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Geek Labs
Geek Labs
May 2, 2026 · Artificial Intelligence

5 Practical Open‑Source AI Tools for Design, Blogging, Prototyping, and Product Analytics

This article introduces five open‑source AI‑powered tools—huashu‑design for high‑fidelity design generation, Kami for elegant static blogs, open‑codesign for multi‑model prototyping, agents‑md for professional AI coding assistance, and PostHog for self‑hosted product analytics—detailing their core features, usage steps, and ideal users.

AI designGitHubopen source
0 likes · 9 min read
5 Practical Open‑Source AI Tools for Design, Blogging, Prototyping, and Product Analytics
Big Data Tech Team
Big Data Tech Team
Sep 17, 2025 · Big Data

How to Build a Scalable Tag System for Recommendation Engines

This article explains why a robust tag system is essential for recommendation and mining strategies, outlines the hierarchy of entity, concept, and theme tags, and provides practical principles, architecture, and step‑by‑step methods for constructing and managing tags in large‑scale data platforms.

Big DataData Architecturedata labeling
0 likes · 14 min read
How to Build a Scalable Tag System for Recommendation Engines
Data Thinking Notes
Data Thinking Notes
Nov 23, 2023 · Big Data

How Data-Driven Metrics Transform Product Analytics and Decision-Making

This article explains how to build a data‑driven metric system—from defining end‑to‑start metrics and combining business and data drivers, to applying statistical analysis, machine‑learning, causal inference, and practical case studies for alerting, diagnosing, and strategizing product performance.

Data-drivenMetricscausal inference
0 likes · 22 min read
How Data-Driven Metrics Transform Product Analytics and Decision-Making
DataFunSummit
DataFunSummit
Oct 26, 2023 · Big Data

Data‑Driven Metric System Construction and Application: Theory, Methods, and Real‑World Cases

This article explains how to build and apply a data‑driven metric system, covering end‑to‑end design principles, business‑ versus data‑driven approaches, frameworks such as OSM, GSM and HEART, statistical and machine‑learning techniques, causal inference, and practical case studies that illustrate alerting, diagnosis, and strategy deployment in product operations.

Data-drivencausal inferencemachine learning
0 likes · 21 min read
Data‑Driven Metric System Construction and Application: Theory, Methods, and Real‑World Cases
DaTaobao Tech
DaTaobao Tech
Sep 8, 2023 · Product Management

BPPISE Framework for Product Data Science Case Studies

The fourth article in a ten‑part Taobao series introduces the BPPISE framework—Business, Problem, Data, Insight, Strategy, Evaluation—as a product‑data‑science case structure, contrasting it with CRISP‑DM, detailing each stage, offering writing tips, and noting the team’s recruitment for data‑science talent.

BPPISEData ScienceFramework
0 likes · 9 min read
BPPISE Framework for Product Data Science Case Studies
Qunhe Technology User Experience Design
Qunhe Technology User Experience Design
Mar 3, 2023 · Product Management

How to Measure User Experience Efficiently: Core Path Tracking & Metric Analysis

This article explains why full‑stack user behavior tracking is often impractical, introduces a low‑cost core‑path instrumentation approach, defines key experience metrics, presents layered and cross‑matrix analysis methods, and shares a concrete product case study that demonstrates how to turn data into actionable business insights while saving technical, communication, and analysis costs.

Data-drivencore pathmetric tracking
0 likes · 12 min read
How to Measure User Experience Efficiently: Core Path Tracking & Metric Analysis
DataFunSummit
DataFunSummit
Jan 30, 2023 · Fundamentals

Understanding AB Testing: Design, Execution, and Analysis

This article explains the purpose, methodology, and practical examples of AB testing, describing how randomized traffic segmentation, logging, and metric analysis enable data‑driven product decisions across various industries while also noting its widespread adoption and promotional resources.

AB testingData-drivenexperiment design
0 likes · 7 min read
Understanding AB Testing: Design, Execution, and Analysis
DataFunTalk
DataFunTalk
Jan 23, 2023 · Fundamentals

Understanding A/B Testing: Purpose, Process, and Practical Examples

A/B testing is a scientific method for product iteration that uses random user grouping, traffic segmentation, and metric analysis to derive representative conclusions, widely applied across major tech companies for evaluating ROI, with detailed workflow, example scenarios, and guidance on design and analysis.

A/B testingproduct analytics
0 likes · 5 min read
Understanding A/B Testing: Purpose, Process, and Practical Examples
Python Crawling & Data Mining
Python Crawling & Data Mining
Dec 4, 2022 · Product Management

Master Data Tracking: Key Scenarios, Workflow & the 7‑Step ‘Seven‑Word’ Guide

Data tracking (埋点) records user actions to inform product optimization, covering passive and active behaviors, with applications ranging from exposure, click, to page events, and follows a detailed workflow—from requirement gathering and design, through development, testing, deployment, to analysis—summarized by a concise seven‑step methodology.

