Construction of high-value user screening model, identifying high-quality customers in 5 major dimensions

High-value user screening is starting from"Experiential judgment" has become "model calculation". More and more teams no longer rely solely on sales feeling or operational intuition, but try to use unified standards to define what kind of users are worthy of focusing on investing resources. Especially as traffic costs continue to rise, the cost of spending budget on low-value users is getting higher and higher.

The core goal of the high-value user screening model is only one: using a set of repeatable rules to divide users into different levels and clarify which users are truly worthy of operational resources.

Why it is necessary to build a screening model

In the early stages of operation, many teams have a small number of users and can maintain results by relying on manual judgment. But when the user scale expands, an obvious problem will arise: resource allocation begins to get out of control.

Some users are repeatedly contacted but do not convert, and some users are ignored but originally have high potential. This imbalance is essentially caused by the lack of unified screening standards.

The significance of the screening model is to solve this problem so that every user can be put into the same evaluation system.

Dimension one User activity determines basic value

Activity is the most basic layer in the screening model. It reflects whether the user is still using the platform or maintaining interactive behavior.

A user who has been inactive for a long time, even if other conditions are good, will be difficult to convert in the short term. Therefore activity is usually used as the first filter condition.

In the actual model, comprehensive judgment can be made through indicators such as login frequency, interactive behavior, and access cycle.

Dimension two Spending power determines the upper limit of conversion

Spending power determines the user’sThe key variable of "how much can you buy".

There are obvious differences in the payment ability of different users, and this difference is often more important than the difference in interests.

Users with high spending power are not only easier to convert, but also have higher unit prices and more stable responses to promotional activities.

Therefore, in screening models, spending power is usually used as the second core dimension.

Dimension three Behavioral frequency reflects true intent

Behavior frequency refers to the density of user operations within a period of time, such as browsing, clicking, collecting, or consulting.

The more frequent the behavior, the stronger the user's interest in the product or service, and the higher the possibility of entering the decision-making stage.

Compared with static labels, behavior frequency can better reflect"Current state", not "historical properties".

Dimension four Retention cycle reflects user stability

Retention period refers to the length of time a user remains active.

Some users are only active for a short period of time and then quickly churn; some users are able to maintain stable interaction for a long time.

The commercial value of the latter is obviously higher, because they can not only bring about a conversion, but also may bring about long-term repurchase or continuous contribution.

The longer the retention period, the higher the user stability and the greater the weight in the model.

Dimension five Equipment and sources affect quality judgment

The user’s device type and source channel will also affect the overall quality judgment.

For example, there are differences in the consumption behavior of users of different devices, and the quality of users from different sources is also different.

Although these factors are not core variables that directly determine conversion, they can be used as auxiliary dimensions to optimize the final scoring results.

The essence of the model is"User Hierarchy"

The core of the high-value user screening model is not complex algorithms, but clear layered logic.

It can usually be divided into a three-tier structure: high-value users, medium-value users and low-value users.

High-value users focus on investing resources, medium-value users focus on cultivating conversion potential, and low-value users reduce the frequency of contact or only perform basic maintenance.

This layering can significantly improve overall operational efficiency.

Why models are more stable than experience

The problem with empirical judgment is that it cannot be replicated, and different operators have inconsistent standards, which can easily lead to confusion in user allocation.

The advantage of the model is stability and consistency, and the results can be repeated as long as the rules remain unchanged.

This means that as the team expands, there will be no problem of out-of-control judgment standards.

Data must be cleaned before entering the model

The prerequisite for any screening model is data quality.

If the data itself contains a large number of invalid numbers, downtime users, or low-quality accounts, the model results will be seriously disturbed.

Therefore, before entering the model, it is usually necessary to perform basic data cleaning and status identification.

At this stage, Digital Planet can be used to perform data preprocessing before screening high-value users, including number status detection, activity identification, and basic tag sorting, so that the data entering the model is cleaner and more unified.

The role of digital planet in screening models

Digital Planet is not a replacement for models, but rather provides cleaner data inputs to models.

In actual use, it can help the team accomplish three things: first, basic data screening to eliminate invalid numbers; second, behavioral label supplementation to improve the integrity of user portraits; third, pre-layering preprocessing to make model calculation results more stable.

Only when these basic tasks are completed can the high-value user screening model really come into play.

from"Select users by feeling" to "Select users by system"

The biggest change in the screening model is to change user choice from subjective judgment to structured calculation.

In the past, we relied on experience, now we rely on rules; in the past, we relied on feelings, now we rely on data.

When the model runs stably, the team will gradually"Finding good users" has been transformed into "continuously screening good users", and the overall operational efficiency will also be improved.

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