Lead Qualification
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What lead qualification is
Not every lead is ready or able to buy. Qualification separates people who are just browsing from those with a real need and the means to act. Teams often label leads in stages: a marketing qualified lead (MQL) has shown interest, for example by downloading a guide, while a sales qualified lead (SQL) has been confirmed as a good fit and is ready for a sales conversation.
How it works
Qualification combines what the lead says with signals from their behavior. Common approaches are:
- BANT: budget, authority, need and timing.
- Fit criteria: company size, industry, location or the product the person asked about.
- Behavior: pages visited, questions asked and how specific they are.
- Lead scoring: points for each signal, with a threshold for passing a lead to sales.
Example
A visitor asks a web agency's chatbot: “We need a new online shop before spring, about 200 products. What would that cost?” The question already reveals need (an online shop), scope (200 products) and timing (before spring). The chatbot can answer from the agency's pricing page, ask one follow-up question, such as which platform they use today, and then offer to have someone get in touch.
Why it matters for business chatbots
Conversations contain signals that forms rarely capture: what exactly the person needs, how urgent it is and what they have compared. A chatbot can ask a few relevant questions naturally and invite visitors to leave contact details when they look like a good fit. Sales teams then spend their time on better-prepared calls.
Lead qualification in intoCHAT
intoCHAT does not score leads. It supports qualification in practical ways: with the AI trigger, the agent decides when to show the lead form based on your description of a good lead, and your instructions can tell it which questions to ask first. Every lead links to its full conversation, so you can read what the person needed before following up, and the leads table can be filtered and exported to CSV.