漳州地区课程老师方案生成数量预测数据

课程老师作为在线教育企业课程推广与销售的核心力量，其业务成效直接关系到在线教育企业的发展。围绕漳州地区课程老师上月的6项关键指标 —— 云产品总分享数（次）、云产品总浏览数（次）、云产品有效访问人数（次），云共享总分享数（次）、云共享总浏览数（次）、AI 客服总咨询数（次）来建立本月预测方案生成数量（个）的预测模型。该模型通过深度挖掘在线教育行业企业课程老师的行为数据，精准预测学习课程方案生成量。对行业企业而言有助于优化资源配置，提升老师能力：为预测高方案生成量的老师提前匹配流量、技术支持等资源，对预测方案生成少的老师，结合其分享量低、浏览数据差等问题，开展定向培训（如云共享传播技巧、高浏览内容设计），针对性提升传播动能。对于在线教育行业相关企业，可据此深入理解课程推广与销售的关系，推动课程老师转发、分享课程，优化客服话术、推送高转化课程包，为将互动转化为付费与续费，显著提升转化效率提供数据支持。1、数据来源于本企业内部，通过采集：分析时间、课程老师ID、地区、数据分析时间段、云产品总分享数（次）、云产品总浏览数（次）、云产品有效访问人数（次），云共享总分享数（次）、云共享总浏览数（次）、AI 客服总咨询数（次）建立方案预测模型，来计算课程老师本月预测方案生成数量（个）。
2、对采集到的数据进行脱敏、清洗、去除异常值。建立本月预测方案生成数量（个）模型。
本月预测方案生成数量（个）=0.791 - 0.053*云产品总分享数（次） + 0.185*云产品总浏览数（次） - 0.131*云产品有效访问人数（次） + 0.176*云共享总分享数（次） + 0.043*云共享总浏览数（次） + 0.258*AI客服总咨询数（次） 。
3、此模型有助于所有在线教育行业企业运营策划。为在线教育行业的稳健发展提供数据支持。

Course teachers serve as the core driving force for course promotion and sales of online education enterprises, and their business performance is directly correlated with the development of such enterprises. This study constructs a prediction model for the monthly number of generated course plans using 6 key metrics of course teachers in Zhangzhou region from the previous month: total count of cloud product shares, total count of cloud product views, total count of valid cloud product visitors, total count of cloud share shares, total count of cloud share views, and total count of AI customer service inquiries.

By deeply mining the behavioral data of course teachers from online education enterprises, this model can accurately predict the volume of generated course plans. For enterprises in the industry, the model contributes to optimizing resource allocation and enhancing teachers' competencies: pre-allocating resources such as traffic and technical support for teachers predicted to have high volumes of generated course plans; for teachers with low predicted plan generation volumes, carrying out targeted training (e.g., cloud share communication skills, design of high-view content) based on their low share counts and poor browsing performance, so as to boost their communication momentum. For online education enterprises, this model enables a deeper understanding of the relationship between course promotion and sales, facilitates teachers' forwarding and sharing of courses, optimizes customer service scripts, and pushes high-conversion course packages, providing data support for converting interactions into payments and renewals, thereby significantly improving conversion efficiency.

1. Data sources: All data is collected from the internal operations of our enterprise. Collected fields include analysis time, course teacher ID, region, data analysis time period, total count of cloud product shares, total count of cloud product views, total count of valid cloud product visitors, total count of cloud share shares, total count of cloud share views, and total count of AI customer service inquiries. A plan prediction model is established to calculate the predicted number of generated course plans for teachers in the current month.
2. Data preprocessing: The collected data undergoes desensitization, cleaning, and outlier removal. The prediction model for the monthly number of generated course plans is formulated as follows:
Predicted number of generated course plans this month = 0.791 - 0.053 * Total count of cloud product shares + 0.185 * Total count of cloud product views - 0.131 * Total count of valid cloud product visitors + 0.176 * Total count of cloud share shares + 0.043 * Total count of cloud share views + 0.258 * Total count of AI customer service inquiries
3. Application value: This model is applicable to the operation and planning of all online education enterprises, providing data support for the steady development of the online education industry.

搜集汇总

背景与挑战

背景概述

该数据集聚焦漳州地区在线教育课程老师，包含551条月度记录，通过云产品分享浏览、云共享活动及AI咨询等6项行为指标，使用线性回归模型预测方案生成数量，旨在帮助企业优化资源配置和教师能力提升，推动教育服务转化。