How to accurately filter Facebook users? Use phone number filtering to find truly active accounts.

What compliance dimensions should be considered for precise user filtering on Facebook?

• Is the screening based on publicly available account information, without infringing on privacy information?
• Does the number filtering comply with platform rules and not involve illegal data collection?
• Does the screening process avoid malicious marketing tags and not compromise account security?

• Violating platform rules, resulting in the restriction or banning of the operating account.
• Crossing privacy red lines poses compliance risks and user complaints.
• Ineffective data filtering makes subsequent operational actions difficult to implement.
• Damaging one's own reputation and losing user trust
  
  

What accuracy issues can number filtering solve?

• How to remove empty or inactive numbers and retain real, bound accounts?
• How can I verify the validity of the link between my phone number and my Facebook account?
• How can I filter out maliciously registered accounts by phone number?

• Pushing content to invalid numbers results in an extremely low message delivery rate.
• Mistakenly identifying malicious accounts as target users leads to account risks.
• Distorted operational data makes it impossible to determine the true effectiveness.
• Wasted operational resources and persistently low conversion rates
  
  

How can we differentiate account activity levels through filtering?

• Has the account recently updated its activity, liked, or commented on anything?
• How long ago was the last login record after the phone number was linked to the account?
• Does the account have stable contacts and is not inactive?

• Push notifications were sent to long-inactive accounts, but there was no response whatsoever.
• Including zombie accounts in our operations is a waste of time and energy.
• Dismal interaction data prevents optimization of operational strategies.
• High-quality content mismatched with the right audience fails to realize its value.
  
  

How to use ITG global filtering to achieve precise filtering on Facebook?

• When importing numbers in batches, the tool only verifies publicly available binding relationships and does not involve privacy.
• Automatically removes empty numbers, dead numbers, and unlinked accounts, retaining only real and valid numbers.
• Label accounts by their activity level to differentiate between high, medium, and low-activity user groups.
• Export the results by category based on the filtering criteria, and directly connect them to subsequent operational actions.

How can the screening results be validated a second time to improve accuracy?

• Have the screening results been sampled and verified to confirm that the percentage of valid accounts meets the standard?
• Have you compared the account's public activity with the selected tags to rule out mislabeling?
• Are the filtered data updated regularly to respond to changes in account status?

• The screening results contained errors, and invalid accounts were treated as high-quality users.
• Incorrectly labeling activity levels caused subsequent push notification strategies to deviate from actual needs.
• Data not updated in a timely manner; outdated screening results used to guide operational actions.
• The combination of these two problems lowered the overall operational accuracy and conversion rate.
  
  

How to effectively segment and manage users after screening?

• Highly active users: Push core activities and conversion content to enhance interaction.
• Active users: Engage them with perks and boost activity.
• Low-activity users: Push neutral content, gradually activate or remove them.

• A unified push notification model cannot meet the needs of different users.
• Highly active users may actively block others if they are excessively disturbed.
• Low-activity users are not targeted for reactivation, leading to gradual churn.
• The operational strategy is vague, making it difficult to achieve precise optimization.

Conclusion