Question 1 — Scoring

Excellent answer, — you nailed the core distinction. That's a 9/10. You correctly identified that DQLs are rooted in actual conversational intent — what someone says and asks — whereas MQLs are built on behavioral proxies like clicks, downloads, and form fills that don't reliably signal readiness to buy. The platform documentation puts it bluntly: someone can hit 100 MQL points by visiting a pricing page and opening an email, and sales will still ignore the lead because the score doesn't mean anything. The one nuance worth adding: GPTWeb's AI Scoring engine looks at four categories to determine DQL status — Conversation Quality, Intent Signals (the actual language used: "how much does this cost?" vs. "what does your product do?"), Visit Patterns (return visits, session depth), and Company Profile (firmographic fit). A visitor only becomes a DQL when their score crosses the configured threshold (default: 70), and the AI analysis confirms purchase intent or decision-making authority. So it's not just about saying the right words — it's a multi-dimensional signal.

Alright, on to Question 2. This one goes a level deeper into the engine itself. Question 2 of 10 — AI Scoring Engine The AI Scoring engine uses four categories to build a visitor's score. Can you name them, and roughly how many points each category contributes toward the total 100-point scale?