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This article introduces how to use big data to build user portraits. The content is very detailed. Interested friends can use it for reference. I hope it will be helpful to you.

In the era of big data, not only ordinary users can enjoy the convenience brought by technology, but enterprises can also extract commercially valuable information from the data and build user portraits to analyze and predict user behavior. Although user portraits are not a new concept, the emergence of big data technology makes user portraits more clear and objective. Let's take a look at how to use big data to build user portraits.

1. Know the user portrait

To put it simply, user profile is the tagging of user information. That is, to collect all kinds of data and behavior of this user, so as to get some basic information and typical characteristics of this user, and finally form a character prototype. General user portraits analyze three information dimensions, namely, basic attributes, consumer shopping and social circles. The basic attributes refer to some basic information of the user, such as age, gender, birthday, school, location, and so on. The dimension of consumer shopping is more specific, such as the area of users' consumer preferences, preferred prices, consumption records and so on.

2. The advantages of using big data to build user portraits.

(1) accurate marketing: when enterprises and merchants have certain information about users, they can build clear user portraits, so that they can recommend goods and services that they will be interested in according to their preferences, income and other tags. The most typical example is that many merchants now analyze potential users of their products and use text messages and other means of marketing for specific groups. Compared with the traditional SMS marketing, precision marketing can move users more, and save more time and effort.

(2) user statistics: through big data, we can make statistics on some data. For example, we often see some APP rankings, and even specific data such as penetration and daily survival rate can be clearly counted.

(3) data mining: build an intelligent recommendation system, use association rules to calculate what sports brands people who like red wine usually like, and use clustering algorithm to analyze the age distribution of people who like red wine.

(4) effect evaluation: in fact, it is equivalent to market research and user research, quickly locate service groups and provide high-level services. For example, if you are a car buyer who wants to advertise, but you don't know which channel is better, you can try it first and see how the data feedback is.

(5) Private customization: private customization of services or products, but unscrupulous merchants will also use user portraits to kill them.

(6) Business operation analysis: business operation analysis and competition analysis influence the business decision-making and even development strategy of the enterprise.

3. The process of building user portraits

(1) data source: generally speaking, the data for building user portraits comes from website transaction data, user behavior data and web log data. Of course, it is not limited to these data, there are also personal credit data on some platforms.

(2) data preprocessing: the first step is cleaning, cleaning some disorderly data, and then summing up the structured data, and finally standardizing the information. We can simply understand the data preprocessing as classifying the data in a table, and this step is to lay the foundation for data analysis.

(3) behavior modeling: text mining, natural language processing, machine learning, prediction algorithm, clustering algorithm. Machine learning needs some mathematical foundation, such as statistics, linear algebra and so on.

(4) user profile: through the previous series of means, we can classify the data into basic attributes, purchasing power, behavioral characteristics, interests, psychological characteristics, social networks and other dimensions.

On how to use big data to build user portraits to share here, I hope the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

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