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Python Programming Learning Circle
Python Programming Learning Circle
Sep 8, 2025 · Big Data

Unlocking E‑Commerce Insights: How Python & SQL Reveal User Behavior and Boost Sales

This article analyzes a JD e‑commerce dataset using Python and MySQL to calculate key metrics such as PV, UV, conversion rates, attrition, daily activity, hourly trends, user‑behavior funnels, purchase intervals, retention rates, product sales, and RFM segmentation, and then offers data‑driven recommendations to improve traffic, conversion, and user loyalty.

PythonRFMSQL
0 likes · 37 min read
Unlocking E‑Commerce Insights: How Python & SQL Reveal User Behavior and Boost Sales
Python Programming Learning Circle
Python Programming Learning Circle
Nov 22, 2023 · Big Data

E‑commerce User Behavior Analysis and KPI Modeling with Python and SQL

This study analyzes JD e‑commerce operational data from February to April 2018, employing Python and SQL to compute key metrics such as PV, UV, conversion rates, attrition, purchase frequency, time‑based behavior, funnel analysis, retention, product sales, and RFM segmentation, and provides actionable recommendations for improving user engagement and sales performance.

MetricsRFMSQL
0 likes · 30 min read
E‑commerce User Behavior Analysis and KPI Modeling with Python and SQL
DataFunTalk
DataFunTalk
Dec 28, 2022 · Artificial Intelligence

Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study

This article explains how DataFun’s automated feature engineering and modeling platform dramatically reduces credit‑risk model development time from weeks to days by standardizing feature creation, integrating popular algorithms such as LR, XGBoost and LightGBM, and providing comprehensive evaluation, deployment and monitoring capabilities.

AIModel MonitoringRFM
0 likes · 14 min read
Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study
21CTO
21CTO
Mar 30, 2022 · Big Data

What Drives Taobao App Users? Insights from AARRR and RFM Analyses

This article analyzes 2 million Taobao app user‑behavior records using AARRR funnel metrics and RFM segmentation, revealing daily and hourly usage patterns, conversion bottlenecks, product‑search mismatches, and offering data‑driven marketing recommendations to boost retention and sales.

AARRRBig DataRFM
0 likes · 25 min read
What Drives Taobao App Users? Insights from AARRR and RFM Analyses
Alimama Tech
Alimama Tech
Nov 17, 2021 · Industry Insights

How to Build and Analyze Consumer Asset Models for Precise Marketing

This article explains the background of shrinking traffic dividends, defines consumer equity and user segmentation, introduces common models such as AIPL and RFM, outlines step‑by‑step methods for behavioral, value, flow and attribution models, and provides real‑world case studies to illustrate how marketers can evaluate asset changes and optimize channel contributions.

AIPLRFMattribution model
0 likes · 14 min read
How to Build and Analyze Consumer Asset Models for Precise Marketing
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 15, 2020 · Big Data

Unlock Insights from 3.4GB Brazilian Car Service Sales Data with Python & Tableau

This article walks through a comprehensive analysis of a 3.43 GB sales dataset from a Brazilian automotive service chain, covering data loading, cleaning, exploratory visualizations, time‑series forecasting with ARIMA, RFM customer segmentation, product clustering, and key business insights using Python and Tableau.

ARIMACustomer SegmentationPython
0 likes · 28 min read
Unlock Insights from 3.4GB Brazilian Car Service Sales Data with Python & Tableau
Python Crawling & Data Mining
Python Crawling & Data Mining
Jul 2, 2020 · Big Data

How to Identify Top Bilibili Creators Using the IFL Model: A Data‑Driven Guide

This article presents a complete data‑analysis workflow that scrapes Bilibili video metrics from January 2019 to March 2020, cleans and preprocesses 50,130 records, and extends the classic RFM model into an IFL framework—calculating interaction, frequency and like rates—to score and rank up‑creators across multiple categories, with code and datasets provided for replication.

IFL modelPythonRFM
0 likes · 11 min read
How to Identify Top Bilibili Creators Using the IFL Model: A Data‑Driven Guide
dbaplus Community
dbaplus Community
Dec 25, 2015 · Artificial Intelligence

Detecting Fraudulent ModemPOOL Terminals with K‑Means Clustering

This article details how telecom operators can identify fraudulent ModemPOOL (cat‑pool) terminals and predict churn using data‑driven clustering and day‑interval warning models, covering metric selection, data exploration, k‑means clustering, model deployment, and performance evaluation.

K-MeansModel DeploymentRFM
0 likes · 18 min read
Detecting Fraudulent ModemPOOL Terminals with K‑Means Clustering