[New Book Recommendation] User Portraits: Understanding Users and Helping Enterprises Grow - "User Portraits: Platform Construction and Business Practice"

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〇.Introduction

In today's era of fierce market competition, understanding user needs and improving user experience have become the keys to business success. As an important tool, user portraits can help companies better understand users and optimize products and services in a targeted manner.

1. What is user portrait?

Persona is a tool that summarizes and simplifies real user data. It can depict the concrete characteristics of the target user group in a clearer and more organized way, so that product designers can better understand users. requirements to provide a basis for design decisions. Through user portraits, companies can better understand target users and provide them with more accurate products and services.

Generally speaking, user portraits include the following information:

Basic information: such as the user's age, gender, occupation, etc.
Social behavior: Users’ social media usage habits, such as what social platforms they commonly use, what content they follow on these platforms, etc.
Lifestyle habits: such as the user’s work and rest time, eating habits, leisure activities, etc.
Location information: the user’s usual residence, work place, etc.
Consumption behavior: users’ shopping habits, preferred product types, brand loyalty, etc.
Device information: devices commonly used by users, such as mobile phone models, computer operating systems, etc.
Psychological characteristics: users’ values, interests and hobbies, etc.
This information can be obtained from user research, market research, data analysis, etc., and can help companies better understand target user groups through user portraits, so as to make more accurate product design, marketing strategy and other decisions.

2. Advantages of user portraits

Help enterprises accurately locate target users: Through the analysis of user portraits, enterprises can clearly understand the characteristics and needs of target users, thereby better positioning products and services.
Improve the efficiency of enterprise decision-making: User portraits provide enterprises with objective and accurate data support, helping to improve the efficiency and accuracy of decision-making.
Optimize product design: Through the analysis of user portraits, companies can discover the pain points and needs of users in the process of using products, and optimize product design in a targeted manner.
Improve user experience: Through in-depth research on user portraits, companies can better understand user needs and behavioral habits, thereby providing products and services that are more in line with user expectations and improving user experience.

3. How to implement user portraits

Data collection: First, user data needs to be collected, which can be obtained from market research, user research, data analysis, etc.
Data processing: Clean, summarize and process the collected data for subsequent analysis.
Build a model: Based on the collected data, build a user portrait model and divide users into different segmented groups.
Analyze needs: Aim at different segmented groups of users, analyze their needs and behavioral characteristics, and formulate corresponding product and service strategies.

4. Problems in the application of user portraits

Information collection issues: The information collected may be incomplete or inaccurate, affecting the accuracy of user portraits.

Data analysis issues: Professional data analysis skills are required, otherwise the analysis results may be inaccurate.

Visualization issues: User portraits are the visual representation of data. How to present complex data to non-professionals in a simple and clear way so that they can quickly understand the meaning of the data is also an issue that requires attention.

Privacy issues: When collecting and using user data, attention needs to be paid to protecting user privacy and data security.

5. Summary

As an important tool, user portraits can help companies better understand user needs and behavioral characteristics, optimize products and services in a targeted manner, and improve decision-making efficiency and user experience. But at the same time, we also need to pay attention to issues in applications such as information collection, data analysis, visual presentation, and privacy issues. With the continuous development of technology, in the future, the application prospects of user portraits will be broader, and the value to enterprises will also continue to increase.

Recommended new book - "User Portraits: Platform Construction and Business Practice"

In the era of big data, how to effectively mine the value of data and present it through portrait data, how to build platform functions based on portrait data and improve business output are things worth thinking about and putting into practice for all types of companies and business personnel.

Unleash the value of big data through profiling. In the era of big data, there is no lack of data, but a lack of systematic methods to mine the value of data. I hope to use this book to improve readers' understanding of portraits, and guide companies and business personnel to make full use of big data resources from the perspective of portraits and release more data value.

Explain clearly what the portrait platform is. This book clearly explains the construction process of the portrait platform and the ways to empower the business, helping readers gain a comprehensive and in-depth understanding of the portrait platform. By referring to the content in the book, readers will be more targeted in the process of building a profiling platform and using profiling data.

$$Scan\; the\; QR\; code\; to\; learn\; more\uparrow$$

brief introduction

This is a book that explains user portraits from the shallower to the deeper levels from four levels: functional modules, technology implementation, platform construction, and business applications. The author has experienced the entire process of its user portrait platform from 0 to 1 and developed into a portrait middle platform in a leading Internet company, laying a solid technical foundation and accumulating rich business experience. This book examines the user portrait platform from the dual dimensions of technology and business. The entire process was reviewed.

