短剧平台在线客服功能使用强度预测数据

对本公司的用途
1.通过分析在线客服功能的使用强度预测数据，识别用户咨询的高峰时段，合理安排客服人员，提升用户满意度。
2.若在线客服功能使用强度持续增长，可探索增值服务(如付费优先客服服务)，提升变现能
对同行(其他短剧平台开发者)的用途:
1.对比行业数据，评估在线客服功能的用户接受度，判断是否值得投入开发类似功能2.数据可作为行业报告基础，推动短剧平台在线客服服务的标准化，促进行业良性发展。1.数据采集和预处理： （1）从公司开发的短剧平台用户行为日志中，每隔一小时提取反映用户使用“在线客服”情况的数据，包括采集时间段、是否为工作时间（工作时间：周一到周五的9:00-17:00）、功能名称、该小时活跃用户数A/人、该小时使用频次B、该小时使用总时长C/min、该小时软件总活跃用户数D/人。（2）对采集的数据进行清洗，以便后续的加工和建模。
2.建立提现功能的用户使用强度计算模型： （1）计算功能该小时使用率S：S=A÷D×100%；（2）引入时间加权因子q：若为工作时间，q为1.2，反之为0.8；（3）计算该小时加权平均使用时长Pq：Pq=C÷A×q；（4）计算该小时用户活跃度指数H：H=√[A×(B×q)]；（5）建立使用强度W计算模型：W=0.5H+0.25Pq+0.25S；权重系数依据每个指标的重要性并结合行业经验确定。
3.建立使用情况预测模型： （1）用rolling函数统计过去24小时和72小时的总使用强度，在此基础上分别除以24和72，计算过去24小时和72小时的小时均使用强度Wa、Wb；（2）基于Wa和Wb进行预测：未来12小时使用强度预测值=Wa×12，为短期预测；未来24小时使用强度预测值=Wb×24，为中期预测。

Purposes for the Company:
1. Analyze the usage intensity forecast data of the "online customer service" function to identify peak hours of user inquiries, reasonably arrange customer service staffing, and improve user satisfaction.
2. If the usage intensity of the "online customer service" function continues to grow, explore value-added services (such as paid priority customer service) to improve monetization capabilities.

Purposes for Peers (Other Short Drama Platform Developers):
1. Compare with industry data to evaluate user acceptance of the "online customer service" function, and judge whether it is worth investing in developing similar functions.
2. The data can serve as a basis for industry reports, promote the standardization of online customer service services for short drama platforms and foster the healthy development of the industry.

1. Data Collection and Preprocessing:
(1) Extract data reflecting users' use of the "online customer service" function from the user behavior logs of the short drama platform developed by the company every hour, including the collection time period, whether it is working hours (working hours: 9:00-17:00 on weekdays, function name, hourly active user count A / person, hourly usage frequency B, total usage duration C / min, total hourly active users of the software D / person.
(2) Clean the collected data for subsequent processing and modeling.

2. Establish a user usage intensity calculation model for the "online customer service" function:
(1) Calculate the hourly usage rate S: S = A ÷ D × 100%;
(2) Introduce a time weighting factor q: 1.2 if it is working hours, otherwise 0.8;
(3) Calculate the hourly weighted average usage duration Pq: Pq = C ÷ A × q;
(4) Calculate the hourly user activity index H: H = √[A × (B × q)];
(5) Establish the usage intensity W calculation model: W = 0.5H + 0.25Pq + 0.25S; the weight coefficients are determined based on the importance of each indicator combined with industry experience.

3. Establish a usage status prediction model:
(1) Use the rolling function to count the total usage intensity of the past 24 hours and 72 hours, and divide by 24 and 72 respectively to calculate the hourly average usage intensity Wa and Wb for the past 24 hours and 72 hours;
(2) Predict based on Wa and Wb: The 12-hour future usage intensity forecast value = Wa × 12, which is a short-term forecast; The 24-hour future usage intensity forecast value = Wb × 24, which is a medium-term forecast.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于短剧平台在线客服功能的使用强度预测，包含526条每小时更新的企业数据，记录了活跃用户数、使用频次、时长等原始指标，并通过算法模型计算了用户活跃度指数、使用强度等衍生指标，以及未来12小时和24小时的使用强度预测值。数据集旨在通过分析用户行为模式，识别咨询高峰时段，优化客服资源配置，并为行业提供标准化参考，支持短期和中期预测以提升服务效率和变现能力。