Data Product ManagementData TrackingExposure Tracking
0 likes · 11 min read
Master Data Tracking: Key Scenarios, Workflow & the 7‑Step ‘Seven‑Word’ Guide
DataFunTalk
DataFunTalk
Nov 27, 2022 · Product Management

Challenges of Traditional Experiment Systems and the Vision for Next‑Generation Evaluation Platforms

The article examines why classic A/B testing frameworks struggle with modern internet services—highlighting issues of intervention, measurement, and analysis—while proposing an observational, dynamic, and decision‑oriented next‑generation experiment system that leverages statistical learning and Bayesian optimization.

A/B testingBayesian OptimizationExperiment Platform
0 likes · 11 min read
Challenges of Traditional Experiment Systems and the Vision for Next‑Generation Evaluation Platforms
DataFunTalk
DataFunTalk
Nov 11, 2022 · Product Management

Data Tracking (埋点) Application Scenarios, Workflow, and the Seven‑Word Guideline

This article explains the concept of data tracking (埋点), outlines its key application scenarios such as exposure, click, and page‑event tracking, describes the end‑to‑end workflow from requirement gathering to deployment and post‑analysis, and summarizes the practical “seven‑word” checklist for successful implementation.

Data Trackingdata collectionproduct analytics
0 likes · 12 min read
Data Tracking (埋点) Application Scenarios, Workflow, and the Seven‑Word Guideline
Dada Group Technology
Dada Group Technology
Sep 30, 2022 · Product Management

Best Practices for Data Tracking Governance at JD Daojia: Process, Standards, and Quality Improvements

This article outlines JD Daojia's comprehensive approach to data tracking (埋点) governance, covering the definition, motivations, unified processes, detailed standards, quality controls, platform optimizations, measurable benefits, and future directions to ensure high‑quality, reliable analytics across multiple client platforms.

Data TrackingProcess Standardizationgovernance
0 likes · 13 min read
Best Practices for Data Tracking Governance at JD Daojia: Process, Standards, and Quality Improvements
Architecture Digest
Architecture Digest
Jul 12, 2022 · Big Data

Intelligent Gray Release Data System for Vivo Game Center: Methodology and Solutions

This article presents Vivo Game Center's end‑to‑end intelligent gray‑release data system, detailing its experimental mindset, statistical methods, data models, and product solutions that ensure scientific version evaluation, project progress, and rapid issue closure through root‑cause analysis and full‑process automation.

A/B testingRoot Cause Analysisdata analysis
0 likes · 18 min read
Intelligent Gray Release Data System for Vivo Game Center: Methodology and Solutions
DaTaobao Tech
DaTaobao Tech
Apr 11, 2022 · Industry Insights

How Offline Causal Inference Unlocks 3D Product Value on Taobao

This article explains observational causal inference fundamentals, compares methods like propensity score matching, Bayesian causal graphs, and difference‑in‑differences, and demonstrates their practical application in evaluating the business impact of Taobao's 3D sample rooms.

3d-visualizationBayesian networksPropensity Score Matching
0 likes · 15 min read
How Offline Causal Inference Unlocks 3D Product Value on Taobao
Baobao Algorithm Notes
Baobao Algorithm Notes
Feb 15, 2022 · Industry Insights

Why Your Algorithm Gains May Still Drag Down Overall Business: 6 Hidden Pitfalls

Even when individual algorithm modules show higher accuracy or revenue, the overall platform can decline due to factors like competitor encroachment, macro‑economic shifts, concept drift, overlapping marginal returns, attribution errors, and coupled A/B experiments, all of which require careful analysis and mitigation.

AB testingMetricsalgorithm
0 likes · 7 min read
Why Your Algorithm Gains May Still Drag Down Overall Business: 6 Hidden Pitfalls
DataFunSummit
DataFunSummit
Feb 5, 2022 · Artificial Intelligence

Causal Analysis: Challenges, Methodology, and Practice at Beike

This article introduces causal analysis, outlines its major challenges such as correlation versus causation, confounding factors, and selection bias, explains a three‑step framework (association, intervention, counterfactual), and details how Beike applied these principles in a smart client‑management tool with rigorous A/B experiments.

AB testingAIBeike
0 likes · 14 min read
Causal Analysis: Challenges, Methodology, and Practice at Beike
DataFunTalk
DataFunTalk
Jan 20, 2022 · Product Management

User Lifecycle Management: Definitions, Segmentation, Metrics, and Operational Strategies

This comprehensive guide explains user lifecycle concepts, including definitions of lifetime and LTV, segmentation methods, stage-specific operational tactics, system architecture, and key performance indicators to help product teams optimize acquisition, growth, retention, and revenue across the entire user journey.