Specifically, this book mainly contains the following contents:

(1) The role of profiling, the core functions and implementation logic of the four mainstream commercial profiling platforms in the industry;

(2) The main functions of the profiling platform, the technical architecture and technology selection of the profiling platform, and the data model of the profiling platform;

(3) The four major functional modules of the profiling platform: tag management, tag service, grouping function, and profiling analysis implementation plan;

(4) Build a user portrait platform from 0 to 1, including environment construction and front-end and back-end engineering framework construction;

(5) How the portrait platform empowers businesses in different life cycle stages of users and various business scenarios;

(6) Optimization and best practices of the portrait platform.

There are more than 200 design drawings and prototype drawings in the book, which can help readers understand the implementation principles and functional forms of the platform more intuitively. 20+ real application cases, technical solutions and cases are all from real projects. This book provides runnable code to help readers quickly build and deploy a user profiling platform.

Table of contents

前　言

第1章　了解画像平台  1

1.1　画像基本概念  1

1.1.1　什么是画像  1

1.1.2　画像的重要性  2

1.1.3　画像平台定位  3

1.2　OLAP介绍  3

1.2.1　OLAP与OLTP对比  3

1.2.2　OLAP场景关键特征  4

1.2.3　OLAP的3种建模类型  5

1.2.4　OLAP相关技术发展历程  5

1.3　业界画像平台介绍  6

1.3.1　神策数据  7

1.3.2　火山引擎增长分析  10

1.3.3　GrowingIO  13

1.3.4　阿里云智能用户增长  16

1.4　画像平台涉及的岗位  18

1.4.1　数据工程师  18

1.4.2　算法工程师  18

1.4.3　研发工程师  18

1.4.4　产品经理  19

1.4.5　运营人员  19

1.5　本章小结  19

第2章　画像平台功能与架构  20

2.1　画像平台主要功能  20

2.1.1　标签管理  20

2.1.2　标签服务  24

2.1.3　分群功能  25

2.1.4　画像分析  28

2.2　画像平台技术架构  32

2.2.1　画像平台常见的技术架构  32

2.2.2　画像平台技术选型示例  33

2.2.3　业界画像功能技术选型  35

2.3　画像平台的3种数据模型  36

2.4　本章小结  38

第3章　标签管理  40

3.1　标签管理整体架构  40

3.2　标签分类  43

3.2.1　标签实体及ID类型  43

3.2.2　标签分类方式  44

3.3　标签管理功能实现  48

3.3.1　标签存储  48

3.3.2　标签生产  55

3.3.3　标签数据监控  67

3.3.4　工程实现  69

3.4　岗位分工介绍  70

3.5　本章小结  72

第4章　标签服务  73

4.1　标签服务整体架构  73

4.2　标签查询服务  74

4.2.1　标签查询服务介绍  74

4.2.2　标签数据灌入缓存  76

4.2.3　标签数据结构  79

4.2.4　标签数据处理  81

4.2.5　工程实现  83

4.3　标签元数据查询服务  85

4.3.1　标签元数据查询服务介绍  85

4.3.2　工程实现  87

4.4　标签实时预测服务  89

4.4.1　标签实时预测服务介绍  89

4.4.2　工程实现  90

4.5　ID-Mapping  93

4.6　岗位分工介绍  97

4.7　本章小结  98

第5章　分群功能  99

5.1　分群功能整体架构  99

