FAQ

Machine Learning is a subset of AI that focuses on developing algorithms and models that allow computers to learn from data and improve their performance over time. It plays a crucial role in enabling AI systems to recognize patterns, make predictions, and adapt to new information.

There are generally two types of AI: Narrow or Weak AI, which is designed to perform specific tasks, and General or Strong AI, which possesses human-level intelligence and can handle a wide range of tasks.

AI has applications in various fields, including:

Natural Language Processing (NLP) for chatbots, language translation, and sentiment analysis.
Computer Vision for image recognition, object detection, and autonomous vehicles.
Machine Learning for predictive analytics, data mining, and pattern recognition.
Robotics for automation in industries such as manufacturing and healthcare.
AI-powered personal assistants, recommendation systems, and fraud detection, among others.

How Do You Determine Intent ?

When we scan 61 billion behaviors per week, we’re monitoring the specific digital actions people take that reveal where they are in their buying journey. These signals aren’t guesses—they’re patterns created by real human behavior online.

What We Track

We track the exact actions buyers take across the internet, including:

Keyword searches related to your product, category, or problem
Website visits to pages in your niche or competing solutions
Video views about your industry, your offer type, or related topics
Repeat visits to the same content, showing rising interest
Link shares of articles, product pages, or relevant resources
Social media activity, including likes, comments, and posts about the topic
Forum discussions and Q&A engagement related to your category
Content consumption patterns that match known buyer journeys
Review reading, competitor comparison, and product exploration
Sudden spikes in topic interest which signal new market demand

The Things We Know

Buyers reveal what they care about through their actions:

If they search repeatedly, they’re researching and comparing.
When they watch multiple videos on the same topic, they’re educating themselves.
If they return to a page, they’re evaluating options.
When they comment or post about an issue, the problem is top-of-mind.
If they share links, they’re seeking opinions or showing interest.
Every one of these behaviors teaches us something about where they are in the buying cycle.

How We Use the Data

By analyzing billions of signals, we can:

Build audiences of people who are currently in-market
Sort buyers into low, medium, and high intent
Detect when someone is moving closer to a purchase
Deliver smarter, more relevant targeting
Identify prospects before competitors even know they exist

These intent levels come from the intensity, frequency, and recency of their actions.
More activity = higher intent.
Broader research = lower intent.
Focused, repeated actions = high intent.