user growth | BestHub

DataFunTalk

Jan 16, 2025 · Artificial Intelligence

Challenges and Opportunities for Xiaohongshu’s Recommendation System Amid TikTok User Influx

The potential ban of TikTok has driven a wave of users to Xiaohongshu, prompting rapid growth but also exposing language, regulatory, and recommendation algorithm challenges that require AI‑driven translation, multi‑language moderation, and a revamped recommendation engine to sustain the platform’s expansion.

AI TranslationLarge Language ModelsXiaohongshu

0 likes · 7 min read

Challenges and Opportunities for Xiaohongshu’s Recommendation System Amid TikTok User Influx

ByteDance Data Platform

Jul 31, 2024 · Product Management

How Data‑Driven Flywheels Power User Growth: Insights from Volcengine

This article shares a data‑centric perspective on user growth, covering entropy reduction, information management, the data‑driven flywheel, A/B testing practices, retention strategies, and practical case studies that illustrate how systematic data analysis fuels sustainable product expansion.

A/B testingdata-drivenentropy reduction

0 likes · 16 min read

How Data‑Driven Flywheels Power User Growth: Insights from Volcengine

DataFunTalk

Jul 7, 2024 · Product Management

User Growth Strategies: From Information Management to a Data‑Driven Flywheel

This article shares a data‑centric perspective on user growth, covering the evolution of information management, distribution and production, the concept of entropy reduction in products, the data‑driven flywheel model, practical AB‑testing case studies, and a Q&A on analytics tools and team collaboration.

AB testinganalyticsdata-driven

0 likes · 16 min read

User Growth Strategies: From Information Management to a Data‑Driven Flywheel

DataFunSummit

Jun 22, 2024 · Artificial Intelligence

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

This article introduces causal inference fundamentals, distinguishes correlation from causation, reviews major methodological streams, and demonstrates how uplift and gain models—implemented with T‑learner, S‑learner, and tree‑based approaches—can be applied to user growth and marketing scenarios, including evaluation metrics and future challenges.

A/B testingcausal inferencemachine learning

0 likes · 14 min read

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

DataFunSummit

Dec 4, 2023 · Product Management

Designing an AB Experiment System for User Growth Scenarios

This article presents a comprehensive AB testing framework tailored for new‑user growth scenarios, detailing the challenges of early traffic allocation, the scientific validation of a new experiment system, real‑world case studies, and practical guidelines for evaluation and implementation.

AB testingdata analysisexperiment design

0 likes · 14 min read

Designing an AB Experiment System for User Growth Scenarios

DataFunSummit

Sep 27, 2023 · Product Management

A Decade of User Growth in the General Entertainment Industry and Its Application to Tencent Video

This article reviews ten years of user growth (UG) development in the general entertainment sector, outlines three historical stages, and then details how Tencent Video sets realistic goals, integrates resources, designs growth plans, measures outcomes, and applies advanced attribution, revenue splitting, LTV modeling, and experimental validation to drive sustainable growth.

AttributionExperimentationLTV Modeling

0 likes · 15 min read

A Decade of User Growth in the General Entertainment Industry and Its Application to Tencent Video

DataFunTalk

Sep 27, 2023 · Product Management

Building an AB Experiment System for User Growth Scenarios

This article presents a comprehensive AB testing framework tailored for new‑user growth scenarios, detailing the challenges of early traffic splitting, the design of a scientifically validated experiment system, ID selection criteria, and real‑world case studies that demonstrate improved retention and device activation.

AB testingMobile AppsProduct Metrics

0 likes · 12 min read

Building an AB Experiment System for User Growth Scenarios

DataFunSummit

Jul 21, 2023 · Operations

User Growth Practices and Data Strategy for Taobao Live App (DianTao)

This article presents a comprehensive overview of Taobao Live's (DianTao) user growth practice, detailing business background, industry challenges, data‑driven acquisition and retention strategies, lifecycle segmentation, and concrete capability designs to boost acquisition, activation, and recall in a competitive e‑commerce live‑streaming market.

AcquisitionData StrategyLifecycle

0 likes · 20 min read

User Growth Practices and Data Strategy for Taobao Live App (DianTao)

DataFunTalk

Jul 17, 2023 · Big Data

User Growth Practices and Data Strategy for Taobao Live App (DianTao)

The article presents a comprehensive case study of Taobao Live’s DianTao app, detailing business background, industry challenges, and a multi‑stage user growth framework that includes data‑driven strategies, lifecycle data systems, and specific capabilities for acquisition, retention, activation, and recall.

AcquisitionData StrategyTaobao Live

0 likes · 19 min read

Tongcheng Travel Technology Center

May 9, 2023 · Artificial Intelligence

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

This article presents EGES, a graph‑embedding model that incorporates side information to construct a directed user graph, apply biased random‑walk sampling, and train weighted Skip‑Gram embeddings, thereby improving large‑scale user acquisition and addressing cold‑start challenges in recommendation systems.

