This article walks readers through the entire data‑modeling lifecycle—from clarifying business goals and inventorying data sources to defining metrics, designing schema, processing data, validating quality, and iterating the model for sustainable business impact.
The article explains that most AI project failures stem from unrealistic evaluation rather than model intelligence, and outlines concrete practices—business‑aligned metrics, scenario sandboxes, human‑in‑the‑loop reviews, and auditable documentation—to make model evaluation truly actionable.
The article clarifies the distinction between data models, which structure and store business data, and metric models, which define and manage business measurements, covering their core elements, construction logic, outputs, application stages, iteration frequency, and how they complement each other.
The article reflects on a CTO‑level reading approach, explains why architects must align with CEOs by measuring economic value rather than pure technical metrics, outlines concrete ways architects create revenue and cut costs, and proposes a capability ladder to guide career growth.
This case study details YTO Express's comprehensive redesign of its member mini‑program—covering visual branding, interaction consistency, emotional resonance, and multi‑device adaptation—showing how systematic UX upgrades drove a 168% rise in experience scores and dramatic business metric improvements.
This article explains the definition of ROI, provides the standard calculation formula with examples, and explores its practical applications in project evaluation, budgeting, risk management, and operational optimization while also highlighting its key limitations.
This article presents a comprehensive AI‑driven system for automatically detecting anomalies in over 1,000 Ctrip train‑ticket business metrics and pinpointing their root causes, detailing the background, unsupervised algorithms, detection and attribution pipelines, practical results, and future improvements.
This article explains metric attribution for internet platforms, covering its definition, a three‑step analytical framework, deterministic and probabilistic methods such as metric decomposition, machine‑learning models with SHAP values, case studies, and a practical tool that guides users through attribution analysis.
This article explains the fundamentals of metric attribution, outlines a three‑step framework for identifying, analyzing, and solving metric changes, compares deterministic, probabilistic, and speculative methods, and illustrates the approach with two real‑world case studies using decomposition and machine‑learning techniques.
This article explains the origins of SaaS, outlines the Chinese market landscape, and details fifteen crucial business metrics—including acquisition cost, LTV, MRR, ARR, NDR, and retention indicators—while also covering product development stages, GTM strategies, and growth models such as PLG and SLG for SaaS startups.
This article presents a comprehensive study on forecasting critical business metrics such as traffic, order volume, and GMV using traditional, machine‑learning, and deep‑learning time‑series models, detailing feature engineering, model design, experimental comparison, online deployment, and monitoring strategies.
Facing unpredictable software behavior, the article explains why traditional system‑level monitoring often misses critical business issues, especially in complex pricing services, and presents a comprehensive approach that combines result (black‑box) and process (white‑box) monitoring, practical metrics, and actionable recommendations to improve observability and reduce operational risk.
The paper presents the ‘Post‑Darwin’ evaluation framework, which uses stratified sampling to compare participants and non‑participants across uniform payment layers, overcoming uneven user distributions and the lack of viable A/B tests in game‑business effect analysis, and demonstrates its effectiveness through real‑world promotional and reservation case studies.
The paper proposes a containerized, machine‑learning framework that fuses rule‑based and XGBoost‑driven anomaly detection to monitor daily active users on a cloud music platform, achieving 89 % recall, 81 % precision and up to 74 % recall improvement over traditional threshold methods, while outlining future model refinement and broader metric applicability.
The article presents a five‑step experience measurement methodology that combines operational and perception data to link customer experience with business performance, offering practical guidance on model building, metric decomposition, diagnosis, redesign, continuous monitoring, and long‑term implementation principles.
This article provides a comprehensive overview of Meituan's merchant platform data metric system, detailing its five main modules—overview, business, traffic, customer, and product—along with competitor analysis and actionable insights for merchants to improve operations and growth.
This article explains how data can empower business decisions, outlines a seven‑step workflow for constructing a cross‑departmental data indicator system, and demonstrates the use of OSM, AARRR/UJM, and MECE models to create a universal, hierarchical metric framework applicable to most internet products.
This article introduces nine fundamental data analysis techniques—including periodic, structural, layered, matrix, decomposition, funnel, correlation, tag, and MECE methods—explaining how to apply each to interpret business metrics, uncover insights, and guide decision‑making without requiring advanced mathematics or complex algorithms.
This case study details how 58.com’s second‑hand car platform aligned product strategy with evolving consumer behavior, dissected user mind differences across key touchpoints, and implemented targeted design improvements on the homepage, filter, list, and detail pages, ultimately exceeding phone‑conversion targets by over 95%.
Sales teams often chase arbitrary “magic numbers” like 100 calls or 100 minutes, mistaking correlation for causation; this article explains the original meaning of magic numbers in user retention analysis, highlights statistical errors, and outlines a data‑driven four‑step process to identify true performance drivers and actionable strategies.
Effective data product development hinges on deep business understanding, clear metric decomposition, rigorous model evaluation, and translating technical performance into business impact, while algorithm engineers are best assessed by publication quality, problem significance, algorithmic contribution, and practical interview questions on model tuning and improvement.
This article describes a monitoring system that leverages machine learning to perform threshold‑free, real‑time anomaly detection on macro business indicators such as network traffic and access volume, detailing its architecture, sample labeling, model training, and multi‑level alarm strategies.
This article describes how a machine‑learning‑driven monitoring system replaces fixed thresholds with personalized, anomaly‑based detection for business‑level metrics such as network traffic and access volume, detailing the architecture, sample labeling, model training, alarm grading, and operational benefits.
Alibaba’s monitoring evolution—from fragmented early tools to the standardized Sunfire platform and now AI‑powered full‑link observability—addresses scaling challenges, introduces business‑centric metrics, automated traceability, and intelligent anomaly detection, illustrating how massive, multi‑tenant infrastructures achieve unified, proactive operations at scale.
Alibaba’s infrastructure team introduces a full‑link business monitoring approach that visualizes end‑to‑end health from a business perspective, unifies metrics, automates data collection, and leverages intelligent baseline alerts, enabling rapid issue detection, precise root‑cause analysis, and fine‑grained dimension monitoring across services.
This article explains why quantifiable data is essential for evaluating the impact of operational changes, outlines common data‑collection stacks, defines core business and user‑centric metrics, and demonstrates practical monitoring techniques such as PCU analysis, simulated user flows, and intelligent scaling to turn ops work into measurable business value.
