Is your labeling system empty and fake? These 3 tips can help you solve your business pain points

A good labeling system is essential for refined operations. This article details common problems, solutions, and practices of the labeling system. It is recommended for students who want to understand user operations.

Traffic dividends are disappearing, and refined operations are the way to go. If you want to reduce costs and increase efficiency, you need to carry out user stratification operations. A good labeling system is essential.

When it comes to the concept of label system, we tend to confuse it with profiling and grouping; when it comes to the establishment of a label system, we fall into the dilemma of confused requirements; when it comes to the role of the label system, we are even more troubled by its "empty talk". After doing a lot of seemingly correct infrastructure work, we find that it cannot actually solve business pain points.

Today, we will explore: How to build a practical user tag system to help business growth . Whether you are an entrepreneur, product manager or marketer, you can get valuable dry goods.

Without further ado, let's go straight to the main text. Enjoy:

1. Common problems of labeling system

1. What are the differences between label systems, user portraits, and user groupings?

Tag system, user portrait and user grouping are commonly used concepts in the field of data analysis and marketing. They have some differences and connections as follows:

(1) Tagging System:

A labeling system is an organizational structure used to classify and describe data or objects. It identifies the properties, characteristics or classifications of data or objects by adding tags to them. Tags can be keywords, attributes, indicators, etc., used to describe and organize data.

(2) User Persona:

User portrait is a model that describes and depicts a specific user group in detail. It is based on the user's personal information, behavior data, interests and hobbies, and creates a specific user image by analyzing and integrating these data. User portraits can include information such as the user's age, gender, geographic location, consumption preferences, and purchasing habits.

(3) User Segmentation:

User segmentation is the process of classifying and grouping users based on some common characteristics or behaviors. Through user segmentation, users can be divided into different groups, each with similar characteristics or behavior patterns. User segmentation can be based on a variety of factors, such as geographic location, age, gender, purchasing behavior, interests and hobbies, etc.

In summary, the labeling system is a structure for classifying and organizing data , user portraits are models for describing and characterizing specific user groups , and user segmentation is the process of classifying and grouping users based on common characteristics .

They are interrelated in data analysis and marketing, and together help companies better understand users, manage data, and develop marketing strategies.

2. Why does the labeling system become "empty and false"?

User tags can exist in many forms. They can be natural attributes of users, statistical indicators of user transactions and asset data, lifecycle tags, user value tags, user preferences, and so on.

A good labeling system can accurately and effectively connect users with content, scenarios, and products in a personalized manner, thereby attracting potential customers, increasing user activity, reducing user interruptions, lowering marketing costs , and ultimately maximizing user value.

However, in actual construction, enterprises found that after investing a lot of time and energy to complete the infrastructure, the label system still became "empty and false". The reasons are as follows:

(1) Lack of clear label definition and management standards: The label system lacks clear definitions of the meaning and purpose of each label, as well as related standards and constraints. This leads to ambiguity and confusion in labels, making the application of labels vague and ineffective.

(2) Lack of in-depth understanding of business needs and actual application scenarios: The builders lack an in-depth understanding of business needs and actual application scenarios. They fail to take into account the specific needs and characteristics of the business, resulting in redundancy and overlap of tags, and are unable to provide valuable business insights.

(3) Improper management and maintenance: The labeling system lacks an effective management and maintenance mechanism, which cannot ensure the accuracy and consistency of labels. Abandoned labels, duplicate labels, or unclear label definitions will make the labeling system lose its actual value and become large and empty.

In this context, the business side cannot accurately identify the various characteristics of users, thereby mining the data growth space in a structured manner and formulating refined operation strategies. The label system is also like "chicken ribs" , which are tasteless to eat and a pity to throw away.

2. Three ways to solve business pain points and rebuild an effective labeling system

1. Clarify business goals and key indicators

In order to solve business pain points and build an effective labeling system, we need to sort out the user experience map and restore the business process.

First, identify business pain points and needs: Work closely with the business side to gain a deep understanding of the problems and pain points they are currently facing. Through communication and analysis with business personnel, determine which aspects need to be improved and optimized, and clarify business needs.

Secondly, define key indicators and goals: According to the business pain points and needs, determine the key indicators and goals, which should be directly related to the business goals. These indicators can be business growth rate, user satisfaction, conversion rate, etc., to ensure that these indicators can be quantified and measured, so as to evaluate the effectiveness of the labeling system and the effectiveness of business improvement.

Take a food delivery platform as an example, and sort out the business processes (covering the user life cycle), including channel source, app download, registration and login, active browsing, card binding, ordering (product preferences, activity preferences), user repurchase, user churn, etc., to build our label system, and then apply the label combination to the operation strategy.

