toss·Dec 8, 2025Original article

Improving Business Data Literacy: (opens in new tab)

Toss’s Business Data Team addressed the lack of centralized insights into their business customer (BC) base by building a standardized Single Source of Truth (SSOT) data mart and an iterative Monthly BC Report. This initiative successfully unified fragmented data across business units like Shopping, Ads, and Pay, enabling consistent data-driven decision-making and significantly raising the organization's overall data literacy. ## Establishing a Single Source of Truth (SSOT) - Addressed the inefficiency of fragmented data across various departments by integrating disparate datasets into a unified, enterprise-wide data mart. - Standardized the definition of an "active" Business Customer through cross-functional communication and a deep understanding of how revenue and costs are generated in each service domain. - Eliminated communication overhead by ensuring all stakeholders used a single, verified dataset rather than conflicting numbers from different business silos. ## Designing the Monthly BC Report for Actionable Insights - Visualized monthly revenue trends by segmenting customers into specific tiers and categories, such as New, Churn, and Retained, to identify where growth or attrition was occurring. - Implemented Cohort Retention metrics by business unit to measure platform stickiness and help teams understand which services were most effective at retaining business users. - Provided granular Raw Data lists for high-revenue customers showing significant growth or churn, allowing operational teams to identify immediate action points. - Refined reporting metrics through in-depth interviews with Product Owners (POs), Sales Leaders, and Domain Heads to ensure the data addressed real-world business questions. ## Technical Architecture and Validation - Built the core SSOT data mart using Airflow for scalable data orchestration and workflow management. - Leveraged Jenkins to handle the batch processing and deployment of the specific data layers required for the reporting environment. - Integrated Tableau with SQL-based fact aggregations to automate the monthly refresh of charts and dashboards, ensuring the report remains a "living" document. - Conducted "collective intelligence" verification meetings to check metric definitions, units, and visual clarity, ensuring the final report was intuitive for all users. ## Driving Organizational Change and Data Literacy - Sparked a surge in data demand, leading to follow-up projects such as daily real-time tracking, Cross-Domain Activation analysis, and deeper funnel analysis for BC registrations. - Transitioned the organizational culture from passive data consumption to active utilization, with diverse roles—including Strategy Managers and Business Marketers—now using BC data to prove their business impact. - Maintained an iterative approach where the report format evolves every month based on stakeholder feedback, ensuring the data remains relevant to the shifting needs of the business. Establishing a centralized data culture requires more than just technical infrastructure; it requires a commitment to iterative feedback and clear communication. By moving from fragmented silos to a unified reporting standard, data analysts can transform from simple "number providers" into strategic partners who drive company-wide literacy and growth.

SqlData AnalysisData PipelineAirflow+4

toss·Dec 1, 2025Original article

Toss Next ML Challenge (opens in new tab)

Toss recently hosted the "Toss Next ML Challenge," a large-scale competition focused on predicting advertisement Click-Through Rates (CTR) using real-world, anonymized data from the Toss app. By tasking over 2,600 participants with developing high-performance models under real-time serving constraints, the event successfully identified innovative technical approaches to feature engineering and model ensembling. ### Designing a Real-World CTR Prediction Task * The competition required participants to predict the probability of a user clicking a display ad based on a dataset of 10.7 million training samples. * Data included anonymized features such as age, gender, ad inventory IDs, and historical user behavior. * A primary technical requirement was "real-time navigability," meaning models had to be optimized for fast inference to function within a live service environment. ### Overcoming Anonymization with Sequence Engineering * To maintain data privacy while allowing external access, Toss provided anonymized features in a single flattened table, which limited the ability of participants to perform traditional data joins. * A complex, raw "Sequence" feature was intentionally left unprocessed to serve as a differentiator for high-performing teams. * Top-tier participants demonstrated extreme persistence by deriving up to 37 unique variables from this single sequence, including transition probabilities, unique token counts, and sequence lengths. ### Winning Strategies and Technical Trends * All of the top 30 teams utilized Boosting Tree-based models (such as XGBoost or LightGBM), while Deep Learning was used only by a subset of participants. * One standout solution utilized a massive ensemble of 260 different models, providing a fresh perspective on the limits of ensemble learning for predictive accuracy. * Performance was largely driven by the ability to extract meaningful signals from anonymized data through rigorous cross-validation and creative feature interactions. The results of the Toss Next ML Challenge suggest that even in the absence of domain-specific context due to anonymization, meticulous feature engineering and robust tree-based architectures remains the gold standard for tabular data. For ML engineers, the competition underscores that the key to production-ready models lies in balancing complex ensembling with the strict latency requirements of real-time serving.

