Overview
Telephone calls being labeled as "spam likely" by carriers and analytics engines are impacted by several factors, which can be classified into three categories: Behavioral, Technical, and Analytical. Below, we'll provide a high-level overview of the factors that contribute to the "spam likely" being applied to calls.
Behavioral Factors
These are based on how recipients interact with calls.
Consumer Behavior
- Frequent call declines
- Not answering
- Blocking the number
- Reporting as spam
Technical Factors
These relate to the infrastructure, configuration, and protocols used in call delivery.
Call Volume and Velocity
- High call frequency from a single number
- Predictive or auto-dialing systems
Inconsistent Caller ID Usage
- Reusing numbers across campaigns
- Frequent changes in the caller ID (spoofing)
Lack of STIR/SHAKEN Compliance
- Missing or low attestation levels (B or C)
- No digital signature verifying the caller ID
Caller ID Branding
- Absence of branded caller ID or Rich Call Data (RCD)
- Branded Calling
Analytical Factors
These are determined by carrier and third-party analytics engines, which use algorithms and historical data.
Caller ID Reputation
- Historical spam complaints
- Low trust score from analytics providers
Call Content and Patterns
- Matching known scam or robocall templates
- Behavioral analysis of call scripts or timing
Carrier and Analytics Provider Policies
- Proprietary algorithms managed by service providers
- Varying thresholds for spam labeling
Best Practices to Avoid "Spam Likely" Labeling
- Use Attestation A whenever possible.
- Register numbers with analytics providers and caller ID registries.
- Avoid excessive call volume from a single number.
- Use a branded caller ID to show your company name/logo.
- Monitor and rotate numbers responsibly.
- Ensure opt-in compliance and maintain clean calling lists.
Support Resources
For questions about "spam likely" labeling, contact Gryphon AI Support
at
support@gryphon.zendesk.com