LTVgrowth strategyproduct analytics
0 likes · 14 min read
User Lifecycle Management: Definitions, Segmentation, Metrics, and Operational Strategies
ByteDance Data Platform
ByteDance Data Platform
Jan 14, 2022 · Product Management

Why A/B Testing Matters: Theory, ByteDance Architecture & Best Practices

This article explains why A/B testing is crucial for data‑driven product decisions, outlines ByteDance’s A/B testing system architecture across multiple layers, describes client‑ and server‑side experiment workflows, shares statistical best practices, and presents real‑world case studies illustrating hypothesis generation, evaluation, and future industry trends.

A/B testingByteDanceData-driven
0 likes · 15 min read
Why A/B Testing Matters: Theory, ByteDance Architecture & Best Practices
58UXD
58UXD
Dec 9, 2021 · Product Management

How We Built an Experience Cockpit: From Data Collection to Actionable Insights

This article details the design and implementation of the Experience Cockpit, a one‑stop platform for monitoring user experience data, covering its purpose, metric hierarchy, non‑intrusive data collection, AI‑driven processing, visualization dashboards, alerting mechanisms, and how it drives product decisions.

AI NLPUX ResearchUser experience
0 likes · 10 min read
How We Built an Experience Cockpit: From Data Collection to Actionable Insights
DataFunTalk
DataFunTalk
Nov 1, 2021 · Product Management

Online Experiment Design and Analysis: Practices, Case Studies, and Guidelines from Tencent Data Platform

This article presents a comprehensive overview of online experiment design and analysis, covering basic definitions, AB testing principles, complex experiment types, real-world case studies from Tencent's information flow platform, and practical guidelines for reliable experiment evaluation and product decision‑making.

A/B testingRecommendation Systemscausal inference
0 likes · 21 min read
Online Experiment Design and Analysis: Practices, Case Studies, and Guidelines from Tencent Data Platform
Liulishuo Tech Team
Liulishuo Tech Team
Oct 26, 2020 · Fundamentals

Causal Inference Methods for Quantifying Product Impact in Data Analytics

This article explains how data analysts can use experimental and observational research methods, including randomized controlled trials, quasi‑experiments, difference‑in‑differences, regression discontinuity, synthetic control, and Bayesian structural time‑series, to assess the causal impact of product and marketing changes on business metrics.

AB testingcausal inferencedifference-in-differences
0 likes · 7 min read
Causal Inference Methods for Quantifying Product Impact in Data Analytics
58 Tech
58 Tech
Oct 21, 2019 · Big Data

Improving Information Exposure Measurement: Visible Ad Metrics and Data Processing Practices at 58 Platform

To address inaccuracies in traditional information exposure metrics, this article proposes adopting advertising visibility standards—defining visible exposure by pixel and time thresholds, implementing client-side logging, unique TID tracking, and ETL pipelines—to provide more reliable data for product strategy and user behavior analysis.

Big DataData Qualityad visibility
0 likes · 8 min read
Improving Information Exposure Measurement: Visible Ad Metrics and Data Processing Practices at 58 Platform
Python Programming Learning Circle
Python Programming Learning Circle
Oct 16, 2019 · Product Management

What Happened to Xiaohongshu? Inside the Data Behind Its 77‑Day Removal

The article analyzes Xiaohongshu’s 77‑day removal, showing a sharp drop in total active users and DAU, a partial rebound driven by loyal users, the scramble for alternative download sources, opportunistic third‑party sellers, and emerging competitors, while highlighting the product‑management challenges of such a disruption.

User RetentionXiaohongshuapp removal
0 likes · 11 min read
What Happened to Xiaohongshu? Inside the Data Behind Its 77‑Day Removal
Liulishuo Tech Team
Liulishuo Tech Team
Dec 2, 2016 · Product Management

Estimating Daily Active Users (DAU) Using New Users and Retention Modeling

This article explains how to estimate future daily active users (DAU) for an app by modeling the accumulation of new users and their retention decay, addressing challenges of changing historical retention rates and proposing a combined approach using recent averages and curve‑fitted functions to predict long‑term user activity.

DAUforecastingproduct analytics
0 likes · 9 min read
Estimating Daily Active Users (DAU) Using New Users and Retention Modeling
Suning Design
Suning Design
Aug 12, 2014 · Game Development

How Data Predicts Game Success: From User Acquisition to Budget Decisions

This article explains how game developers and marketers use internal, platform, and external data to forecast product performance, optimize user acquisition, predict market trends, model retention curves, and make informed budgeting decisions throughout a game's lifecycle.

MarketingPredictive ModelingUser Retention
0 likes · 14 min read
How Data Predicts Game Success: From User Acquisition to Budget Decisions