WhatsApp high net worth user screening

Batch identify whether WhatsApp accounts belong to regular consumers and provide judgment results for potential middle-class or high-value customers. Help the cross-border marketing team select users who are more worthy of priority operations from large batches of numbers, and optimize customer stratification and marketing resource allocation.

WhatsApp
业务痛点 Business Pain Points

The number of cross-border marketing lists is huge, but there are obvious differences in the consumption tendencies and potential values ​​of different customers. If all numbers use the same promotion strategy, it is easy to devote a lot of budget and sales time to low-intent users.


  • The customer list only has numbers and lacks consumption tendency and user value labels.

  • Unable to quickly identify people who consume frequently or have high willingness to consume

  • It is difficult to screen potential target users from ordinary customers for high-priced products

  • All users use the same marketing content, which affects promotion relevance

  • Sales team fails to prioritize follow-up based on lead value

解决方案 Solutions

WhatsApp middle-class and high-net-worth user detection determines whether the account belongs to the regular consumer group based on the product model, and outputs potential high-value user labels to provide reference for customer stratification, marketing budget allocation and differentiated reach.


  • Identification of regular consumer groups:Determine whether the account has the characteristics of regular consumers

  • Potential value judgment:Provide reference tags for potential middle-class or high-value users

  • Customer value tiering:Organize customer lists with different priorities based on test results

  • Marketing resource optimization:Invest more promotion and sales resources into potential high-value groups

  • Result classification export:Export numbers and user value judgment results to facilitate subsequent operations

Core Features

Automatic batch screening in the cloud
After the number file is uploaded, the detection is automatically performed in the cloud. There is no need to prepare local equipment or complex configurations, which improves the data processing efficiency of WhatsApp batch screening numbers.
Multi-dimensional account information detection
Detect the account opening status based on the selected product, and obtain results such as last online time, gender, age, and banned status to meet different customer screening needs.
Global number intelligent identification
Supports international mobile phone numbers in different countries and regions, automatically identifies country area codes and standardizes number formats, making it easy to centrally process customer data in multiple overseas markets.
Classification and export of test results
Supports structured export of test results to facilitate continued screening and analysis, or imported into CRM, customer service and marketing systems for subsequent customer follow-up.
Quick screening of target customers
Classify and organize the numbers according to the actual test results to help the team screen potential customers who are reachable, recently active, or meet the target profile.
Batch detection of millions of numbers
It supports uploading files containing up to 1 million numbers, and the system automatically completes WhatsApp batch detection to meet the needs of large-scale overseas customer expansion and customer data cleaning.

Our Clients

Cross-border E-commerce Sellers

Accurately filter overseas buyer numbers to increase WhatsApp reach rate

Private Domain Operation Teams

Filter invalid or banned numbers to focus resources on cultivating high-quality customers.

Outbound Call & Customer Service Teams

Verify number validity in advance to prevent cost waste from calling invalid numbers.

Social Media Operation & Advertising Teams

Target ads based on active users to increase conversion rate.

DIGITAL PLANET

Digital Planet — Fully Automated Screening for Easier Precision Marketing

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