Cold StartEGESgraph embedding

0 likes · 9 min read

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

DataFunSummit

Oct 16, 2022 · Artificial Intelligence

Applying and Building LTV Models for User Growth

This article explains the concept of Lifetime Value (LTV), how it can be decomposed into Life Time and ARPU, outlines the five stages of user growth where LTV can be applied, discusses key dimensions for LTV estimation, and presents practical modeling and data‑pipeline approaches for device‑level LTV prediction.

LTVLifetime Value Modelingdata science

0 likes · 14 min read

Applying and Building LTV Models for User Growth

DataFunTalk

Aug 27, 2022 · Artificial Intelligence

User Growth Algorithms and Engineering Practices at Huya Live Streaming

This article details Huya's comprehensive user growth framework, covering the full acquisition‑activation‑retention‑revenue funnel, advertising workflow, crowd targeting stages, uplift modeling, virtual callbacks, intelligent bidding, and engineering implementations such as material automation, low‑latency RTA filtering, and dynamic strategy operators.

HuyaReal-Time Biddingadvertising algorithms

0 likes · 14 min read

User Growth Algorithms and Engineering Practices at Huya Live Streaming

ByteDance Data Platform

Aug 5, 2022 · Product Management

How ByteDance Drives User Growth from 0‑1: Models and Retention Tactics

This article reviews ByteDance’s user‑growth framework, explaining the lifecycle‑based model, MAU formula, and how the company boosts new‑user retention in the critical 0‑to‑1 stage through incentive systems, onboarding, targeted outreach, and fine‑grained analytics.

ByteDanceMAUgrowth model

0 likes · 10 min read

How ByteDance Drives User Growth from 0‑1: Models and Retention Tactics

DataFunSummit

Mar 27, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can uncover subtle correlations in large datasets, detailing user growth metrics, propensity‑score matching, causal recommendation models, heterogeneous treatment effect analysis, and practical strategies for improving retention and activity in recommendation systems.

Recommendation systemscausal inferenceheterogeneous treatment effect

0 likes · 12 min read

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

DataFunTalk

Feb 7, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can detect subtle correlations in large datasets, improve user growth metrics such as retention and activity, and presents practical methods like propensity score matching, uplift modeling, HTE analysis, and meta‑learners applied to recommendation systems.

Recommendation systemscausal inferenceheterogeneous treatment effect

0 likes · 13 min read

DataFunSummit

Nov 7, 2021 · Artificial Intelligence

How Information‑Flow Recommendation Systems Upgrade Drives User Growth

The article examines how low‑level recommendation‑algorithm improvements in information‑flow feeds can boost user retention, LTV and overall growth by addressing cold‑start challenges, survivor bias, and causal inference through personalized ranking, ecosystem construction, and multi‑task learning.

Algorithmcausal inferenceinformation flow

0 likes · 14 min read

How Information‑Flow Recommendation Systems Upgrade Drives User Growth

DataFunSummit

Jul 24, 2021 · Artificial Intelligence

Alibaba 1688 User Growth, Full‑Chain Growth System, and Deep‑Learning Applications in Search and Promotion

This article presents a comprehensive overview of Alibaba 1688's user‑growth strategy, detailing lifecycle segmentation, budget‑constrained installation optimization, intelligent red‑packet allocation, smart push mechanisms, information‑flow advertising, and the deep‑learning‑driven search pipeline that together power the platform's growth engine.

Recommendation systemsbudget optimizatione-commerce

0 likes · 20 min read

Alibaba 1688 User Growth, Full‑Chain Growth System, and Deep‑Learning Applications in Search and Promotion

JD Tech

May 9, 2021 · Artificial Intelligence

Design and Architecture of JD’s User Growth “Machine” for Scalable Intelligent Operations

The article explains how JD’s retail user growth team built a data‑driven, AI‑powered “machine” that automates user insight, operation planning, conflict resolution, external touchpoint, and real‑time strategy engines to achieve precise, large‑scale user acquisition and retention.

AIOptimizationdata-driven

0 likes · 7 min read

Design and Architecture of JD’s User Growth “Machine” for Scalable Intelligent Operations

Xianyu Technology

Apr 7, 2021 · Backend Development

Xianyu User Growth Task System Architecture and Implementation

Xianyu’s growth team built a modular user‑task system that abstracts diverse growth playbooks into a unified model defined by five dimensions, manages task lifecycles through dedicated services for periods, benefits, and scheduling, and uses strategy and template patterns to enable rapid, reusable, and efficient user‑engagement experiments.

AlibabaTask Systemarchitecture

0 likes · 6 min read

Xianyu User Growth Task System Architecture and Implementation

DataFunTalk

Dec 2, 2020 · Artificial Intelligence

How Recommendation Algorithms Drive User Growth in Content Feed Systems

This article examines how low‑level recommendation algorithm techniques can upgrade content‑feed systems to boost user growth, covering problem analysis, growth factors, personalization upgrades, cold‑start mechanisms, bias mitigation via causal inference, and utility‑driven user profiling.

Recommendation systemsalgorithm designcausal inference

0 likes · 14 min read