AIMachine LearningFeature EngineeringXgboost+4

toss·Dec 1, 2025Original article

Toss Income Tax Refund Service: (opens in new tab)

Toss Income’s QA team transitioned from traditional manual testing and rigid class-based Page Object Models (POM) to a stateless Functional POM to keep pace with rapid deployment cycles. This shift allowed them to manage complex tax refund logic and frequent UI changes with high reliability and minimal maintenance overhead. By treating automation as a modular assembly of functions, they successfully reduced verification times from four hours to twenty minutes while significantly increasing test coverage. ### Transitioning to Functional POM * Replaced stateful classes and complex inheritance with stateless functions that receive a `page` object as input and return the updated `page` as output. * Adopted a clear naming convention (e.g., `gotoLoginPage`, `enterPhonePin`, `verifyRefundAmount`) to ensure that test cases read like human-readable scenarios. * Centralized UI selectors and interaction logic within these functions, allowing developers to update a single point of truth when UI text or button labels change. ### Modularizing the User Journey * Segmented the complex tax refund process into four distinct modules: Login/Terms, Deduction Checks, Refund/Payment Info, and Reporting. * Developed independent, reusable functions for specific data inputs—such as medical or credit card deductions—which can be assembled like "Lego blocks" to create new test scenarios rapidly. * Decoupled business logic from UI interactions, enabling the team to create diverse test cases by simply varying parameters like amounts or dates. ### Robust Interaction and Page Management * Implemented a 4-step "Robust Click Strategy" to eliminate flakiness caused by React rendering timings, sequentially trying an Enter key press, a standard click, a forced click, and finally a direct JavaScript execution. * Created a `waitForNetworkIdleSafely` utility that prevents test failures during polling or background network activity by prioritizing UI anchors over strict network idleness. * Standardized page transition handling with a `getLatestNonScrapePage` utility, ensuring the `currentPage` object always points to the most recent active tab or redirect window. ### Integration and Performance Outcomes * Achieved a 600% increase in test coverage, expanding from 5 core scenarios to 35 comprehensive automated flows. * Reduced the time required to respond to UI changes by 98%, as modifications are now localized to a single POM function rather than dozens of test files. * Established a 24/7 automated validation system that provides immediate feedback on functional correctness, data integrity (tax amount accuracy), and performance metrics via dedicated communication channels. For engineering teams operating in high-velocity environments, adopting a stateless, functional approach to test automation is a highly effective way to reduce technical debt. By focusing on modularity and implementing fallback strategies for UI interactions, teams can transform QA from a final bottleneck into a continuous, data-driven validation layer that supports rapid experimentation.

