A comprehensive glossary for e‑commerce product managers that defines key metrics such as GMV, SKU, UV, CVR, AOV, CPC, and models like RFM and AARRR, while highlighting common pitfalls and practical strategies for data‑driven decision making.
The Funnel Model breaks down any process into sequential stages, measures entry and exit numbers at each step, calculates stage and overall conversion rates, and reveals where the greatest losses occur, enabling data‑driven optimization for e‑commerce, management, and other applications.
AB testing is a statistical method for comparing multiple strategies or versions to determine the most effective one, and this article explains its risks, mitigation measures, advantages, key dimensions, step‑by‑step workflow, fairness considerations, data‑cleaning techniques, target goals, design guidelines, and alternative experimental approaches.
Screen efficiency measures the commercial value generated per unit screen area; by optimizing floor placement, increasing effective information ratio, arranging slots strategically, and improving content presentation, e‑commerce pages can significantly raise exposure UV and GMV, as illustrated through data‑driven examples and visual analyses.
This article explains the concept of conversion rate, its significance for product success, and presents a systematic, full‑process approach to identify and optimize key decision points—covering user and scenario analysis, metric definition, business and user research, and concrete design strategies for entry, detail, and submission stages.
This article presents Beike's experience in intelligent real‑estate advertising, detailing the business background, key technical challenges such as conversion‑rate estimation, delayed‑feedback modeling, GEO targeting, and budget allocation, and sharing practical solutions that improved conversion rates by over 10%.
This article presents Beike's experience in applying intelligent advertising to the real‑estate industry, detailing business background, key technical challenges, multi‑task conversion modeling, GEO user targeting, delayed‑feedback calibration, and ROI‑driven budget allocation to improve ad effectiveness.
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.
The Adaptive Information Transfer Multi‑task (AITM) framework integrates multi‑task learning with an attention‑based information‑transfer module to jointly model the sequential conversion chain in targeted display ads, mitigating class imbalance and boosting end‑to‑end user acquisition rates, as demonstrated by offline and online experiments.
This article introduces the motivation and logic behind A/B testing, highlights common misunderstandings of p‑values, and demonstrates how Bayesian A/B testing using PyMC3 can provide intuitive probability statements about which variant performs better, complete with Python code examples.
Over six months, 58.com revamped the category pages for its rental and home services, introducing clear metrics like connection conversion rate and redesigning navigation to prioritize top services, which resulted in a three‑fold increase in order and contact conversions across both platforms.
This article explains the purpose and types of data metrics, outlines a logical framework for analyzing e‑commerce performance from traffic to behavior to transaction, and details ten key metrics—including GMV, conversion rate, UV value, click‑through and exposure rates—along with practical interpretation tips.
This article describes the design and implementation of a comprehensive efficiency‑optimization platform at 58.com, detailing its end‑to‑end workflow—from log aggregation and feature extraction through machine learning model training and online experimentation—highlighting modular, configurable, and scalable solutions for multi‑business, multi‑product ranking.
Meituan Scan-to-Pay mini program optimized by reducing package size through split and independent sub‑packages, moving assets to CDN, and adding detailed funnel, exception, performance, and cross‑boundary timing monitoring, which together increased external conversion by about 3 % and improved overall payment success rates.
Learn how A/B testing and multivariate testing can identify the most effective UI designs, improve user experience, and increase conversion rates, while exploring essential tools such as Google Optimize, Crazy Egg, and Optimizely, and best practices for planning, executing, and analyzing experiments.
This article introduces two Python baselines—one using historical average conversion rates and another applying one‑hot encoding with logistic regression—to demonstrate feature engineering and model training for an app advertising conversion rate prediction contest.
The article explains how Ctrip’s flight‑booking team uses AB testing—from definition and experimental design to data collection, traffic allocation, orthogonal experiments, and result analysis—to drive conversion‑rate and revenue improvements across multiple platforms.
The article explains the goals of user behavior analysis, details two implementation methods for single-step and multi-step path conversions using log preprocessing, adjacency matrices, and KMP pattern matching, and illustrates these techniques with a Baidu Doctor product line case study.
