综合财务管理软件由资金追踪功能触发的行为路径偏好数据

本数据的应用场景包括：1.软件功能改进和用户体验优化：通过分析综合财务管理软件由资金追踪功能触发的行为路径的用户偏好，有助于开发人员判断软件的用户界面、功能设置、功能布局的合理性及有效性，为本公司团队和其他软件公司（当需开发类似软件时）围绕资金追踪功能开展产品优化工作提供依据。2.提升客户支持和服务的针对性：基于由资金追踪功能触发的行为路径的用户偏好数据，可以有针对性地向用户提供技术支持和培训服务，有助于提高用户满意度，减少用户流失，增强用户忠诚度。3.促进软件安全和风险管理：信息安全管理团队可在本数据的基础上，利用其他算法进一步对用户行为路径的异常模式进行监测，从而提前发现潜在的安全威胁。1.数据采集和预处理：（1）从公司自营综合财务管理软件用户行为日志中，实时提取由资金追踪功能触发的用户行为路径数据，包括用户ID、触发功能、行为路径、发生时间。（2）对提取的数据进行清洗，移除无效或错误的记录。
2、计算支持度：（1）基于历史数据，利用数据透视表快速计算所有由资金追踪功能触发的行为路径的总数量。（2）对于由资金追踪功能触发的每种行为路径，基于历史数据利用COUNTIF函数计算其在所有行为路径总数量中出现的次数，形成集合数据X。（3）每种行为路径支持度=每种行为路径出现次数/所有行为路径总数量；形成集合数据Y。
3.频繁路径挖掘：对于每种行为路径（集合数据Y），应用Apriori算法来挖掘频繁出现的行为路径模式（通过设定支持度阈值来提高挖掘的准确性），形成集合数据Z。
4.行为路径偏好识别：根据Apriori算法挖掘出的频繁路径（集合数据Z），结合业务逻辑识别用户偏好的行为路径，即典型行为路径，形成集合数据W。
5.路径排序和输出：按支持度大小对典型行为路径（集合数据W）进行降序排序，并将排序前三的进行可视化输出。

Application scenarios of this dataset include: 1. Software function improvement and user experience optimization: By analyzing user preferences of user behavior paths triggered by the funds tracking feature of the company's self-operated comprehensive financial management software, developers can judge the rationality and effectiveness of the software's user interface, function settings and function layout, providing a basis for our company's team and other software companies (when developing similar software) to carry out product optimization work around the funds tracking feature. 2. Enhancing targeted customer support and services: Based on user preference data of user behavior paths triggered by the funds tracking feature, targeted technical support and training services can be provided to users, which helps improve user satisfaction, reduce user churn, and enhance user loyalty. 3. Promoting software security and risk management: Based on this dataset, the information security management team can use other algorithms to further monitor abnormal patterns of user behavior paths, so as to detect potential security threats in advance.

1. Data collection and preprocessing:
(1) Extract user behavior path data triggered by the funds tracking feature in real time from the user behavior logs of the company's self-operated comprehensive financial management software, including user ID, triggered function, behavior path, and occurrence time.
(2) Clean the extracted data and remove invalid or erroneous records.
2. Support Degree Calculation:
(1) Based on historical data, quickly calculate the total number of all user behavior paths triggered by the funds tracking feature using a pivot table.
(2) For each type of user behavior path triggered by the funds tracking feature, use the COUNTIF function based on historical data to calculate the number of its occurrences in the total number of all behavior paths, forming dataset X.
(3) Support degree of each behavior path = number of occurrences of each behavior path / total number of all behavior paths; forming dataset Y.
3. Frequent Path Mining: For each type of user behavior path (dataset Y), apply the Apriori algorithm to mine frequently occurring behavior path patterns (set a support degree threshold to improve mining accuracy), forming dataset Z.
4. User Behavior Path Preference Identification: According to the frequent paths (dataset Z) mined by the Apriori algorithm, combine with business logic to identify user-preferred behavior paths, namely typical behavior paths, forming dataset W.
5. Path Sorting and Output: Sort the typical behavior paths (dataset W) in descending order according to their support degrees, and visualize and output the top three paths.

搜集汇总

数据集介绍

特点

该数据集记录了综合财务管理软件中由资金追踪功能触发的用户行为路径偏好数据，包含用户ID、触发功能、行为路径等关键字段，数据规模为922条，每日更新。应用场景包括软件功能改进、客户支持和服务针对性提升以及软件安全和风险管理。