TypeScriptTest AutomationPlaywrightFunctional Programming+3

toss·Nov 30, 2025Original article

In Search of the Toss Brand Symbol (opens in new tab)

Toss, a leading Korean fintech platform, embarked on a UX research journey to define its visual identity as it expanded from digital services into offline environments like Toss Pay payment stations. The study revealed that while users strongly associate the brand with seamless "usability," they lacked a single, clear mental image of a visual symbol. By analyzing user perceptions of fonts, colors, and shapes, Toss identified a specific visual formula—combining the app icon shape with a white, blue, and black palette—to ensure the brand remains instantly recognizable in the physical world. ## The Challenge of Offline Brand Recognition * The project began with the need to design "danglers" (small signage at payment counters) to signal that Toss Pay is accepted at offline merchants. * While Toss had successfully used various logo iterations online, the team realized that "Toss-ness" learned within the app might not automatically translate to unfamiliar offline environments. * Initial internal debates focused on superficial visual tweaks, such as background colors or language choices, rather than understanding the core assets that trigger brand recognition. ## Identifying Usability as the Core Brand Image * In-depth interviews were conducted with participants selected for their ability to articulate abstract brand impressions. * Research showed that users primarily associate Toss with keywords like "clean," "practical," and "convenient," rather than specific aesthetic elements. * One participant described Toss as a "program made by a genius engineer in Excel," highlighting that the brand’s value was rooted in its utility rather than a distinct visual symbol. * This presented a challenge: since the "app experience" cannot be felt through a static offline sign, the team had to find a visual surrogate for that functional reliability. ## Deconstructing the Toss Symbol: Font, Color, and Shape * **Font:** Testing revealed that the most recognizable font was the black English "toss" wordmark, primarily because users see it most often in external media and news rather than inside the app. * **Color:** Surprisingly, users did not associate Toss with a single shade of blue. Instead, they recognized the specific combination of a "blue logo on a white background." * **Logo:** When asked to draw the logo from memory, users consistently included a square border. This indicated that users perceive the brand’s "face" specifically as the smartphone app icon (the blue logo inside a rounded square) rather than the standalone logo mark. ## Implementing the "Toss Formula" in Design * The research led to a refined brand identity formula: **White background + Black bold English font + Blue app-icon-shaped logo.** * In the "10 to 100" 10th-anniversary campaign, the company shifted away from all-blue backgrounds in favor of this white-based combination to maximize recognition. * Toss Pay payment screens were updated to remove blue backgrounds, adopting the white-and-black layout to align with how users intuitively identify the service. For UX researchers and designers, this case demonstrates that brand identity is often a composite of environmental cues rather than a single graphic. When moving a digital-first brand into the physical world, it is essential to look beyond the logo and identify the specific "visual formula" that triggers the user's memory of the product experience.

User ExperienceUser ResearchFintechBrand Identity+2

toss·Nov 30, 2025Original article

From Legacy Payment Ledger to Scalable System (opens in new tab)

Toss Payments successfully modernized a 20-year-old legacy payment ledger by transitioning to a decoupled, MySQL-based architecture designed for high scalability and consistency. By implementing strategies like INSERT-only immutability and event-driven domain isolation, they overcame structural limitations such as the inability to handle split payments. Ultimately, the project demonstrates that robust system design must be paired with resilient operational recovery mechanisms to manage the complexities of large-scale financial migrations. ### Legacy Ledger Challenges * **Inconsistent Schemas:** Different payment methods used entirely different table structures; for instance, a table named `REFUND` unexpectedly contained only account transfer data rather than all refund types. * **Domain Coupling:** Multiple domains (settlement, accounting, and payments) shared the same tables and columns, meaning a single schema change required impact analysis across several teams. * **Structural Limits:** A rigid 1:1 relationship between a payment and its method prevented the implementation of modern features like split payments or "Dutch pay" models. ### New Ledger Architecture * **Data Immutability:** The system shifted from updating existing rows to an **INSERT-only** principle, ensuring a reliable audit trail and preventing database deadlocks. * **Event-Driven Decoupling:** Instead of direct database access, the system uses Kafka to publish payment events, allowing independent domains to consume data without tight coupling. * **Payment-Approval Separation:** By separating the "Payment" (the transaction intent) from the "Approval" (the specific financial method), the system now supports multiple payment methods per transaction. ### Safe Migration and Data Integrity * **Asynchronous Mirroring:** To maintain zero downtime, data was initially written to the legacy system and then asynchronously loaded into the new MySQL ledger. * **Resource Tuning:** Developers used dedicated migration servers within the same AWS Availability Zone to minimize latency and implemented **Bulk Inserts** to handle hundreds of millions of rows efficiently. * **Verification Batches:** A separate batch process ran every five minutes against a Read-Only (RO) database to identify and correct any data gaps caused by asynchronous processing failures. ### Operational Resilience and Incident Response * **Query Optimization:** During a load spike, the MySQL optimizer chose "Full Scans" over indexes; the team resolved this by implementing SQL hints and utilizing a 5-version Docker image history for rapid rollbacks. * **Network Cancellation:** To handle timeouts between Toss and external card issuers, the system uses specific logic to automatically send cancellation requests and synchronize states. * **Timeout Standardization:** Discrepancies between microservices were resolved by calculating the maximum processing time of approval servers and aligning all upstream timeout settings to prevent merchant response mismatches. * **Reliable Event Delivery:** While using the **Outbox pattern** for events, the team added log-based recovery (Elasticsearch and local disk) and idempotency keys in event headers to handle both missing and duplicate messages. For organizations tackling significant technical debt, this transition highlights that initial design is only half the battle. True system reliability comes from building "self-healing" structures—such as automated correction batches and standardized timeout chains—that can survive the unpredictable nature of live production environments.

