数智营销场景行业发展趋势数据

对个推累计的APP应用数据进行数据处理、人工打标等，对app资产进行分类分级，并依托 数据的汇报，结合数智营销场景的不同行业完成不同行业趋势的洞察，助力品牌方、APP运营者明晰行业趋势走向并提供app运营决策依据在自研的每日治数平台上，结合自然语言平台打标和模型训练，完成APP维表构建以及APP日活、趋势等资产构建。
一、数据抽取、清理和处理
数据抽取：从数据源中抽取与APP相关的数据。
数据清理：去除重复、无效或错误的数据，处理缺失值，确保数据的准确性和一致性。
数据处理：对数据进行标准化、转换和聚合等操作，以便后续分析和建模。
二、数据仓库层建设
1.数据模型设计
2.ETL过程
3.数据仓库优化
三、自然语言平台打标和模型训练
自然语言平台打标：利用自然语言处理技术，对APP相关的文本数据进行打标，提取出有用的信息，如APP的名称、功能描述等。
模型训练：基于打标后的数据，训练机器学习模型，用于后续APP特征的提取和分类。
四、APP维表构建
特征提取：结合自然语言平台打标和模型训练的结果，提取APP的关键特征，如功能、类别等。
维表设计：根据提取的特征，设计APP维表的结构和字段，确保能够全面、准确地描述APP的信息。
维表填充：利用提取的特征数据，填充APP维表，构建完整的APP维度数据。
五、结合个推 数据能力完成APP日活、趋势等资产构建。

This dataset is developed based on accumulated APP application data from GeTui, involving data processing, manual annotation, classification and grading of APP assets. Leveraging data reports, it conducts industry trend insights across various sectors in the intelligent digital marketing scenario, to assist brands and APP operators in clarifying industry trends and providing decision-making supports for APP operations. Built on the self-developed Daily Data Governance Platform, this work completes the construction of APP dimension tables and APP-related assets such as daily active users (DAU) and trend metrics by combining natural language platform annotation and model training.

1. Data Extraction, Cleaning and Processing
- Data extraction: Extract APP-related data from various data sources.
- Data cleaning: Remove duplicate, invalid or erroneous data, and handle missing values to ensure data accuracy and consistency.
- Data processing: Conduct standardization, transformation and aggregation operations on the data to support subsequent analysis and modeling.

2. Data Warehouse Layer Construction
1. Data Model Design
2. ETL Process
3. Data Warehouse Optimization

3. Natural Language Platform Annotation and Model Training
- Natural language platform annotation: Utilize natural language processing (NLP) technologies to annotate APP-related text data and extract valuable information such as APP names and functional descriptions.
- Model training: Train machine learning models based on the annotated data for subsequent APP feature extraction and classification tasks.

4. APP Dimension Table Construction
- Feature extraction: Extract key features of APPs (e.g., functions, categories) by combining the results of natural language platform annotation and model training.
- Dimension table design: Design the structure and fields of the APP dimension table based on the extracted features to ensure comprehensive and accurate description of APP information.
- Dimension table population: Fill the APP dimension table with the extracted feature data to build complete APP dimension datasets.

5. Construction of APP Assets Including DAU and Trends
Leverage GeTui's data capabilities to build APP-related assets such as daily active users and trend metrics.

搜集汇总

数据集介绍

特点

该数据集由每日互动股份有限公司提供，包含4001条记录，每日更新，涵盖设备标识、APP活跃类别、兴趣标签等多维度信息，适用于数智营销场景的行业趋势分析和决策支持。