产品策划必看！常用的数据分析模型有哪些？2026年进阶指南

Core Insights - The article emphasizes the importance of data analysis in product planning, stating that intuition is no longer sufficient in a rapidly changing market environment as companies approach 2026 [2] - It introduces various data analysis models that can significantly aid product managers in making informed decisions throughout the product lifecycle [2] Group 1: Understanding User Behavior - The first step in product planning is to understand user flow, addressing questions like where users come from, where they go, and why they leave [4] - The AARRR model (Pirate Metrics) is highlighted as a classic framework for structuring product business flows, helping to identify which stage of user engagement a new feature serves [5][6] - Funnel Analysis is described as a process-oriented model that reveals user behavior and conversion rates at each stage, allowing for precise identification of user drop-off points [7] Group 2: User Value and Segmentation - The RFM model is introduced as a tool for measuring user value, particularly useful for e-commerce and O2O products, allowing for user segmentation based on recency, frequency, and monetary value [9][10] - User profiling and clustering techniques are discussed, emphasizing the need to categorize users into distinct segments for targeted marketing strategies [12] Group 3: Prioritizing Features - The KANO model categorizes user needs into three types, helping product managers prioritize features effectively while ensuring basic requirements are met [15][20] - The Four-Quadrant Analysis is mentioned as a straightforward yet effective method for prioritizing tasks based on urgency and importance [16] Group 4: Analyzing Performance - The DuPont Analysis is presented as a method for breaking down key performance indicators (KPIs) to identify the root causes of performance issues [21] - Cohort Analysis is described as an advanced retention analysis technique that examines the performance of users acquired during the same time period, providing insights into product iterations [22] Group 5: Professional Development in Data Analysis - The article stresses the necessity for product managers to develop systematic data analysis skills, particularly in light of the increasing reliance on data-driven decision-making in the industry [24] - The CDA (Certified Data Analyst) certification is highlighted as a highly recognized credential that equips professionals with essential data analysis skills applicable across various industries [25][26] - The CDA certification is noted for its practical focus, integrating business intelligence and technical skills, making it relevant for product managers [27] Group 6: Career Opportunities - Holding a CDA certification can significantly enhance career prospects, with many leading companies prioritizing candidates with this qualification [28] - The article outlines various career paths available to CDA holders, including roles in data product management, data analysis, and market research [28]