Database DesignApache KafkaMicroservicesMysql+4

toss·Nov 27, 2025Original article

Toss Payments' Open API (opens in new tab)

Toss Payments treats its Open API not just as a communication tool, but as a long-term infrastructure designed to support over 200,000 merchants for decades. By focusing on resource-oriented design and developer experience, the platform ensures that its interfaces remain intuitive, consistent, and easy to maintain. This strategic approach prioritizes structural stability and clear communication over mere functionality, fostering a reliable ecosystem for both developers and businesses. ### Resource-Oriented Interface Design * The API follows a predictable path structure (e.g., `/v1/payments/{id}`) where the root indicates the version, followed by the domain and a unique identifier. * Request and response bodies utilize structured JSON with nested objects (like `card` or `cashReceipt`) to modularize data and reduce redundancy. * Consistency is maintained by reusing the same domain objects across different APIs, such as payment approval, inquiry, and cancellation, which minimizes the learning curve for external developers. * Data representation shifts from cryptic legacy codes (e.g., SC0010) to human-readable strings, supporting localization into multiple languages via the `Accept-Language` HTTP header. * Standardized error handling utilizes HTTP status codes paired with a JSON error object containing specific `code` and `message` fields, allowing developers to either display messages directly or implement custom logic. ### Asynchronous Communication via Webhooks * Webhooks are provided alongside standard APIs to handle asynchronous events where immediate responses are not possible, such as status changes in complex payment flows. * Event types are clearly categorized (e.g., `PAYMENT_STATUS_CHANGED`), and the payloads mirror the exact resource structures used in the REST APIs to simplify parsing. * The system ensures reliability by implementing an Exponential Backoff strategy for retries, preventing network congestion during recipient service outages. * A dedicated developer center allows merchants to register custom endpoints, monitor transmission history, and perform manual retries if automated attempts fail. ### External Ecosystem and Documentation Automation * Developer Experience (DX) is treated as the core metric for API quality, focusing on how quickly and efficiently a developer can integrate and operate the service. * To prevent the common issue of outdated manuals, Toss Payments uses a documentation automation system based on the OpenAPI Specification (OAS). * By utilizing libraries like `springdoc`, the platform automatically syncs the technical documentation with the actual server code, ensuring that parameters, schemas, and endpoints are always up-to-date and trustworthy. To ensure the longevity of a high-traffic Open API, organizations should prioritize automated documentation and resource-based consistency. Moving away from cryptic codes toward human-readable, localized data and providing robust asynchronous notification tools like webhooks are essential steps for building a developer-friendly infrastructure.

