Shulou(Shulou.com)06/03 Report--

The concept of "taking the user as the core" is deeply rooted in the Internet age. However, in order to really understand the user, we have to mention the "user profile". With the in-depth research and application of big data technology, with the help of user profiles, enterprises or APP can deeply explore the needs of users, so as to achieve fine operation and lay a solid foundation for precision marketing. This article will focus on what the user profile is, the construction process of the user profile and the application scenario.

User portrait is essentially the embodiment of data ability.

User profile, that is, the tagging of user information, and in essence, user portrait is the tagging of data. There are three common user portrait systems: structured system, unstructured system and semi-structured system. The unstructured system has no obvious hierarchy and is more independent. The semi-structured hierarchy has a certain hierarchical concept, but it does not have too strict dependencies. The structured system has a strong hierarchical structure. Take a simple three-level structured tag as an example, the first-level tag has basic attributes and interest preferences, and thus can be extended to the second-level tag and the third-level tag, specific to different attributes and interests.

In the field of Internet and e-commerce, user profile is often used as the basic work of precision marketing and recommendation system.

(1) Precision marketing: according to the characteristics of historical users, operators can analyze the potential users and potential needs of products, and then marketing for specific groups through corresponding means.

(2) user analysis: after classifying users according to their attributes and behavior characteristics, we can count the number and distribution of users under different characteristics, and analyze the distribution characteristics of different user portrait groups.

(3) data mining: based on user portraits, developers can build recommendation systems, search engines and advertising systems to improve service accuracy.

(4) Service products: depict the user portraits of the products, analyze the audience of the products, more thoroughly understand the psychological motivation and behavior habits of the users using the products, improve the product operation and improve the service quality.

(5) Industry report-user research: through user profile analysis, operators can better understand the dynamics of the industry, such as crowd consumption habits, consumption preference analysis, consumption difference analysis of different regional categories, and so on.

The practice of pushing user portraits

Relying on the accumulation of many years of push services and strong big data analysis capabilities, GE Tweet has launched a push SDK (image), which provides APP developers with rich user profile data and real-time scene recognition capabilities.

Push unique cold, hot and temperature data tags, can effectively analyze users' online and offline behavior, in-depth mining of user characteristics, and help APP operators to fully understand user attributes. Among them, "cold data" refers to the basic attributes of users, with a small probability of change, such as gender and age, etc.; "warm data" can trace users' recent active applications and scenarios, with a certain timeliness; "hot data" refers to users' current scenes and real-time user behavior to help APP operators seize fleeting marketing opportunities.

GE Tweet not only has a rich general label system, but also can jointly model according to the specific needs of customers and output customized tags to meet the needs of APP in different scenarios.

Standardize the process of portrait construction

The construction of user portraits requires the participation of technical and business personnel to avoid formal user portraits. There are also some ways for developers to refer to.

(1) the design of label system. Developers need to know their own data and determine the form of tags that need to be designed.

(2) basic data collection and multi-source data fusion. When building a user profile, a tweet integrates a tweet and the APP's own data.

(3) realize the unified identification of users. In most cases, many users of APP are distributed in different account systems, and a tweet will uniformly identify them.

(4) the construction of user portrait feature layer. Each data is characterized.

(5) Portrait label rule + algorithm modeling. Both are indispensable, in practical applications, the algorithm is difficult to solve the problem, the use of simple rules can also achieve good results.

(6) use the algorithm to label all users.

(7) Portrait quality control. In practical applications, user portraits will have certain fluctuations. In order to solve this problem, a corresponding monitoring system has been set up to monitor the quality of the portraits.

The whole process of building a user profile can be divided into three parts: first, basic data processing. The basic data includes user equipment information, users' online APP preferences and offline scene data.

Second, the middle data processing of the portrait. The processing results include online APP preference features and offline scene features.

Third, the portrait information table. There should be four kinds of information in the table: basic attributes of the device; basic user portraits, including the user's gender, age, and relevant consumption level; and user interest profile, that is, the direction in which the user is more interested, such as the user's preference for price comparison APP or other portraits of Haitao APP; users.

In the process of building a user profile, machine learning occupies a more important position. Machine learning is mainly a process of continuous updating, data cleaning and data storage of massive data. It makes more use of machine learning platform for corresponding prediction analysis, model output and so on.

There are two key points in the attention of portrait quality. First, how to optimize the quality. The model of the user's portrait will be modified and optimized regularly. Second, pay attention to the fluctuation of portrait quality and give early warning of abnormal changes.

Push user portrait application

The integration of SDK can enrich the user analysis dimension of APP. Its main applications are reflected in two aspects: first, accurate recommendation. APP operators can recommend different content for different users through rich tags such as gender, age, hobbies, scenarios, etc., so as to achieve more refined operations and improve user activity and retention.

Second, user clustering, push can help APP deal with user data, complete user profile, and establish user clustering model. At the same time, through the analysis of user characteristics, personal push can also map the old users of APP to a certain cluster, so as to produce the target clustering of APP, and finally help APP operators to make more accurate operation strategies for different user groups.

"thousands of people flirt with you, it is better for one person to understand you". When the Internet gradually enters the era of big data, APP can only really understand users in order to get users and retain users. Based on a complete big data computing architecture, a push like SDK access can not only help developers improve the efficiency of development decisions, but also help APP operators to carry out fine operations, thus improving the marketing efficiency and market competitiveness of enterprises.

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