AI glossary · Business
Lead scoring
In one sentence
Lead scoring is a way of giving each sales contact a score based on how close they are to buying, combining who they are (sector, size, location) with what they've done (questions, visits, replies), so the sales team deals with the most promising first.
Also known as: lead score · lead rating · prospect scoring · predictive lead scoring
What is lead scoring?
A salesperson can’t call every contact at once. Lead scoring sorts the queue: each lead gets a score and you work from the top down. The score blends two kinds of signal:
- Profile (fit). Does the contact look like your ideal customer? Sector, size, location, job title.
- Behaviour (interest). What they’ve done: asked for a price, asked about delivery times, opened three emails, got back in touch.
Put it to work
An AI agent on your WhatsApp, CRM included
From €49/mo + VAT
It replies to customers on WhatsApp and logs every conversation in the CRM.
See the WhatsApp agentA lead with a good profile and plenty of interest is a “hot lead”. A good profile with little interest gets nurtured more slowly.
Related to this term
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See the WhatsApp agentHow it works
- Set the criteria. With your team, write down what sets buying customers apart. For example: “asks for a quote” +20, “company with more than 10 staff” +15, “only asks opening hours” +0.
- Collect the data. From forms, from the CRM and, increasingly, from the conversations themselves.
- Calculate the score. Add up points by rule, or use a statistical model trained on your past sales.
- Act on thresholds. Above a certain score, the lead goes to a salesperson; below it, it gets automated follow-up.
- Review. If high-scoring leads don’t buy, the criteria are wrong and need adjusting.
AI fits mainly into step 2. A model reads a free-form message like “we run three clinics and want to switch supplier before January” and extracts size, need and timing. Techniques such as text classification or sentiment analysis add further signals.
Example
A building and renovation firm in Brighton gets dozens of WhatsApp enquiries each week. Many just say “how much to paint a flat?”. A few say “we’ve got a 200 m² shop unit and want to open in March”. With a simple lead score, floor area, timing and type of job earn points. The agent asks those three questions during the chat, and each morning the sales lead opens a ranked list instead of reading everything.
Lead scoring at UTIO
UTIO’s WhatsApp agent works with the CRM built in: every conversation becomes a record, enters the pipeline and is assigned to a rep. The agent asks the qualifying questions you would ask, saves the answers to the record and passes hot leads to your salesperson with the context. With that structured data you can apply your own scoring criteria.
If you already use HubSpot, Zoho, Pipedrive or Holded, UTIO’s CRM mirrors them, so your lead scoring rules can stay where they are. The voice agent does the same qualification over the phone.
Frequently asked questions
What is the difference between lead scoring and lead qualification?
Qualification decides whether a contact fits at all, often through specific questions about budget, need and timing. Lead scoring puts a number on everyone who fits so you can rank them. They work together: qualification supplies the data you score with.
Do I need a lot of data to start?
No. Start with four or five criteria your team already applies in their heads, give them points, then check after a few weeks whether the top-scoring leads are the ones that actually buy.
What does AI add to lead scoring?
Two things: it pulls data out of free-form conversations, such as what a customer says on WhatsApp, and it can spot patterns in your sales history that would be hard to see by hand. Deciding which criteria count is still up to you.
Related terms
- Sentiment analysis
Sentiment analysis is the AI technique that detects the emotional tone of a text (positive, negative or neutral, and sometimes emotions such as anger or urgency) so you know how the writer feels without reading everything by hand.
- Text classification
Text classification is the AI task of automatically assigning one or more categories to a piece of text, such as an email or a message: type of request, topic, priority or language. It's the basis for sorting, prioritising and routing work.
- AI automation
AI automation means using artificial intelligence models to handle tasks that used to need human judgement, such as reading a message, working out what it asks for and choosing the next step, rather than just repeating fixed steps defined in advance.
- Intent
An intent is the goal behind what a person writes or says to a chatbot or AI agent, such as booking, cancelling or asking for a price. Recognising it lets the system decide what to do with the message.
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