API DesignJsonRest ApiDeveloper Experience+4

toss·Nov 26, 2025Original article

The era when everyone does research (opens in new tab)

In an era where AI moderators and non-researchers handle the bulk of data collection, the role of the UX researcher has shifted from a technical specialist to a strategic guide. The core value of the researcher now lies in "UX Leadership"—the ability to frame problems, align team perspectives, and define the fundamental identity of a product. By bridging the gap between business goals and user needs, researchers ensure that products solve real problems rather than just chasing metrics or technical feasibility. ### Setting the Framework in the Idea Phase When starting a new project, a researcher’s primary task is to establish the "boundaries of the puzzle" by shifting the team’s focus from business impact to user value. * **Case - AI Signal:** For a service that interprets stock market events using AI, the team initially focused on business metrics like retention and news consumption. * **Avoiding "Metric Traps":** A researcher intervenes to prevent fatigue-inducing UX (e.g., excessive notifications to boost CTR) by defining the "North Star" as the specific problem the user is trying to solve. * **The Checklist:** Once the user problem and value are defined, they serve as a persistent checklist for every design iteration and action item. ### Aligning Team Direction for Product Improvements When a product already exists but needs improvement, different team members often have scattered, subjective opinions on what to fix. The researcher structures these thoughts into a cohesive direction. * **Case - Stock Market Calendar:** While the team suggested UI changes like "it doesn't look like a calendar," the researcher refocused the effort on the user's ultimate goal: making better investment decisions. * **Defining Success Criteria:** The team agreed on a "Good Usage" standard based on three stages: Awareness (recognizing issues) → Understanding (why it matters) → Preparation (adjusting investment plans). * **Identifying Obstacles:** By identifying specific friction points—such as the lack of information hierarchy or the difficulty of interpreting complex indicators—the researcher moves the project from "simple UI cleanup" to "essential tool development." ### Redefining Product Identity During Stagnation When a product's growth stalls, the issue often isn't a specific UI bug but a fundamental mismatch between the product's identity and its environment. * **Case - Toss Securities PC:** Despite being functional, the PC version struggled because it initially tried to copy the "mobile simplicity" of the app. * **Contextual Analysis:** Research revealed that while mobile users value speed and portability, PC users require an environment for deep analysis, multi-window comparisons, and deliberate decision-making. * **Consensus through Synthesis:** The researcher integrates data, user interviews, and market trends into workshops to help the team decide where the product should "live" in the market. This process creates team-wide alignment on a new strategic direction rather than just fixing features. The modern UX researcher must move beyond "crafting the tool" (interviewing and data gathering) and toward "UX Leadership." True expertise involves maintaining a broad view of the industry and product ecosystem, structuring team discussions to reach a consensus, and ensuring that every product decision is rooted in a clear understanding of the user's context and goals.

AIUser ExperienceData AnalysisUsability Testing+3

toss·Nov 19, 2025Original article

Beyond LTV: MTV (opens in new tab)