2. Optimize data collection and organization

This step requires full communication with professional data personnel, determination of standards, and feasibility assessment. The highlights include the following:

Selection and integration of data sources: In order to solve business pain points, it is necessary to collect and integrate information from multiple data sources. This includes internal data, external data, and third-party data. Make sure the selected data sources are relevant to business goals and key indicators and can provide valuable information.

Data quality assurance and cleaning: Data quality is the key to establishing an effective labeling system. Through data cleaning and processing, duplicate, erroneous or incomplete data can be removed to ensure data accuracy and consistency. Data verification and error correction technology can be used to improve data quality and reduce errors.

3. Accurate classification and hierarchical design

When establishing a labeling system, accurate classification and hierarchical design are required. According to business needs and data characteristics, labels are divided into different categories and hierarchies to better organize and manage data. This helps improve the usability and scalability of labels.

Generally speaking, labels can be divided into static labels, dynamic labels, and model labels according to their timeliness and extraction dimensions.

• Static tags: Determined during the data collection and organization stage, usually classified and labeled based on fixed attributes or features. These tags do not change over time or with events, such as the user's age, gender, geographic location, etc.
• Dynamic tags: used to capture and reflect dynamic changes in data, providing more accurate and real-time information. For example, a user's purchase frequency, the time of the last purchase, and the items in the shopping cart can be used as dynamic tags, which are calculated and updated based on the user's real-time behavior and interaction data.
• Model label: It is abstracted based on user data attributes to reflect user portrait characteristics, such as core users, churned users, and warning churned users. It can also be said to be a prediction label.

A complete labeling system covers the above three categories of labels in terms of label types. From a business perspective, it can also add dimensions such as life cycle, user value, active features, and user preferences .

3. Application and Practice of Labeling System

How to evaluate whether the label system you have built is useful? How to use it? Can it solve business problems? Industry benchmarks provide reference answers.

1. Getui - Solve user segmentation and group analysis needs through the tag system

As a company that provides mobile push and precision marketing services , Getui helps customers solve the needs of user segmentation and group analysis by establishing an effective labeling system.

Getui’s user portrait backend is a user analysis tool based on a tag system. It generates detailed user portraits by collecting and integrating user behavior data, interests, hobbies, geographic locations and other information, and conducts user segmentation and group analysis.

For example, a customer can use Getui's user portrait background to analyze their own user groups, such as young female user groups. By analyzing the interests, hobbies, purchase preferences and other tag information of this group, customers can better understand their needs, formulate refined marketing strategies, and provide push content that is more in line with their interests.

2. Taobao has a thousand faces for each person - improving personalized recommendation effects by optimizing the tag system

As one of the largest e-commerce platforms in China , Taobao has implemented a personalized recommendation strategy for each individual by optimizing the label system.

Taobao uses big data analysis and machine learning technologies to collect and analyze users' behavior data, purchase history, browsing history and other information, and has established a large and sophisticated labeling system.

Through tag association and key attribute definition, Taobao is able to match users' interests and preferences with products and provide users with personalized recommendation results.

This personalized recommendation strategy greatly improves users' shopping experience and satisfaction . Users can find products of interest more easily, which improves purchase efficiency, conversion rate and order value.

3. Digital marketing for auto companies: using precise labeling systems to achieve precise marketing and advertising

In the automotive industry, digital marketing and precision advertising are becoming increasingly important. Many automakers use precise labeling systems to achieve precision marketing and advertising to better attract potential customers and increase sales.

Car companies have established a precise labeling system by collecting and analyzing user behavior data, car purchase intentions, interests and hobbies, etc. This labeling system includes labels such as user's geographic location, age, gender, income level, family status, and car-related labels such as brand preference, model preference, and car purchase budget.

Through this labeling system, automakers can more accurately identify potential customers and deliver relevant advertising and marketing campaigns to them.

For example, when a user shows interest in luxury cars and intends to buy one, car companies can use a precise tagging system to deliver relevant advertisements to him (friend circle ads) and provide more attractive car purchase plans and discounts.

4. Final Thoughts

The traffic dividend is disappearing, and refined operations are the way to go.

The label system plays an important role in the business and is an indispensable underlying infrastructure for refined operations. However, sometimes it may become large and vague and fail to solve the actual business pain points.

However, we can solve these problems by optimizing and rebuilding the label system. By referring to successful application practices, we can achieve business growth and improve user satisfaction. At the same time, we cannot ignore the continuous monitoring and evaluation of the effectiveness of the label system. Labels also need to be iterated with business priorities.

The above is the content of "Optimizing the label system and solving business pain points" .

Author: Add fish and meat

WeChat public account: Notes on adding fish and meat