BestHub
Sign in
Tag

rfm

1 views collected around this technical thread.

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.

Data AnalysisPythonSQL
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.

AIMachine Learningautomated feature engineering
0 likes · 14 min read
Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study
Python Programming Learning Circle
Python Programming Learning Circle
Dec 19, 2020 · Fundamentals

Implementing RFM Customer Value Analysis with Python and Pandas

This article demonstrates how to use Python's time, NumPy, and Pandas libraries to build an RFM (Recency, Frequency, Monetary) model for customer value segmentation, covering data preparation, score calculation, weighting strategies, and exporting the results for marketing insights.

Data Analysiscustomer-segmentationpandas
0 likes · 5 min read
Implementing RFM Customer Value Analysis with Python and Pandas
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Apr 2, 2020 · Fundamentals

Improving the Usefulness of Data Analysis Reports: Finding Standards for Static Data

This article explains why many static data reports are ineffective, identifies the lack of judgment standards as the core issue, and offers three practical ways—problem‑based, goal‑based, and business‑based—to establish meaningful standards that make data analysis reports valuable.

Business IntelligenceData Analysisjudgment standards
0 likes · 7 min read
Improving the Usefulness of Data Analysis Reports: Finding Standards for Static Data