Toss has developed MTVi (Mid-term Value - incremental) to quantify the financial impact of specific services within its platform, moving beyond the limitations of traditional LifeTime Value (LTV). By focusing on the incremental value generated over a one-year period, the metric allows the company to justify services that may lose money individually but drive significant ecosystem-wide growth. This framework provides a data-driven standard for prioritizing features and setting marketing budgets based on actual financial contributions. ### Limitations of Traditional LTV * **Time Horizon Mismatch:** Traditional LTV projects value over 3 to 5 years, which is too slow for Toss’s rapid iteration cycles and fails to reflect the immediate impact of service improvements. * **Investment Recovery Gaps:** Standard LTV models often benchmark marketing costs (CAC) against long-term projections, making it difficult to evaluate the efficiency of short-term experiments. * **Lack of Incrementality:** LTV measures average user value but cannot isolate the specific "extra" value created by a single service, making it impossible to distinguish between a service's impact and natural user growth. ### Defining MTVi and DID Methodology * **Incremental Focus:** MTVi is defined as the net financial value generated over one year specifically because a user experienced a new service, rather than just the average revenue of a user. * **Quasi-Experimental Design:** Since A/B testing every service combination is impossible, Toss uses the Difference-in-Difference (DID) method to compare "Newly Activated Users" (NAU) against "Never" users. * **Segment-Based Analysis:** To prevent bias—such as highly active users naturally gravitating toward more services—Toss segments users by age and historical activity (e.g., app open frequency) to ensure "apples-to-apples" comparisons within identical cohorts. ### Organizational Impact and Strategy * **Unified Decision Metric:** MTVi provides a "common language" for different product teams (silos), allowing them to compare the value of disparate services—like pedometers versus remittances—on a single financial scale. * **Efficiency Benchmarking:** The metric establishes a hard ceiling for investment; for example, Customer Acquisition Cost (CAC) is strictly managed so it does not exceed the calculated MTVi. * **Platform-Wide Valuation:** By calculating both direct revenue and indirect spillover effects, Toss can prove the financial viability of "loss-leader" services that provide user benefits but increase overall app engagement and cross-service usage. For organizations operating complex multi-service platforms, adopting an incremental value metric like MTVi is essential for moving beyond isolated P&L statements. Data teams should prioritize quasi-experimental methods like DID and rigorous user segmentation to accurately map how individual features influence the broader financial health of the ecosystem.

Data AnalyticsCausal InferenceDifference In DifferenceUnit Economics+4

toss·Nov 13, 2025Original article

Frontend Code That Lasts 1 (opens in new tab)

Toss Payments evolved its Payment SDK to solve the inherent complexities of integrating payment systems, where developers must navigate UI implementation, security flows, and exception handling. By transitioning from V1 to V2, the team moved beyond simply providing a library to building a robust, architecture-driven system that ensures stability and scalability across diverse merchant environments. The core conclusion is that a successful SDK must be treated as a critical infrastructure layer, relying on modular design and deep observability to handle the unpredictable nature of third-party runtimes. ## The Unique Challenges of SDK Development * SDK code lives within the merchant's runtime environment, meaning it shares the same lifecycle and performance constraints as the merchant’s own code. * Internal logging can inadvertently create bottlenecks; for instance, adding network logs to a frequently called method can lead to "self-DDoS" scenarios that crash the merchant's payment page. * Type safety is a major hurdle, as merchants may pass unexpected data types (e.g., a number instead of a string), causing fatal runtime errors like `startsWith is not a function`. * The SDK acts as a bridge for technical communication, requiring it to function as both an API consumer for internal systems and an API provider for external developers. ## Ensuring Stability through Observability * To manage the unpredictable ways merchants use the SDK, Toss implemented over 300 unit tests and 500 E2E integration tests based on real-world use cases. * The team utilizes a "Global Trace ID" to track a single payment journey across both the frontend and backend, allowing for seamless debugging across the entire system. * A custom Monitoring CLI was developed to compare payment success rates before and after deployments, categorized by merchant and runtime environment (e.g., PC Chrome vs. Android WebView). * This observability infrastructure enables the team to quickly identify edge-case failures—such as a specific merchant's checkout failing only on mobile WebViews—which are often missed by standard QA processes. ## Scaling with Modular Architecture * To avoid "if-statement hell" caused by merchant-specific requirements (e.g., fixing installment months or custom validation for a specific store), Toss moved to a "Lego-block" architecture. * The SDK is organized into three distinct layers based on the "reason for change" principle: * **Public Interface Layer:** Manages the contract with the merchant, validating inputs and translating them into internal domain models. * **Domain Layer:** Encapsulates core business logic and payment policies, keeping them isolated from external changes. * **External Service Layer:** Handles dependencies like Server APIs and Web APIs, ensuring technical shifts don't leak into the business logic. * This separation allows the team to implement custom merchant logic by swapping specific blocks without modifying the core codebase, reducing the risk of regressions and lowering maintenance costs. For developers building SDKs or integration tools, the shift from monolithic logic to a layered, observable architecture is essential. Prioritizing the separation of domain logic from public interfaces and investing in environment-specific monitoring allows for a highly flexible product that remains stable even as the client-side environment grows increasingly complex.

