济南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。围绕济南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 are the core force for course promotion and sales of online education enterprises, and their business performance is directly related to the development of such enterprises. Focusing on 6 key indicators of course teachers in Jinan region from last month: total number of cloud product shares (times), total number of cloud product views (times), total number of valid visitors to cloud products (times), total number of cloud sharing shares (times), total number of cloud sharing views (times), and total number of AI customer service inquiries (times), a prediction model for the predicted number of course plan generation this month is established.

This model accurately predicts the volume of course plan generation by deeply mining the behavioral data of course teachers from online education enterprises. For enterprises in the industry, it helps optimize resource allocation and improve teachers' capabilities: match resources such as traffic and technical support in advance for teachers predicted to have high plan generation volume; for teachers with low predicted plan generation, carry out targeted training (such as cloud sharing communication skills, high-traffic content design) based on their low share volume and poor browsing data, so as to comprehensively improve communication momentum. For enterprises related to the online education industry, it can help them deeply understand the relationship between course promotion and sales, promote teachers to forward and share courses, optimize customer service scripts, push high-conversion course packages, and provide data support for converting interactions into payments and renewals, thereby significantly improving conversion efficiency.

1. The data comes from the internal sources of the enterprise. By collecting analysis time, course teacher ID, region, data analysis time period, total number of cloud product shares (times), total number of cloud product views (times), total number of valid visitors to cloud products (times), total number of cloud sharing shares (times), total number of cloud sharing views (times), and total number of AI customer service inquiries (times), a plan prediction model is established to calculate the predicted number of course plan generation for teachers this month.
2. The collected data is desensitized, cleaned, and outliers are removed. The prediction model for the number of course plan generation this month is formulated as follows:
Predicted number of course plan generation this month = 0.791 - 0.053 * total number of cloud product shares (times) + 0.185 * total number of cloud product views (times) - 0.131 * total number of valid visitors to cloud products (times) + 0.176 * total number of cloud sharing shares (times) + 0.043 * total number of cloud sharing views (times) + 0.258 * total number of AI customer service inquiries (times).
3. This model is applicable to the operation and planning of all online education industry enterprises, and provides data support for the steady development of the online education industry.

搜集汇总

背景与挑战

背景概述

该数据集聚焦济南地区在线教育课程老师的业务数据，包含551条记录，每月更新，通过6项关键指标（如云产品分享数和AI客服咨询数）预测本月方案生成数量，旨在帮助企业优化资源配置和提升老师能力，推动教育行业转化效率提升。