User growth refers to the growth of user-related metrics, and after finding the North Star indicator, it is necessary to analyze the user's demand matching model. This article specifically explains the basic knowledge of building a user demand matching model, and I hope it will be helpful to you.
User growth refers to the growth of user-related indicators, we have analyzed how to find the North Star indicator in the previous article, then find the North Star indicator, and determine the growth index, and then divide the user's demand matching model.
Benign "user growth" is the product to meet the needs of users and the market wants to match, in the product to reach the user blockage point, the main work of growth is to find the growth choke point, find the funnel factor with the greatest loss, reduce the growth obstacle, reduce the cost of user transaction decision-making (cognition, trust, impulse, satisfaction), how to evaluate the product demand to meet the demand?
The precondition for the quantification of demand satisfaction is to judge the satisfaction of demand matching, how to judge the satisfaction of demand? Then it is necessary to build a set of user demand portrait model, the core work of user portrait is to label users, and the purpose of labeling is to classify statistics and obtain accurately, so user portrait is a general concept, and the basic model of user portrait is suitable for most industries and scenarios.
Case: There is a concept called "competency model" in the HR module, "competency model" is the basis of human resources "selection, education, use, retention" of the work, is based on the concept of internal management, is the target benchmark formulated by enterprise training and development research, is the measure of recruitment, then "competency model" is equal to "user portrait", I personally think that "competency model" is only a part of the talent portrait, is a static part, and "user portrait" is the construction process of continuous improvement and development.
Take the "competency model" of the HR module as an example, I will use the "user portrait" of enterprise training to build a case to illustrate.
So what is the goal of corporate training?The goal of corporate training is to develop a training plan based on the company's development and job talent needs. For different levels, different positions, and different learning Xi plans and learning Xi models.
- For example, the training for middle and senior executives is more based on sand tables, cases, and heuristics;
- For grassroots employees, the basic abilities A, B, and C knowledge points are the mainstay.
The above are the needs of the company's management, so what are the needs of the "trainee" users? Will these training contents meet the needs of users? Whether users still have personalized needs? How to obtain the needs of users? Whether the management needs match the needs of users? If you do not do user portrait analysis, then this kind of training plan will mostly end in failure.
1. Build user portraits: Build customer portraits through multi-dimensional indicators
Let's analyze, how to build a user portrait of enterprise training?
- First of all, it is necessary to analyze the competency model of positions and positions, and then disassemble the "competency model" in a structured manner to establish a matching Xi map of positions, positions should be known, and promotion and development capabilities.
- Sort out the basic portrait model of users, demographic attributes such as gender, age, and education.
- Evaluate the learning Xi ability of the "user" (the matching degree of knowledge points in the knowledge graph), which can be through assessments, exams, questionnaires, etc.
Establish user dynamic data behavior tracking points to track users' learning Xi behaviors.
2. Establish a labeling system: Establish a systematic customer labeling and content labeling system
The above is the basis for establishing user portraits, then demand matching is a "supply and demand relationship", there is already a demand base, then the supply side should also be matched with products that meet user needs, and the core needs of training are courses and knowledge points.
- Sort out the skill tree and knowledge point structure of each position and position, establish a knowledge graph, and disassemble it to the level of "element-knowledge point" (for example, product display is a course, then pile placement is a knowledge point).
- Establish a personalized learning Xi plan for users, and establish a clear personalized knowledge graph of job knowledge and interests based on the user's training Xi goals, personal learning Xi level, hobbies, and learning Xi behaviors.
- Build a Xi cultivation and feedback mechanism, users in the process of learning Xi through the cultivation system, value evaluation system (medals, certificates) quantitative chemical Xi process, through quantifiable goals, drive users and growth, while improving professional skills, you can also have a clear understanding of yourself, so that the learning Xi is no longer boring and boring, and the external drive is transformed into internal drive.
Summary: The core of user portrait is to establish a label system, accurate customer stratification, and build user portraits.
- Establish a labeling system: Establish a systematic customer labeling and content labeling system
- Precise customer segmentation: Users are grouped and stratified through the multi-level dimensions behind customer portraits
- Build user portraits: Build customer portraits through multi-dimensional indicators
Dimensions of User Portraits:
- User source: The source of user data can be accurate to the source platform, media, creative, advertising space, content and other channels;
- User attributes: the demographic attributes of the user and the uniquely identifiable attributes (gender, age, life stage, etc.);
- User identity: identity information, WeChat OpenID, mobile IMEI, DeviceID, email address and third-party ID;
- User tags: Tags are the core part, establishing a library of customer tags, content, and industry tags;
- Content tags: tags of marketing and content preferences reached by users, so as to achieve stratification of groups with the same preferences;
- User behavior: Customizable user events, such as browse clicks, registrations, interactions, transactions, purchases, and more.
Annotation:
This article was originally published by @闯爷 on Everyone is a Product Manager. Reproduction without permission is prohibited
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