ObservabilityTypeScriptSoftware ArchitectureElasticsearch+3

toss·Nov 6, 2025Original article

Creating the worst experience at Toss (opens in new tab)

Toss designer Lee Hyeon-jeong argues that business goals and user experience are not mutually exclusive, even when integrating controversial elements like advertising. By identifying the intersection between monetization and usability, her team transformed intrusive ads into value-driven features that maintain user trust while driving significant revenue. The ultimate conclusion is that transparency and appropriate rewards can mitigate negative feedback and even increase user engagement. ### Reducing Friction through Predictability and Placement * Addressed "surprise" ads by introducing clear labeling, such as "Watch Ad" buttons or specifying ad durations (e.g., "30-second ad"), which reduced negative sentiment without decreasing revenue. * Discovered that when users are given a choice and clear expectations, their anxiety decreases and their willingness to engage with the content increases. * Eliminated "flow-breaking" ads that mimicked functional UI elements, such as banners placed inside transaction histories that users frequently mistook for personal bank records. * Established a design principle to place advertisements only in areas that do not interfere with information discovery or core user navigation tasks. ### Transforming Advertisements into User Benefits * Developed a dedicated B2B ad platform to scale the variety of available advertisements, ensuring that users receive ads relevant to their specific life stages, such as car insurance or new credit cards. * Shifted the internal perception of ads from "noise" to "benefits" by focusing on the right timing and high-quality matching between the advertiser and the user's needs. * Institutionalized regular "creative ideation sessions" to explore interactive formats, including advertisements that respond to phone movement (gyroscope), quizzes, and mini-games. * Leveraged long-term internal experiments to ensure that even if an idea cannot be implemented immediately, it remains in the team's "creative bank" for future product opportunities. ### Optimizing Value Exchange through Rewards * Conducted over a year of A/B testing on reward thresholds, comparing small cash amounts (1 KRW to 200 KRW), non-monetary items (gifticons), and high-stakes lottery-style prizes. * Analyzed the "labor intensity" of ads by adjusting lengths (10 to 30 seconds) to find the psychological tipping point where users felt the reward was worth their time. * Implemented a high-value lottery system within the Toss Pedometer service, which successfully transitioned a loss-making feature into a profitable revenue stream. * Maintained user activity and satisfaction levels despite the increased presence of ads by ensuring the "worst-case experience"—viewing ads for no gain—was entirely avoided. Product teams should stop viewing business requirements and UX as a zero-sum game. By focusing on user psychology—specifically transparency, non-disruption, and fair value exchange—it is possible to achieve aggressive business targets while maintaining a sustainable and trusted user environment.

User ExperienceProduct DesignA B TestingGamification+3

toss·Nov 3, 2025Original article

Working as a QA in a (opens in new tab)

