This article explains how enterprises can construct, design, and manage comprehensive tag systems—from foundational concepts and core design principles to construction workflows, evaluation methods, industry case studies, and practical Q&A—enabling precise customer segmentation and data‑driven marketing.
This article introduces ten data‑mining applications for marketing—including customer segmentation, purchase forecasting, market‑basket analysis, churn prediction, sentiment analysis, response modeling, recommendation systems, brand reputation, competitive analysis, and public‑opinion monitoring—each illustrated with concise Python code examples.
This article explores how retailers can leverage big‑data analytics across people, products, and places—both offline and online—to build comprehensive indicator systems, apply methods like ABC, RFM, association and funnel analysis, and drive smarter decision‑making in the evolving retail landscape.
RFM analysis is a powerful tool for understanding customer value and behavior, enabling businesses to optimize marketing strategies, improve customer satisfaction, and increase business revenue through data-driven customer segmentation.
This article explains why understanding a business model is essential for executives, designers and product teams, introduces the Business Model Canvas as a visual tool, walks through each of its nine blocks with a used‑car case study, and shows how to apply the framework to improve product strategy, resource allocation and revenue planning.
Data analysis must be tightly linked to business needs, as illustrated by a Pareto analysis of a food specialty store that reveals a 55% user contribution to 80% sales, prompting strategic decisions on customer segmentation, value distribution, and growth tactics beyond traditional 20/80 expectations.
This article explains the concept of user profiling, outlines common dimensions and tag structures, discusses its importance in marketing, finance and product design, and provides a step‑by‑step methodology for constructing, modeling, and applying user profiles in real‑world scenarios.
This article examines how financial institutions can manage risk and improve operations for existing loan customers by analyzing client flow, regulatory impacts, accelerated deterioration, and layered segmentation, and by applying advanced models such as rule‑based alerts, B‑card scoring, LSTM, and survival analysis to enable timely risk detection and targeted cross‑selling.
This article provides a comprehensive overview of user profiling, covering its definition, the five‑dimensional framework (goal, method, organization, standards, validation), various tag classifications, tag‑system architecture, modeling techniques, practical applications such as precise marketing and product innovation, and a step‑by‑step guide for building a profiling system using big‑data and AI methods.
This article explains what user profiling is, why it is essential for businesses, outlines practical steps to build accurate user profiles, showcases various application scenarios, and provides references for further study, offering a comprehensive overview for data‑driven product and marketing teams.
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.
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.
This article explains what a business model is, outlines its nine essential modules—including customer segmentation, value proposition, channels, customer relationships, revenue streams, core resources, key activities, partnerships, and cost structure—and offers practical insights on how organizations can design and evaluate effective models.
This article explains the concept, importance, and step‑by‑step methodology for constructing user and product profiles, illustrating how data‑driven segmentation, static and dynamic attributes, and matching techniques can enhance product experience, marketing, and operational decisions across e‑commerce platforms.
This article presents a data‑driven personalized red‑envelope marketing solution that cleans and selects features, builds consumption‑demand and red‑envelope‑sensitivity logistic regression models, and iteratively optimizes parameters to lower costs while boosting cross‑project new‑customer conversion rates.
The article presents Ping An Bank's AI-driven data platform, covering business background, architectural layers, algorithmic applications such as customer segmentation, portrait, business forecasting, and graph analysis, and shares practical insights on platform design, model deployment, and the role of data product managers.
This article provides a comprehensive overview of customer segmentation, detailing its definition, multi‑dimensional challenges, seven implementation guidelines, five systematic steps, ten frequent pitfalls, and a practical RFM model case study using big‑data mining techniques.
