In the AI era the classic funnel of exposure‑click‑register‑retain‑pay no longer reflects value creation, so product managers must shift the focus to effective task entry, first usable results, mid‑funnel adoption, retention of high‑impact tasks, and stable commercial metrics.
This article details the architecture, core processes, and module designs of the JiShi BI platform, a self‑built data visualization and analysis system that integrates data ingestion, processing, enhanced AI‑driven analytics, and multi‑dimensional dashboard capabilities to support enterprise decision‑making.
This article explains how to analyze and redesign the entire conversion funnel for a home‑service platform, covering background, data goals, user‑journey models, a custom funnel framework, and concrete UI optimizations that significantly improve click‑through and lead‑submission rates.
The second article in the user‑behavior series explains ClickHouse‑based funnel analysis, covering unordered and ordered models, configuration, computation, and storage phases, key ClickHouse functions such as windowFunnel and array utilities, detailed SQL examples, and optimization strategies for real‑time, low‑cost querying.
This article explains the concept and core steps of funnel analysis, demonstrates its application with a detailed Taobao reverse‑logistics case, identifies key metrics and pain points, and offers data‑driven recommendations to improve conversion and on‑site pickup rates.
This article introduces the 5W2H analysis framework, demonstrates its application to a game’s declining retention problem, and walks through funnel analysis, user‑group profiling, and A/B testing to identify causes and design data‑driven solutions for player churn.
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.
Didi's data architecture team migrated high‑volume, real‑time funnel analysis from ClickHouse to StarRocks, built a multi‑layer pipeline with Kafka, Flink/Spark, Hive, and materialized views, and achieved sub‑3‑second query times on billions of rows, while outlining future enhancements.
This article explains the design of a high‑performance event analysis platform that ingests billions of daily logs, supports event, funnel, and retention queries, and leverages ClickHouse for storage, efficient writes, and fast analytical queries across massive datasets.
This talk explores how to conduct user behavior analysis on massive data sets, compares existing analytics tools, and presents Alibaba Dataworks' end‑to‑end solution—including funnel and link visualizations, a big‑data processing architecture, and future intelligent link capabilities—to uncover and resolve user‑experience issues efficiently.
This article details how Beike Zhaofang leveraged the ClickHouse columnar OLAP database for large‑scale user behavior analysis, covering its architecture, key features, performance benchmarks against other engines, data ingestion pipelines, custom UDFs for funnel and retention metrics, deployment setup, and future enhancements.
The article explains how to move beyond macro app metrics by collecting offline and real‑time user data, storing it in HDFS, processing it with Spark, visualizing behavior paths as state‑machine trees, and performing branch‑funnel analysis to uncover conversion bottlenecks and improve product quality.
