Signals use our predictive anthropology algorithms to establish and examine the broader context of meaning that consumers associate with a given topic of focus.
Once a larger contextual boundary, the Signals algorithm discovers early signs of cultural change at the peripheries of culture. It does this by:
- Examining all the meanings within that contextual boundary to surface deeply hidden connections
- Identifying which emergent patterns of meaning are growing in influence or experiencing significant change
- Filtering out noise and short-lived fads, ensuring only meaningful shifts are surfaced
Much like analyzing a weather system and identifying specific points of change, Signals detects cultural shifts before they become mainstream trends.
How are signals detected and measured?
To identify meaningful signals of change, our system:
- Scans millions of consumer conversations across forums, blogs, and digital communities.
- Tracks the introduction of new topics into relevant conversations.
- Measures the consistency and growth of these emerging meanings over time.
- Compares each signal’s movement to historical trend development patterns to determine if it has the potential for long-term cultural impact.
Key Metrics Measured in Signals:
- Velocity: How quickly a topic is becoming relevant.
- Resonance: How deeply the topic connects with broader consumer discussions.
- Sustainability: Whether the topic is consistently growing or likely to fade.