This guide explains how to design, track, collect, manage, analyze, and monitor product metrics to create a closed‑loop growth system, and demonstrates the process with a real‑world video‑app submission‑rate optimization case study.
The article provides a detailed technical overview of the Turing Data Finder (TDF) platform, describing its background, core components, data schema, ingestion workflow, and a suite of growth‑analysis features such as event, retention, funnel, path, component, distribution, and attribution analysis, while also outlining performance‑optimisation techniques and future development directions.
This article explains how Tencent Video leverages causal inference techniques—including uplift gain models, propensity‑score‑matching (PSM), and a distributed Spark‑based CausalML library—to identify incremental user effects, evaluate marketing interventions, and improve growth across advertising, internal flow, push notifications, and coupon strategies.
The article details Xiaomi's growth analytics platform evolution from a Lambda architecture using SparkSQL, Kudu, and HDFS to a streamlined MPP solution with Apache Doris, covering performance gains, real‑time data ingestion, query tuning, and operational improvements for large‑scale analytics.
