frequently based on trends and algorithms. * The FE Infrastructure team shared technical know-how on collecting and processing client-side logs to ensure data accuracy even as the search interface evolves. * Significant trial and error were involved in developing a visualization framework that remains consistent and reliable across diverse search result types and user devices. ### Practical Application for Service Improvement * By using heatmaps as a primary diagnostic tool, teams can move beyond speculative design and base UI/UX updates on concrete visual evidence of user friction or engagement. * The technology allows for the identification of "dead zones" or overlooked features that may require repositioning or removal to streamline the search experience. * Integrating these visual tools into the development workflow enables faster feedback loops between data analysis and front-end implementation. For organizations managing high-traffic web platforms, moving from raw data tables to visual behavior mapping is essential for understanding the nuance of user interaction. Implementing a robust heatmap and histogram system allows teams to validate product hypotheses quickly and ensures that service updates are driven by actual user behavior rather than just aggregate metrics.