Toss Place implements a dual-role QA structure where managers are embedded directly within product Silos from the initial planning stages to final deployment. This shift moves QA from a final-stage bottleneck to a proactive partner that enhances delivery speed and stability through deep historical context and early risk mitigation. Consequently, the organization has transitioned to a culture where quality is viewed as a shared team responsibility rather than a siloed functional task. ### Integrating QA into Product Silos * QA managers belong to both a central functional team and specific product units (Silos) to ensure they are involved in the entire product lifecycle. * Participation begins at the OKR design phase, allowing QA to align testing strategies with specific product intentions and business goals. * Early involvement enables accurate risk assessment and scope estimation, preventing the "shallow testing" that often occurs when QA only sees the final product. ### Optimizing Spec Reviews and Sanity Testing * The team introduced a structured flow consisting of Spec Reviews followed by Q&A sessions to reduce repetitive discussions and information gaps. * All specification changes are centralized in shared design tools (such as Deus) or messenger threads to ensure transparency across all roles. * "Sanity Test" criteria were established where developers and QA agree on "Happy Case" validations and minimum spec requirements before development begins, ensuring everyone starts from the same baseline. ### Collaborative Live Monitoring * Post-release checklists were developed to involve the entire Silo in live monitoring, overcoming the limitations of having a single QA manager per unit. * This collaborative approach encourages non-technical roles to interact with the live product, reinforcing the culture that quality is a collective team responsibility. ### Streamlining Issue Tracking and Communication * The team implemented a "Send to Notion" workflow to instantly capture messenger-based feedback and ideas into a structured, prioritized backlog. * To reduce communication fragmentation, they transitioned from Jira to integrated Messenger Lists and Canvases, which allowed for centralized discussions and faster issue resolution. * Backlogs are prioritized based on user experience impact and release urgency, ensuring that critical bugs are addressed while minor improvements are tracked for future cycles. The success of these initiatives demonstrates that QA effectiveness is driven by integration and autonomy rather than rigid adherence to specific tools. To achieve both high velocity and high quality, organizations should empower QA professionals to act as product peers who can flexibly adapt their processes to the unique needs and data-driven goals of their specific product teams.

Database DesignJiraNotionQuality Assurance+4

toss·Oct 30, 2025Original article

Toss People: Designing a structure (opens in new tab)

Data architecture is evolving from a reactive "cleanup" task into a proactive, end-to-end design process that ensures high data quality from the moment of creation. In fast-paced platform environments, the role of a Data Architect is to bridge the gap between rapid product development and reliable data structures, ultimately creating a foundation that both humans and AI can interpret accurately. By shifting from mere post-processing to foundational governance, organizations can maintain technical agility without sacrificing the integrity of their data assets. **From Post-Processing to End-to-End Governance** * Traditional data management often involves "fixing" or "matching puzzles" at the end of the pipeline after a service has already changed, leading to perpetual technical debt. * Effective data architecture requires a culture where data is treated as a primary design object from its inception, rather than a byproduct of application development. * The transition to an end-to-end governance model ensures that data quality is maintained throughout its entire lifecycle—from initial generation in production systems to final analysis and consumption. **Machine-Understandable Data and Ontologies** * Modern data design must move beyond human-readable metadata to structures that AI can autonomously process and understand. * The implementation of semantic-based standard dictionaries and ontologies reduces the need for "inference" or guessing by either humans or machines. * By explicitly defining the relationships and conceptual meanings of columns and tables, organizations create a high-fidelity environment where AI can provide accurate, context-aware responses without interpretive errors. **Balancing Development Speed with Data Quality** * In high-growth environments, insisting on "perfect" design can hinder competitive speed; therefore, architects must find a middle ground that allows for future extensibility. * Practical strategies include designing for current needs while leaving "logical room" for anticipated changes, ensuring that future cleanup is minimally disruptive. * Instead of enforcing rigid rules, architects should design systems where following the standard is the "path of least resistance," making high-quality data entry easier for developers than the alternative. **The Role of the Modern Data Architect** * The role has shifted from a fixed, corporate function to a dynamic problem-solver who uses structural design to solve business bottlenecks. * A successful architect must act as a mediator, convincing stakeholders that investing in a 5% quality improvement (e.g., moving from 90 to 95 points) provides significant long-term ROI in decision-making and AI reliability. * Aspiring architects should focus on incremental structural improvements, as any data professional who cares about how data functions is already operating on the path to data architecture.

Data GovernanceData ArchitectureData ModelingMetadata+4