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论文编号：	15747
作者编号：	2320233856
上传时间：	2025/12/9 19:06:40
中文题目：	G公司基于用户数据的个性化学习产品定价策略研究
英文题目：	Research on Pricing Strategy of Personalized Learning Products Based on User Date for Company G
指导老师：	王玮
中文关键字：	定价策略；个性化学习；用户行为建模；用户分群；教育科技
英文关键字：	Differential Pricing; Personalized; User Behavior Modeling; User segmentation; Educational technology
中文摘要：	在“双减”政策驱动教育转型与教育科技智能化发展的背景下，智能硬件产品成为提升学生个性化学习效能的关键载体。作为行业标杆企业，G公司通过其智能点阵笔这一创新产品，实时采集学生笔迹行为、答题过程与结果数据，为个性化学习服务奠定基础。然而，海量用户的行为差异性与需求动态性导致传统统一定价策略难以实现资源最优配置，亟须建立以学习行为为导向的用户分群机制及个性化定价体系。为此，本文提出以用户行为洞察驱动的分群与个性化定价思路，试图在保障学习成效与平台可持续的前提下，实现学习资源的精准匹配与用户价值的提升。 本文以G公司面向智能教育平台的个性化服务场景为背景，以点阵笔项目的用户行为大数据为依托，创新性地构建由“聚类分析、个性化产品服务、定价策略”三部分组成的一体化的商业策略框架：首先，基于用户的笔迹行为与历史支付行为，构建非监督学习的用户行为分群模型，提取学习活跃度与支付能力的结构特征，得到可解释的行为画像；然后，设计基于题目行为特征的知识点掌握度建模方法，识别用户薄弱知识点集合；在此基础上，结合服务等级价值与推荐时长构造三元组定价组合，估算个体推荐的产品服务价格；最后，引入收益结构函数对推荐组合的利润空间与成本结构进行评估，辅助平台优化定价策略。并用小规模实验的方法通过采用真实的平台日数据，对个性化产品定价策略进行并行测试评估，并在转化、收入、利润与人均产出等关键指标上进行多维对比，同时开展分层与稳健性分析，以验证策略在真实业务环境中的可实施性。 研究发现，个性化组合定价在综合表现上明显优于统一定价，主要表现在：转化效率显著提升，平台收入与利润同步扩张，单用户价值呈现成倍级改善，单位经济结构更稳健。该结果说明“由行为到画像、由画像到服务与价格”的链路具有工程可行性与经济有效性，契合行为定价的理论逻辑。同时，从未来推广潜力来看，本模型在在线教育、在线健康管理、在线健身等多类平台中具备迁移潜力，并在模型优化与动态策略协同方面提出了未来研究方向。
英文摘要：	Against the backdrop of the “Double Reduction” policy driving educational transformation and the intelligent development of edtech, smart hardware has become a pivotal vehicle for enhancing the effectiveness of personalized learning. As an industry-leading company, G Company leverages its innovative intelligent dot-matrix pen to capture, in real time, students’ handwriting behaviors as well as their problem-solving processes and outcomes, thereby laying the data foundation for personalized learning services. However, the substantial heterogeneity of user behaviors and the dynamism of user needs render uniform pricing strategies inadequate for optimal resource allocation. It is therefore necessary to establish a learning-behavior-oriented user segmentation mechanism and a personalized pricing system. To this end, this study proposes a behavioral-insights-driven approach to segmentation and personalized pricing that seeks to achieve precise matching of learning resources and enhancement of user value without compromising learning outcomes or platform sustainability. Situated in G Company’s personalized service scenario for an intelligent education platform and drawing on large-scale behavioral data from the dot-matrix pen project, this study develops an integrated business strategy framework comprising three components—clustering analysis, personalized product/service design, and pricing strategy. First, an unsupervised user behavior clustering model based on handwriting and historical payment behaviors is constructed to extract structural features of learning activeness and payment capacity, yielding interpretable behavioral profiles. Second, a knowledge-mastery modeling method grounded in item-level behavioral features is designed to identify each user’s set of weak knowledge points. Building on this, a triplet pricing configuration is formulated by combining service-tier value and recommended duration to estimate individual-level recommended prices for product/service bundles. Finally, a revenue-structure function is introduced to evaluate the profit potential and cost structure of the recommended bundles, thereby supporting platform-level pricing optimization. Using small-scale experiments with real daily platform data, the personalized product-pricing strategy is tested in parallel and evaluated through multi-dimensional comparisons on key metrics such as conversion, revenue, profit, and per-user output, alongside stratified and robustness analyses to verify its practical feasibility in a live business environment. The findings indicate that personalized bundled pricing outperforms uniform pricing in overall performance: conversion efficiency improves markedly, platform revenue and profit expand in tandem, per-user value increases substantially, and unit economics become more resilient. These results demonstrate that the pipeline “from behavior to profiles, and from profiles to services and prices” is both engineering-feasible and economically effective, aligning with the theoretical logic of behavior-based pricing. Looking ahead, the proposed model shows transferability to multiple platform types—including online education, online health management, and online fitness—while future research can further explore model optimization and the coordination of dynamic strategies.
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