5.2　基础数据准备  101

5.2.1　画像宽表  101

5.2.2　画像BitMap  108

5.3　人群创建方式  111

5.3.1　规则圈选  112

5.3.2　导入人群  119

5.3.3　组合人群  121

5.3.4　行为明细  123

5.3.5　人群Lookalike  125

5.3.6　挖掘人群  126

5.3.7　LBS人群  127

5.3.8　其他人群圈选  128

5.3.9　工程实现  131

5.4　人群数据对外输出  137

5.5　人群附加功能  138

5.5.1　人群预估  138

5.5.2　人群拆分  140

5.5.3　人群自动更新  141

5.5.4　人群下载  142

5.5.5　ID转换  143

5.6　人群判存服务  144

5.6.1　Redis方案  144

5.6.2　BitMap方案  147

5.6.3　基于规则的判存  149

5.7　岗位分工介绍  150

5.8　本章小结  152

第6章　画像分析  153

6.1　画像分析整体架构  153

6.2　人群画像分析  155

6.2.1　人群分布分析  155

6.2.2　人群指标分析  156

6.2.3　人群下钻分析  157

6.2.4　人群交叉分析  158

6.2.5　人群对比分析  158

6.2.6　工程实现  159

6.3　人群即席分析  165

6.3.1　分布分析与指标分析  166

6.3.2　下钻分析与交叉分析  167

6.3.3　人群画像预览  168

6.4　行为明细分析  169

6.4.1　明细统计  171

6.4.2　用户分析  173

6.4.3　流程转化  176

6.4.4　价值分析  179

6.4.5　工程实现  181

6.5　单用户分析  183

6.5.1　用户画像查询  184

6.5.2　用户关系数据分析  185

6.5.3　用户涨掉粉分析  190

6.5.4　用户内容流量分析  192

6.6　其他常见分析  193

6.6.1　业务分析看板  193

6.6.2　地域分析  195

6.6.3　人群投放分析  197

6.7　岗位分工介绍  199

6.8　本章小结  200

第7章　从0到1构建画像平台  201

7.1　基础准备  201

7.1.1　技术组件协作关系  201

7.1.2　基础环境准备  203

7.2　大数据环境搭建  206

7.2.1　Hadoop  207

7.2.2　Spark  210

7.2.3　Hive  212

7.2.4　ZooKeeper  215

7.2.5　DolphinScheduler  216

7.2.6　Flink  217

7.3　存储引擎安装  219

7.3.1　ClickHouse  219

7.3.2　Redis  221

7.3.3　MySQL  222

7.4　工程框架搭建  223

7.4.1　服务端工程搭建  223

7.4.2　前端工程搭建  237

7.5　运行开源代码  238

7.6　本章小结  240

第8章　画像平台应用与业务实践  241

8.1　画像平台常见应用案例  241

8.1.1　标签管理应用案例  241

8.1.2　标签服务应用案例  244

8.1.3　分群功能应用案例  245

8.1.4　画像分析应用案例  247

8.2　用户生命周期中画像的使用  248

8.2.1　用户生命周期的划分方式  249

8.2.2　引入期画像的使用  250

8.2.3　成长期画像的使用  251

8.2.4　成熟期画像的使用  252

8.2.5　休眠期画像的使用  253

8.2.6　流失期画像的使用  254

8.3　画像平台业务实践  255

8.3.1　用户增长  255

8.3.2　用户运营  259

8.3.3　电商卖货  263

8.3.4　内容推荐  266

8.3.5　风险控制  268

8.3.6　其他业务  271

8.4　本章小结  273

第9章　画像平台优化总结  274

9.1　任务模式  274

9.1.1　任务定义及执行模式  276

9.1.2　任务优先级及并发控制  277

9.1.3　父子任务拆分  277

9.1.4　任务异常检测与重试  278

9.1.5　便捷的横向拓展能力  279

9.2　人群创建优化进阶  279

9.2.1　人群圈选需求  279

9.2.2　简单直接的解决思路  280

9.2.3　将ClickHouse作为缓存  281

9.2.4　SQL优化  283

9.3　BitMap在画像平台中的

使用方案  286

9.3.1　BitMap基本原理  286

9.3.2　BitMap在人群圈选中的

使用方案  287

9.3.3　BitMap在分布分析中的

使用方案  289

9.3.4　BitMap在判存服务中的

使用方案  291

9.4　画像宽表生成优化  292

9.4.1　多表左连接  293

9.4.2　分组再合并  294

9.4.3　增加数据加载层  296

9.4.4　采用Bucket Join  297

9.5　ID编码映射方案  299

9.6　如何构建一个类似神策的平台  301

9.6.1　神策产品介绍  301

9.6.2　主要技术模块  302

9.7　平台技术优化思考  305

9.8　本章小结  307