1. User portrait
1. Concept description
User portraits, as an effective tool for delineating target users, linking user demands and design directions, user portraits have been widely used in various fields. User portraits were initially applied in the field of e-commerce, especially in the field of digital marketing. The core reliance is based on rich tags describing user portraits.
In the context of the era of big data, user information is flooded in the network, and each specific information of the user is abstracted into tags. Based on the tags, the user profile is used to understand the user, and these tags are used to concretize the user image, so as to provide users with targeted Sexual service. For example, the above-mentioned user portrait information that can be analyzed based on the simplest user data.
2. Composition structure
The core job of user portraits is to label users based on data collection. With the continuous enrichment of labels, user portraits will become clearer and clearer, eventually achieving the ability to understand and even understand users.
In the actual user portrait system, the classification and grading of tags is far more than that simple, but more detailed and accurate:
- Basic attributes: gender, age, spending power, occupation, etc.;
- Behavioral attributes: activity, browse, click to view, not interested;
- Realistic scenes: frequently visited business districts, movie theaters, scenic spots, etc.;
- Description of interests: shopping, film, music, games, reading, etc.
- Customization: through machine analysis, judgment based on certain label combinations;
Through the various scene data generated by the user, to analyze or infer the user’s information labelling and visual description. Through the user portrait, the product can automatically understand the user and serve the user. For example, many information flow applications will be based on the user Reading content automatically judges and pushes content that users may like.
3. The value of portraits
Companies with a large number of users and complex businesses will spend a high cost to build a user profile system, collect data for analysis on various business lines, and continuously understand users to provide more accurate services and diversified operating strategies.
User drainage
Through the analysis of the profile of existing users, it is placed on the relevant DMP advertising platform, focusing on recommending users with similar tags on the platform to guide users to the product. This is also similar to the concept of rapid expansion of similar users.
Cold start for new users
Quickly analyze the attributes and interest preferences of newly registered users to realize fast and accurate recommendation of services. For example, the area where the user is registered can be used to infer the characteristics of the new user through the general tags of users in this area.
Accurate or personalized service
Here is to understand users and provide accurate or personalized services based on rich user portrait analysis. Providing good services can naturally achieve the deep precipitation of users.
Multiple scene recognition
The scenario here is relatively complicated. It is described through a case. For example, when you register with mobile phone number A on a certain platform, then the mobile phone number A is lost. After you switch to mobile phone number B, you can understand whether the user is a user of mobile phone number A through relevant behaviors. It can identify different users based on the same mobile phone sequence or multiple mobile phone sequences to identify the same user.
Silent user wake up
Based on refined tags and multiple scene data, the user’s silence is quickly identified, and operating strategies are formulated based on profile analysis to activate recall to reduce user loss.
2. Crowd analysis
Every time a case of user group analysis is developed, a paragraph can be heard in my mind: only child, arrogant, greenhouse flower, indifferent and selfish, exquisite and self-interested, pure thinking, unassuming personality, lack of team awareness, non-mainstream, and collapse. In the past few years, this was the label that many elders or society put on the 90 generation, and there were also many labels that were self-ridicule in the 90s. As a member of the post-90s, I am quite satisfied with this portrait...
The above is a typical atypical case of crowd profile analysis. In fact, in recent years, there have been many and accurate analysis reports on 90 people. Many data companies will start from: social attributes, spending power, game hobbies, pets, network applications, etc. Do in-depth analysis of popular areas. Analyzing crowd portraits can generate very high value in commercial applications.
Three, deep application
1. Business district analysis
First, based on the user group in the business district, it is easy to understand that users generate data in a certain business district, and then obtain user-related tags in turn to analyze user portraits in the business district.
Secondly, analyze the business district's own services, such as the flow of people in the food field, the flow of people in the entertainment field, and the flow of people in the shopping field, and compare different business circles to provide strategies for the operation of the business circle.
The profile obtained through the analysis of the comprehensive business district conducts a comprehensive study on the composition, characteristics and various factors that affect the scale of the business district. It serves the company to choose a reasonable store location and also serves the business district to accurately introduce rich brands Shop.
2. Industry analysis
Industry analysis profile is a very complex report, which usually considers multiple factors such as user size, demographic characteristics, technology, revenue scale, competitiveness, competitive landscape, industry policies, and market saturation. Industry analysis is also a different concept from a different perspective. For example, from the perspective of industry products: based on industry analysis to determine whether to do, how to do it, how to do it well, to clarify product direction and operation strategy, etc.; to judge new products from the investment banking field Is it worth the investment? Is there stable and high return, risk control, etc.?
Build user portraits through data in multiple scenarios, and conduct commercial operation and management in many business scenarios that are applied to the product to generate higher value.
Fourth, the source code address
GitHub·地址
https://github.com/cicadasmile
GitEE·地址
https://gitee.com/cicadasmileData Insight Business Series
Recommended reading: finishing programming system
| Serial number | project name | GitHub address | GitEE address | Recommended |
|---|---|---|---|---|
| 01 | Java describes design patterns, algorithms, and data structures | GitHub·click here | GitEE·Click here | ☆☆☆☆☆ |
| 02 | Java foundation, concurrency, object-oriented, web development | GitHub·click here | GitEE·Click here | ☆☆☆☆ |
| 03 | Detailed explanation of SpringCloud microservice basic component case | GitHub·click here | GitEE·Click here | ☆☆☆ |
| 04 | Comprehensive case of SpringCloud microservice architecture actual combat | GitHub·click here | GitEE·Click here | ☆☆☆☆☆ |
| 05 | Getting started with SpringBoot framework basic application to advanced | GitHub·click here | GitEE·Click here | ☆☆☆☆ |
| 06 | SpringBoot framework integrates and develops common middleware | GitHub·click here | GitEE·Click here | ☆☆☆☆☆ |
| 07 | Basic case of data management, distribution, architecture design | GitHub·click here | GitEE·Click here | ☆☆☆☆☆ |
| 08 | Big data series, storage, components, computing and other frameworks | GitHub·click here | GitEE·Click here | ☆☆☆☆☆ |